Added InputBlockV2 support to DeepFMModel (refactored and fixed) and DCNModel by gabrielspmoreira · Pull Request #717 · NVIDIA-Merlin/models

gabrielspmoreira · 2022-09-06T00:29:42Z

Fixes #604

Goals ⚽

Sets InputBlockV2 as the default input block for DCNModel and DeepFMModel
Creates ToSparseFeatures and ToDenseFeatures to convert tensor types for selected or all features
Refactors DeepFMModel to extract the FMBlock and fixes the implemenation to match the one described in the original papers: (Fixes [BUG] DeepFM implementation is incorrect and misses tests #742 )

[1] Huifeng, Guo, et al. "DeepFM: A Factorization-Machine based Neural Network for CTR Prediction". arXiv:1703.04247  (2017).
[2] Steffen, Rendle, "Factorization Machines" IEEE International Conference on Data Mining, 2010.

Implementation Details 🚧

Previously the InputBlock was used for DCNModel and DeepFMModel
For DCNModel, the user were allowed to set embedding_options. Now the user can provide its own InputBlock, or provide custom args for the Embeddings() via InputBlockV2(..., **kwargs)
The previous implementation of DeepFMModel had the following issues that are fixed here:
- In the wide part the one-hot categorical features was not sparse, which might cause OOM for high cardinality features.
- The DeepFMModel supported only categorical features. If continuous features were passed the branch with FMPairwiseInteraction an error were raised
- The FM and deep tower output should be 1d and summed. But instead those towers were outputing multiple dimensions (>1), which were projected to a single dim by an MLP layer, which is not part of the original architectures described in the papers.

Testing Details 🔍

There was no test for DeepFMModel, so added some and fixed bugs

nvidia-merlin-bot · 2022-09-06T00:45:54Z

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GitHub pull request #717 of commit 5e8b254f7cc54689f3eeb5cc9605d728829bbb90, no merge conflicts.
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Commit message: "Added support to InputBlockV2 to DCNModel and DeepFMModel"
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[merlin_models] $ /bin/bash /tmp/jenkins2149850330022829085.sh
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 683 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

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tests/unit/lightfm/test_lightfm.py .                                     [  4%]

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tests/unit/tf/test_dataset.py ................                           [  7%]

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tests/unit/tf/blocks/test_cross.py ...........                           [  9%]

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tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

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tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=================================== FAILURES ===================================

____________________ test_train_metrics_steps[60-10-3-6-2] _____________________
num_rows = 60, batch_size = 10, train_metrics_steps = 3, expected_steps = 6

expected_metrics_steps = 2
@pytest.mark.parametrize(
    ["num_rows", "batch_size", "train_metrics_steps", "expected_steps", "expected_metrics_steps"],
    [
        (1, 1, 1, 1, 1),
        (60, 10, 2, 6, 3),
        (60, 10, 3, 6, 2),
        (120, 10, 4, 12, 3),
    ],
)
def test_train_metrics_steps(
    num_rows, batch_size, train_metrics_steps, expected_steps, expected_metrics_steps
):
    dataset = generate_data("e-commerce", num_rows=num_rows)
    model = ml.Model(
        ml.InputBlock(dataset.schema),
        ml.MLPBlock([64]),
        ml.BinaryClassificationTask("click"),
    )
    model.compile(
        run_eagerly=True,
        optimizer="adam",
        metrics=[tf.keras.metrics.AUC(from_logits=True, name="auc")],
    )
    metrics_callback = MetricsLogger()
    callbacks = [metrics_callback]
    _ = model.fit(
        dataset,
        callbacks=callbacks,
        epochs=1,
        batch_size=batch_size,
        train_metrics_steps=train_metrics_steps,
    )
    epoch0_logs = metrics_callback.epoch_logs[0]

    # number of times compute_metrics called (number of batches in epoch)
    assert len(epoch0_logs) == expected_steps

    # number of times metrics computed (every train_metrics_steps batches)


  assert len({metrics["auc"] for metrics in epoch0_logs}) == expected_metrics_steps


E       assert 1 == 2

E        +  where 1 = len({0.4166666865348816})
tests/unit/tf/models/test_base.py:139: AssertionError

----------------------------- Captured stdout call -----------------------------
1/6 [====>.........................] - ETA: 1s - loss: 0.7019 - auc: 0.4167 - regularization_loss: 0.0000e+00�������������������������������������������������������������������������������������������������������������

2/6 [=========>....................] - ETA: 0s - loss: 0.7014 - auc: 0.4167 - regularization_loss: 0.0000e+00�������������������������������������������������������������������������������������������������������������

3/6 [==============>...............] - ETA: 0s - loss: 0.7013 - auc: 0.4167 - regularization_loss: 0.0000e+00�������������������������������������������������������������������������������������������������������������

4/6 [===================>..........] - ETA: 0s - loss: 0.6997 - auc: 0.4167 - regularization_loss: 0.0000e+00�������������������������������������������������������������������������������������������������������������

5/6 [========================>.....] - ETA: 0s - loss: 0.7022 - auc: 0.4167 - regularization_loss: 0.0000e+00�������������������������������������������������������������������������������������������������������������

6/6 [==============================] - ETA: 0s - loss: 0.6984 - auc: 0.4167 - regularization_loss: 0.0000e+00�������������������������������������������������������������������������������������������������������������

6/6 [==============================] - 1s 130ms/step - loss: 0.6984 - auc: 0.4167 - regularization_loss: 0.0000e+00

___________________________ test_deepfm_model[True] ____________________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f2e22806580>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:171:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:725: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/models/base.py:923: in call

outputs, context = self._call_child(block, outputs, context)

merlin/models/tf/models/base.py:952: in _call_child

outputs = call_layer(child, inputs, **call_kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:497: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:289: in call

outputs = call_layer(layer, outputs, training=training, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:497: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/transformations.py:569: in call

outputs[name] = utils.encode_categorical_inputs(

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:124: in encode_categorical_inputs

bincounts = dense_bincount(inputs, depth, binary_output, dtype,

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:68: in dense_bincount

result = tf.math.bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bincount_ops.py:211: in bincount

return gen_math_ops.dense_bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py:3020: in dense_bincount

_ops.raise_from_not_ok_status(e, name)

e = _NotOkStatusException(), name = None
def raise_from_not_ok_status(e, name):
  e.message += (" name: " + name if name is not None else "")


raise core._status_to_exception(e) from None  # pylint: disable=protected-access


E     tensorflow.python.framework.errors_impl.UnimplementedError: Exception encountered when calling layer "category_encoding" (type CategoryEncoding).

E

E     Determinism is not yet supported in GPU implementation of DenseBincount. [Op:DenseBincount]

E

E     Call arguments received by layer "category_encoding" (type CategoryEncoding):

E       • inputs={'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}

E       • kwargs={'training': 'False', 'features': {'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}, 'testing': 'False'}
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py:7164: UnimplementedError

___________________________ test_deepfm_model[False] ___________________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f2e294ddc70>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:171:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:725: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/models/base.py:923: in call

outputs, context = self._call_child(block, outputs, context)

merlin/models/tf/models/base.py:952: in _call_child

outputs = call_layer(child, inputs, **call_kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:497: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:289: in call

outputs = call_layer(layer, outputs, training=training, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:497: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/transformations.py:569: in call

outputs[name] = utils.encode_categorical_inputs(

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:124: in encode_categorical_inputs

bincounts = dense_bincount(inputs, depth, binary_output, dtype,

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:68: in dense_bincount

result = tf.math.bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bincount_ops.py:211: in bincount

return gen_math_ops.dense_bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py:3020: in dense_bincount

_ops.raise_from_not_ok_status(e, name)

e = _NotOkStatusException(), name = None
def raise_from_not_ok_status(e, name):
  e.message += (" name: " + name if name is not None else "")


raise core._status_to_exception(e) from None  # pylint: disable=protected-access


E     tensorflow.python.framework.errors_impl.UnimplementedError: Exception encountered when calling layer "category_encoding" (type CategoryEncoding).

E

E     Determinism is not yet supported in GPU implementation of DenseBincount. [Op:DenseBincount]

E

E     Call arguments received by layer "category_encoding" (type CategoryEncoding):

E       • inputs={'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}

E       • kwargs={'training': 'False', 'features': {'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}, 'testing': 'False'}
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py:7164: UnimplementedError

=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 34 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 18 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_fileq9g8moqx.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:980: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

===== 3 failed, 669 passed, 11 skipped, 1017 warnings in 947.00s (0:15:47) =====

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins17529944726332883267.sh

gabrielspmoreira · 2022-09-06T01:58:41Z

merlin/models/tf/models/ranking.py

+
+    # TODO: Identify why we need to set the schema manually for `InputBlockV2`
+    # to avoid an error of ParallelBlock without schema?
+    input_block.set_schema(schema)


@marcromeyn I had to include this hack because the InputBlockV2 returns a ParallelBlock without schema in this case.
I suspect that is the case because the data for this test contains only categorical features and the Filter() for continuous features gets empty, so that not all branches within ParallelBlock contains a schema.
Is this related to your recent refactory that makes ParallelBlock schema-aware?

This hack is not needed anymore, as @marcromeyn implemented a fix for that on InputBlockV2 in this PR

nvidia-merlin-bot · 2022-09-06T02:02:43Z

Click to view CI Results

GitHub pull request #717 of commit e4c777ceda6d7b063c6f7df40fb181c47dfc9002, no merge conflicts.
Running as SYSTEM
Setting status of e4c777ceda6d7b063c6f7df40fb181c47dfc9002 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1144/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse e4c777ceda6d7b063c6f7df40fb181c47dfc9002^{commit} # timeout=10
Checking out Revision e4c777ceda6d7b063c6f7df40fb181c47dfc9002 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f e4c777ceda6d7b063c6f7df40fb181c47dfc9002 # timeout=10
Commit message: "Fixed error on Jenkins GPU CI tests (an error occurs when CategoryEncoding is called with sparse=False for CI machine)"
 > git rev-list --no-walk 1d48d951a4fb336739b6ffde04ed295dd8928290 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins10010199132761638169.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 683 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  9%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py .                              [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 27%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 31%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/core/test_transformations.py s............................ [ 35%]

..................                                                       [ 38%]

tests/unit/tf/data_augmentation/test_misc.py .                           [ 38%]

tests/unit/tf/data_augmentation/test_negative_sampling.py ..........     [ 40%]

tests/unit/tf/data_augmentation/test_noise.py .....                      [ 40%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 40%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 41%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 41%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 41%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 41%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 41%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 41%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 42%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 42%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 42%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]

.....                                                                    [ 48%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 51%]

tests/unit/tf/layers/test_queue.py ..............                        [ 53%]

tests/unit/tf/losses/test_losses.py .......................              [ 56%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 57%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 60%]

tests/unit/tf/models/test_base.py s................                      [ 63%]

tests/unit/tf/models/test_benchmark.py ..                                [ 63%]

tests/unit/tf/models/test_ranking.py ................................    [ 68%]

tests/unit/tf/models/test_retrieval.py ................................  [ 72%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 73%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 75%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 76%]

tests/unit/tf/prediction_tasks/test_regression.py ..                     [ 76%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 76%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 77%]

tests/unit/tf/predictions/test_base.py .....                             [ 78%]

tests/unit/tf/predictions/test_classification.py .......                 [ 79%]

tests/unit/tf/predictions/test_dot_product.py ........                   [ 80%]

tests/unit/tf/predictions/test_regression.py ..                          [ 80%]

tests/unit/tf/predictions/test_sampling.py ....                          [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 81%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 83%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 84%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 90%]

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tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 34 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 18 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_fileubbsdxd3.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/sequential_block_4/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/sequential_block_4/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/sequential_block_4/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:980: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 672 passed, 11 skipped, 1018 warnings in 950.72s (0:15:50) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins12423255128051031398.sh

nvidia-merlin-bot · 2022-09-08T20:47:39Z

Click to view CI Results

GitHub pull request #717 of commit 53d1ec95c7f4ac56c18b74d872e3818a8bea443a, no merge conflicts.
Running as SYSTEM
Setting status of 53d1ec95c7f4ac56c18b74d872e3818a8bea443a to PENDING with url https://10.20.13.93:8080/job/merlin_models/1173/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse 53d1ec95c7f4ac56c18b74d872e3818a8bea443a^{commit} # timeout=10
Checking out Revision 53d1ec95c7f4ac56c18b74d872e3818a8bea443a (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 53d1ec95c7f4ac56c18b74d872e3818a8bea443a # timeout=10
Commit message: "Removing the hack that set the schema of input block for DeepFMModel"
 > git rev-list --no-walk fbd2083e16db79c010aa95ff7a372c102773a00c # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins2898401193199117777.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 686 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  9%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py .                              [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 26%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 31%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/core/test_transformations.py s............................ [ 35%]

..................                                                       [ 38%]

tests/unit/tf/data_augmentation/test_misc.py .                           [ 38%]

tests/unit/tf/data_augmentation/test_negative_sampling.py ..........     [ 40%]

tests/unit/tf/data_augmentation/test_noise.py .....                      [ 40%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 40%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 41%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 41%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 41%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 41%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 41%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 41%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 41%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 42%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 42%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]

.....                                                                    [ 48%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 51%]

tests/unit/tf/layers/test_queue.py ..............                        [ 53%]

tests/unit/tf/losses/test_losses.py .......................              [ 56%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 57%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 60%]

tests/unit/tf/models/test_base.py s................                      [ 62%]

tests/unit/tf/models/test_benchmark.py ..                                [ 63%]

tests/unit/tf/models/test_ranking.py ..............FFFF................  [ 68%]

tests/unit/tf/models/test_retrieval.py ................................  [ 72%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 73%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 75%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 76%]

tests/unit/tf/prediction_tasks/test_regression.py ..                     [ 76%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 76%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 77%]

tests/unit/tf/predictions/test_base.py .....                             [ 78%]

tests/unit/tf/predictions/test_classification.py .......                 [ 79%]

tests/unit/tf/predictions/test_dot_product.py ........                   [ 80%]

tests/unit/tf/predictions/test_regression.py ..                          [ 80%]

tests/unit/tf/predictions/test_sampling.py ....                          [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 81%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 83%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 84%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 90%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=================================== FAILURES ===================================

___________________ test_deepfm_model_only_categ_feats[True] ___________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f692b0f3460>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_only_categ_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:171:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:725: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/models/base.py:923: in call

outputs, context = self._call_child(block, outputs, context)

merlin/models/tf/models/base.py:952: in _call_child

outputs = call_layer(child, inputs, **call_kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:492: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:269: in call

return call_sequentially(self.layers, inputs, training=training, **kwargs)

merlin/models/tf/core/combinators.py:752: in call_sequentially

outputs = call_layer(layer, outputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:492: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/transformations.py:569: in call

outputs[name] = utils.encode_categorical_inputs(

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:124: in encode_categorical_inputs

bincounts = dense_bincount(inputs, depth, binary_output, dtype,

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:68: in dense_bincount

result = tf.math.bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bincount_ops.py:211: in bincount

return gen_math_ops.dense_bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py:3020: in dense_bincount

_ops.raise_from_not_ok_status(e, name)

e = _NotOkStatusException(), name = None
def raise_from_not_ok_status(e, name):
  e.message += (" name: " + name if name is not None else "")


raise core._status_to_exception(e) from None  # pylint: disable=protected-access


E     tensorflow.python.framework.errors_impl.UnimplementedError: Exception encountered when calling layer "category_encoding" (type CategoryEncoding).

E

E     Determinism is not yet supported in GPU implementation of DenseBincount. [Op:DenseBincount]

E

E     Call arguments received by layer "category_encoding" (type CategoryEncoding):

E       • inputs={'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}

E       • kwargs={'training': 'False', 'features': {'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}, 'testing': 'False'}
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py:7164: UnimplementedError

__________________ test_deepfm_model_only_categ_feats[False] ___________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f695031dbb0>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_only_categ_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:171:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:725: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/models/base.py:923: in call

outputs, context = self._call_child(block, outputs, context)

merlin/models/tf/models/base.py:952: in _call_child

outputs = call_layer(child, inputs, **call_kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:492: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:269: in call

return call_sequentially(self.layers, inputs, training=training, **kwargs)

merlin/models/tf/core/combinators.py:752: in call_sequentially

outputs = call_layer(layer, outputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:492: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/transformations.py:569: in call

outputs[name] = utils.encode_categorical_inputs(

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:124: in encode_categorical_inputs

bincounts = dense_bincount(inputs, depth, binary_output, dtype,

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:68: in dense_bincount

result = tf.math.bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bincount_ops.py:211: in bincount

return gen_math_ops.dense_bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py:3020: in dense_bincount

_ops.raise_from_not_ok_status(e, name)

e = _NotOkStatusException(), name = None
def raise_from_not_ok_status(e, name):
  e.message += (" name: " + name if name is not None else "")


raise core._status_to_exception(e) from None  # pylint: disable=protected-access


E     tensorflow.python.framework.errors_impl.UnimplementedError: Exception encountered when calling layer "category_encoding" (type CategoryEncoding).

E

E     Determinism is not yet supported in GPU implementation of DenseBincount. [Op:DenseBincount]

E

E     Call arguments received by layer "category_encoding" (type CategoryEncoding):

E       • inputs={'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}

E       • kwargs={'training': 'False', 'features': {'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}, 'testing': 'False'}
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py:7164: UnimplementedError

______________ test_deepfm_model_categ_and_continuous_feats[True] ______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f6932c631f0>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_categ_and_continuous_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "user_age", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:186:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:725: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/models/base.py:923: in call

outputs, context = self._call_child(block, outputs, context)

merlin/models/tf/models/base.py:952: in _call_child

outputs = call_layer(child, inputs, **call_kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:492: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:269: in call

return call_sequentially(self.layers, inputs, training=training, **kwargs)

merlin/models/tf/core/combinators.py:752: in call_sequentially

outputs = call_layer(layer, outputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:492: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/transformations.py:569: in call

outputs[name] = utils.encode_categorical_inputs(

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:124: in encode_categorical_inputs

bincounts = dense_bincount(inputs, depth, binary_output, dtype,

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:68: in dense_bincount

result = tf.math.bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bincount_ops.py:211: in bincount

return gen_math_ops.dense_bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py:3020: in dense_bincount

_ops.raise_from_not_ok_status(e, name)

e = _NotOkStatusException(), name = None
def raise_from_not_ok_status(e, name):
  e.message += (" name: " + name if name is not None else "")


raise core._status_to_exception(e) from None  # pylint: disable=protected-access


E     tensorflow.python.framework.errors_impl.UnimplementedError: Exception encountered when calling layer "category_encoding" (type CategoryEncoding).

E

E     Determinism is not yet supported in GPU implementation of DenseBincount. [Op:DenseBincount]

E

E     Call arguments received by layer "category_encoding" (type CategoryEncoding):

E       • inputs={'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}

E       • kwargs={'training': 'False', 'features': {'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_age': 'tf.Tensor(shape=(50, 1), dtype=int64)'}, 'testing': 'False'}
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py:7164: UnimplementedError

_____________ test_deepfm_model_categ_and_continuous_feats[False] ______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f6933989100>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_categ_and_continuous_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "user_age", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:186:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:725: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/models/base.py:923: in call

outputs, context = self._call_child(block, outputs, context)

merlin/models/tf/models/base.py:952: in _call_child

outputs = call_layer(child, inputs, **call_kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:492: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:269: in call

return call_sequentially(self.layers, inputs, training=training, **kwargs)

merlin/models/tf/core/combinators.py:752: in call_sequentially

outputs = call_layer(layer, outputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/combinators.py:492: in call

out = call_layer(layer, layer_inputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:403: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/core/tabular.py:483: in _tabular_call

outputs = self.super().call(inputs, *args, **kwargs)  # type: ignore

merlin/models/config/schema.py:58: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/transformations.py:569: in call

outputs[name] = utils.encode_categorical_inputs(

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:124: in encode_categorical_inputs

bincounts = dense_bincount(inputs, depth, binary_output, dtype,

/usr/local/lib/python3.8/dist-packages/keras/layers/preprocessing/preprocessing_utils.py:68: in dense_bincount

result = tf.math.bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bincount_ops.py:211: in bincount

return gen_math_ops.dense_bincount(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py:3020: in dense_bincount

_ops.raise_from_not_ok_status(e, name)

e = _NotOkStatusException(), name = None
def raise_from_not_ok_status(e, name):
  e.message += (" name: " + name if name is not None else "")


raise core._status_to_exception(e) from None  # pylint: disable=protected-access


E     tensorflow.python.framework.errors_impl.UnimplementedError: Exception encountered when calling layer "category_encoding" (type CategoryEncoding).

E

E     Determinism is not yet supported in GPU implementation of DenseBincount. [Op:DenseBincount]

E

E     Call arguments received by layer "category_encoding" (type CategoryEncoding):

E       • inputs={'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)'}

E       • kwargs={'training': 'False', 'features': {'item_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'item_category': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_id': 'tf.Tensor(shape=(50, 1), dtype=int64)', 'user_age': 'tf.Tensor(shape=(50, 1), dtype=int64)'}, 'testing': 'False'}
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py:7164: UnimplementedError

=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_file6e3x9b0z.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:980: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

===== 4 failed, 671 passed, 11 skipped, 1028 warnings in 966.00s (0:16:05) =====

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins8099385560801091154.sh

nvidia-merlin-bot · 2022-09-09T02:59:41Z

Click to view CI Results

GitHub pull request #717 of commit cb87acc168fd00b7bc2df168e5e3978fc0d3bf29, no merge conflicts.
Running as SYSTEM
Setting status of cb87acc168fd00b7bc2df168e5e3978fc0d3bf29 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1175/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse cb87acc168fd00b7bc2df168e5e3978fc0d3bf29^{commit} # timeout=10
Checking out Revision cb87acc168fd00b7bc2df168e5e3978fc0d3bf29 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f cb87acc168fd00b7bc2df168e5e3978fc0d3bf29 # timeout=10
Commit message: "Added ToSparseFeatures and ToDenseFeatures"
 > git rev-list --no-walk de8faf947cc7399b547067903439225244180b4d # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins3530415144233309613.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
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Requirement already satisfied: pkgutil-resolve-name>=1.3.10; python_version < "3.9" in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (1.3.10)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (0.18.1)
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Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 686 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  9%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py .                              [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 26%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 31%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/core/test_transformations.py s............................ [ 35%]

..................                                                       [ 38%]

tests/unit/tf/data_augmentation/test_misc.py .                           [ 38%]

tests/unit/tf/data_augmentation/test_negative_sampling.py ..........     [ 40%]

tests/unit/tf/data_augmentation/test_noise.py .....                      [ 40%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 40%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 41%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 41%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 41%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 41%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 41%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 41%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 41%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 42%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 42%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]

.....                                                                    [ 48%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 51%]

tests/unit/tf/layers/test_queue.py ..............                        [ 53%]

tests/unit/tf/losses/test_losses.py .......................              [ 56%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 57%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 60%]

tests/unit/tf/models/test_base.py s................                      [ 62%]

tests/unit/tf/models/test_benchmark.py ..                                [ 63%]

tests/unit/tf/models/test_ranking.py ..................................  [ 68%]

tests/unit/tf/models/test_retrieval.py ................................  [ 72%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 73%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 75%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 76%]

tests/unit/tf/prediction_tasks/test_regression.py ..                     [ 76%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 76%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 77%]

tests/unit/tf/predictions/test_base.py .....                             [ 78%]

tests/unit/tf/predictions/test_classification.py .......                 [ 79%]

tests/unit/tf/predictions/test_dot_product.py ........                   [ 80%]

tests/unit/tf/predictions/test_regression.py ..                          [ 80%]

tests/unit/tf/predictions/test_sampling.py ....                          [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 81%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 83%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 84%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 90%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_fileda20_r3u.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/sequential_block_5/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/sequential_block_5/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/sequential_block_5/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:1078: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 675 passed, 11 skipped, 1030 warnings in 963.02s (0:16:03) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins6133307794422847305.sh

nvidia-merlin-bot · 2022-09-09T03:20:57Z

Click to view CI Results

GitHub pull request #717 of commit c18fc738224e55beaff4cb6002eb8b5e4451aef1, no merge conflicts.
Running as SYSTEM
Setting status of c18fc738224e55beaff4cb6002eb8b5e4451aef1 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1176/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse c18fc738224e55beaff4cb6002eb8b5e4451aef1^{commit} # timeout=10
Checking out Revision c18fc738224e55beaff4cb6002eb8b5e4451aef1 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f c18fc738224e55beaff4cb6002eb8b5e4451aef1 # timeout=10
Commit message: "Adjusting FMBlock"
 > git rev-list --no-walk cb87acc168fd00b7bc2df168e5e3978fc0d3bf29 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins2083787317041757524.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (22.1.0)
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 686 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  9%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py .                              [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 26%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 31%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/core/test_transformations.py s............................ [ 35%]

..................                                                       [ 38%]

tests/unit/tf/data_augmentation/test_misc.py .                           [ 38%]

tests/unit/tf/data_augmentation/test_negative_sampling.py ..........     [ 40%]

tests/unit/tf/data_augmentation/test_noise.py .....                      [ 40%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 40%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 41%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 41%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 41%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 41%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 41%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 41%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 41%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 42%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 42%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]

.....                                                                    [ 48%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 51%]

tests/unit/tf/layers/test_queue.py ..............                        [ 53%]

tests/unit/tf/losses/test_losses.py .......................              [ 56%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 57%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 60%]

tests/unit/tf/models/test_base.py s................                      [ 62%]

tests/unit/tf/models/test_benchmark.py ..                                [ 63%]

tests/unit/tf/models/test_ranking.py ..................................  [ 68%]

tests/unit/tf/models/test_retrieval.py ................................  [ 72%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 73%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 75%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 76%]

tests/unit/tf/prediction_tasks/test_regression.py ..                     [ 76%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 76%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 77%]

tests/unit/tf/predictions/test_base.py .....                             [ 78%]

tests/unit/tf/predictions/test_classification.py .......                 [ 79%]

tests/unit/tf/predictions/test_dot_product.py ........                   [ 80%]

tests/unit/tf/predictions/test_regression.py ..                          [ 80%]

tests/unit/tf/predictions/test_sampling.py ....                          [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 81%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 83%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 84%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 90%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_file0l5ezxq4.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:1078: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 675 passed, 11 skipped, 1030 warnings in 959.05s (0:15:59) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins14105039611225086074.sh

nvidia-merlin-bot · 2022-09-09T03:44:00Z

Click to view CI Results

GitHub pull request #717 of commit 9eae80eca9cea11b6c1d7a2d51ecad5840417828, no merge conflicts.
Running as SYSTEM
Setting status of 9eae80eca9cea11b6c1d7a2d51ecad5840417828 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1177/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse 9eae80eca9cea11b6c1d7a2d51ecad5840417828^{commit} # timeout=10
Checking out Revision 9eae80eca9cea11b6c1d7a2d51ecad5840417828 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 9eae80eca9cea11b6c1d7a2d51ecad5840417828 # timeout=10
Commit message: "Fixing lint issues"
 > git rev-list --no-walk c18fc738224e55beaff4cb6002eb8b5e4451aef1 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins5817833640320918358.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 686 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  9%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py .                              [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 26%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 31%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/core/test_transformations.py s............................ [ 35%]

..................                                                       [ 38%]

tests/unit/tf/data_augmentation/test_misc.py .                           [ 38%]

tests/unit/tf/data_augmentation/test_negative_sampling.py ..........     [ 40%]

tests/unit/tf/data_augmentation/test_noise.py .....                      [ 40%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 40%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 41%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 41%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 41%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 41%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 41%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 41%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 41%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 42%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 42%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]

.....                                                                    [ 48%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 51%]

tests/unit/tf/layers/test_queue.py ..............                        [ 53%]

tests/unit/tf/losses/test_losses.py .......................              [ 56%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 57%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 60%]

tests/unit/tf/models/test_base.py s................                      [ 62%]

tests/unit/tf/models/test_benchmark.py ..                                [ 63%]

tests/unit/tf/models/test_ranking.py ..................................  [ 68%]

tests/unit/tf/models/test_retrieval.py ................................  [ 72%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 73%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 75%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 76%]

tests/unit/tf/prediction_tasks/test_regression.py ..                     [ 76%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 76%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 77%]

tests/unit/tf/predictions/test_base.py .....                             [ 78%]

tests/unit/tf/predictions/test_classification.py .......                 [ 79%]

tests/unit/tf/predictions/test_dot_product.py ........                   [ 80%]

tests/unit/tf/predictions/test_regression.py ..                          [ 80%]

tests/unit/tf/predictions/test_sampling.py ....                          [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 81%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 83%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 84%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 90%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_file2lolhene.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:1078: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 675 passed, 11 skipped, 1030 warnings in 961.70s (0:16:01) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins3589659317840274405.sh

oliverholworthy · 2022-09-09T16:51:28Z

merlin/models/tf/inputs/base.py

    pre: Optional[BlockType] = None,
    post: Optional[BlockType] = None,
    aggregation: Optional[TabularAggregationType] = "concat",
+    **kwargs,


if we're passing these kwargs to the Embeddings, maybe can rename this to embeddings_kwargs?

And why would these kwargs on the input block be passed to the embeddings instead of the ParallelBlock for example? If the embeddings needs to be customized from the default that can already be achieved by passing in the embeddings param?

Makes sense @oliverholworthy . As the user can always provide the embeddings as an argment, the **kwargs can be forwarded to another block (ParallelBlock)

oliverholworthy · 2022-09-09T16:54:04Z

merlin/models/tf/models/ranking.py


-    pairwise_block = FMPairwiseInteraction().prepare(aggregation=StackFeatures(axis=-1))
-    deep_block = deep_block.prepare(aggregation=ConcatFeatures())
+    input_block = input_block or InputBlockV2(schema, dim=embedding_dim, aggregation=None, **kwargs)


Perhaps it would be clearer to change the default aggregation in the InputBlock to be None and change the places where an aggregation is required to pass it?

The only places where currently InputBlockV2(...., aggregation=None) are for DeepFM and FMBlock. So I it seems that is the exception and not the rule.

nvidia-merlin-bot · 2022-09-09T18:49:58Z

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GitHub pull request #717 of commit ee83e4f5cd27c5d0765f207a531da22032823d32, no merge conflicts.
Running as SYSTEM
Setting status of ee83e4f5cd27c5d0765f207a531da22032823d32 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1182/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse ee83e4f5cd27c5d0765f207a531da22032823d32^{commit} # timeout=10
Checking out Revision ee83e4f5cd27c5d0765f207a531da22032823d32 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f ee83e4f5cd27c5d0765f207a531da22032823d32 # timeout=10
Commit message: "Making DeepFM accept wide_inputs for more flexibility (e.g. multi-hot support), fixing bugs and adding tests to FMBlock"
 > git rev-list --no-walk 1a8a386ba97107613b751c5d88bbba5a96cf4cca # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins12955174775313121952.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 688 items
tests/unit/config/test_schema.py ....                                    [  0%]

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tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

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tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

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tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  9%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py ...                            [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

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tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 27%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 31%]

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tests/unit/tf/inputs/test_continuous.py .....                            [ 43%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]

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tests/unit/tf/inputs/test_tabular.py ..................                  [ 51%]

tests/unit/tf/layers/test_queue.py ..............                        [ 53%]

tests/unit/tf/losses/test_losses.py .......................              [ 56%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 57%]

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tests/unit/tf/models/test_base.py s................                      [ 63%]

tests/unit/tf/models/test_benchmark.py ..                                [ 63%]

tests/unit/tf/models/test_ranking.py ..................................  [ 68%]

tests/unit/tf/models/test_retrieval.py ................................  [ 72%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 73%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 75%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 76%]

tests/unit/tf/prediction_tasks/test_regression.py ..                     [ 76%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 76%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 77%]

tests/unit/tf/predictions/test_base.py .....                             [ 78%]

tests/unit/tf/predictions/test_classification.py .......                 [ 79%]

tests/unit/tf/predictions/test_dot_product.py ........                   [ 80%]

tests/unit/tf/predictions/test_regression.py ..                          [ 80%]

tests/unit/tf/predictions/test_sampling.py ....                          [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 81%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 84%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 84%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 90%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filewwz_bklm.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:1077: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 677 passed, 11 skipped, 1034 warnings in 962.29s (0:16:02) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins11343461032109086018.sh

nvidia-merlin-bot · 2022-09-09T19:06:18Z

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 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse d8454ebe55bea1c976169d314cb4bff261615c2c^{commit} # timeout=10
Checking out Revision d8454ebe55bea1c976169d314cb4bff261615c2c (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f d8454ebe55bea1c976169d314cb4bff261615c2c # timeout=10
Commit message: "Making DeepFM accept wide_inputs for more flexibility (e.g. multi-hot support), fixing bugs and adding tests to FMBlock"
 > git rev-list --no-walk ee83e4f5cd27c5d0765f207a531da22032823d32 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins16037232090776809191.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
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Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (4.9.1)
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Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 688 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  9%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py ...                            [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 27%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 31%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/core/test_transformations.py s............................ [ 36%]

..................                                                       [ 38%]

tests/unit/tf/data_augmentation/test_misc.py .                           [ 38%]

tests/unit/tf/data_augmentation/test_negative_sampling.py ..........     [ 40%]

tests/unit/tf/data_augmentation/test_noise.py .....                      [ 40%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 41%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 41%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 41%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 41%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 41%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 41%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 42%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 42%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 42%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 43%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]

.....                                                                    [ 48%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 51%]

tests/unit/tf/layers/test_queue.py ..............                        [ 53%]

tests/unit/tf/losses/test_losses.py .......................              [ 56%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 57%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 60%]

tests/unit/tf/models/test_base.py s................                      [ 63%]

tests/unit/tf/models/test_benchmark.py ..                                [ 63%]

tests/unit/tf/models/test_ranking.py ..................................  [ 68%]

tests/unit/tf/models/test_retrieval.py ................................  [ 72%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 73%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 75%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 76%]

tests/unit/tf/prediction_tasks/test_regression.py ..                     [ 76%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 76%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 77%]

tests/unit/tf/predictions/test_base.py .....                             [ 78%]

tests/unit/tf/predictions/test_classification.py .......                 [ 79%]

tests/unit/tf/predictions/test_dot_product.py ........                   [ 80%]

tests/unit/tf/predictions/test_regression.py ..                          [ 80%]

tests/unit/tf/predictions/test_sampling.py ....                          [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 81%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 84%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 84%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 90%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filew__lhxty.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:1077: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 677 passed, 11 skipped, 1034 warnings in 964.24s (0:16:04) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins13190562239929945405.sh

oliverholworthy · 2022-09-12T12:42:21Z

merlin/models/tf/core/transformations.py

+        if schema is not None:
+            self.column_names = schema.column_names
+
+    def call(self, inputs: TabularData, **kwargs) -> TabularData:


If this is intended to be a base class, is it safe to remove this call method?

Sure, the base class FeaturesTensorTypeConversion should not have the call() method, as it need to be defined in child classes.

marcromeyn · 2022-09-13T07:28:29Z

tests/unit/tf/blocks/test_interactions.py

+        {
+            "categorical_ohe": mm.SequentialBlock(
+                mm.Filter(cat_schema_onehot),
+                mm.CategoryEncoding(cat_schema_onehot, sparse=True, output_mode="one_hot"),


If mm.CategoryEncoding already takes in a schema, can’t we make that class also do the filtering?

The thing is that CategoryEncoding can be used with both output_mode="one_hot" and output_mode="multi_hot".
I am not sure we should trust within CategoryEncoding that multi-hot features will be tagged as SEQUENCE / LIST. So I used the test as an example on how the use can ensure the right features are selected for each branch

marcromeyn · 2022-09-13T07:31:13Z

merlin/models/tf/core/transformations.py

+
+@Block.registry.register("to_sparse")
+@tf.keras.utils.register_keras_serializable(package="merlin.models")
+class ToSparseFeatures(FeaturesTensorTypeConversion):


This naming confuses me. In the moving transforms PR, I am proposing to rename our current layers to ListToSparse & ListToRagged (source).

I think it would make more sense to call this one ToSparse and make it take in an optional schema in the constructor. If a schema is provided, only the features in the schema will be converted.

Sure @marcromeyn. Renamed the new transformers to ToSparse and ToDense, and added tests to cover both cases (when schema is provided or not)

marcromeyn · 2022-09-13T07:35:21Z

merlin/models/tf/blocks/interaction.py

+    wide_input_block = wide_input_block or ParallelBlock(
+        {
+            "categorical": CategoryEncoding(cat_schema, output_mode="multi_hot", sparse=True),
+            "continuous": SequentialBlock(Filter(cont_schema), ToSparseFeatures()),


Could be replace with ToSparse(cont_schema) with my previous comment.

Another maybe easier to read way could be:

InputBlockV2( cont_schema, CategoryEncoding(cat_schema, output_mode="multi_hot", sparse=True), post=ToSparseFeatures() )

The CategoryEncoding needs the sparse argument for the usage of the right TF op for encoding. But I have converted the continuous branch from SequentialBlock(Filter(cont_schema), ToSparseFeatures()) to Filter(cont_schema, post=ToSparse())

marcromeyn · 2022-09-13T07:39:21Z

merlin/models/tf/blocks/interaction.py

+    )
+
+    first_order = wide_input_block.connect(
+        MLPBlock([1], activation="linear", use_bias=True, **kwargs)


Seems like **kwargs is being forwarded to a few different blocks, if possible we’d like to remove this as much as possible since it leads to more understandable code. If you want to allow for full customization is would be cleaner if a param like wide_logit be added to FMBlock and make Dense(1, activation="linear", use_bias=True) the default. MLPBlock is not needed since we only need one dense-layer.

Makes sense. I was forwarding those **kwargs to be able to add regularization (dropout, kernel reg) to the wide part. But have created the wide_logit_block argument you suggested to make it more explicit.

marcromeyn · 2022-09-13T07:40:45Z

merlin/models/tf/blocks/interaction.py

+    )
+
+    fm_input_block = fm_input_block or InputBlockV2(
+        cat_schema, dim=factors_dim, aggregation=None, **kwargs


We already expose a fm_input_block so I feel like forwaring **kwargs could be removed. Also, aggregation=None is the default so we could remove that here.

marcromeyn · 2022-09-13T07:41:17Z

merlin/models/tf/blocks/interaction.py

+        cat_schema, dim=factors_dim, aggregation=None, **kwargs
+    )
+    pairwise_interaction = SequentialBlock(
+        Filter(cat_schema),


The InputBlock already does filtering so this could be removed.

I think we should keep that Filter, as the user might want to provide his own fm_input_block, whose schema might not be filtered to output only categ features.

nvidia-merlin-bot · 2022-09-13T08:00:56Z

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Checking out Revision 48b7e4ade146052710796e64c8d7f6425fa53e38 (detached)
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Commit message: "Merge branch 'main' into ranking_models_inputs"
 > git rev-list --no-walk ba5c54b424b4b1fa849e8d876affb31106acf49d # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins15352671815863805251.sh
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 691 items
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=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
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/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
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/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 13 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/data_augmentation/test_noise.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/core/test_transformations.py: 10 warnings

tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/predictions/test_base.py: 5 warnings

tests/unit/tf/predictions/test_classification.py: 7 warnings

tests/unit/tf/predictions/test_dot_product.py: 8 warnings

tests/unit/tf/predictions/test_regression.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]

tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]

tests/unit/tf/test_dataset.py::test_tf_catname_ordering

tests/unit/tf/test_dataset.py::test_tf_map

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/predictions/test_classification.py: 12 warnings

tests/unit/tf/predictions/test_dot_product.py: 2 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filet6n923z.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag_.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]

tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_4/sequential_block_3/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:1077: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 680 passed, 11 skipped, 1043 warnings in 990.52s (0:16:30) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins15102925909240585320.sh

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nvidia-merlin-bot · 2022-09-20T18:45:36Z

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GitHub pull request #717 of commit c55b32df2b76dba4eb6771a2554745fecfd9abcb, no merge conflicts.
Running as SYSTEM
Setting status of c55b32df2b76dba4eb6771a2554745fecfd9abcb to PENDING with url https://10.20.13.93:8080/job/merlin_models/1301/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse c55b32df2b76dba4eb6771a2554745fecfd9abcb^{commit} # timeout=10
 > git rev-parse origin/c55b32df2b76dba4eb6771a2554745fecfd9abcb^{commit} # timeout=10
 > git rev-parse c55b32df2b76dba4eb6771a2554745fecfd9abcb^{commit} # timeout=10
ERROR: Couldn't find any revision to build. Verify the repository and branch configuration for this job.
Performing Post build task...
Match found for : : True
Logical operation result is TRUE
Running script  : #!/bin/bash
cd /var/jenkins_home/
CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log" 
[merlin_models] $ /bin/bash /tmp/jenkins2903456058832889539.sh

nvidia-merlin-bot · 2022-09-20T19:51:35Z

Click to view CI Results

GitHub pull request #717 of commit d8782f08784e32dc2d1437eba136aac9374b2218, no merge conflicts.
Running as SYSTEM
Setting status of d8782f08784e32dc2d1437eba136aac9374b2218 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1305/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse d8782f08784e32dc2d1437eba136aac9374b2218^{commit} # timeout=10
Checking out Revision d8782f08784e32dc2d1437eba136aac9374b2218 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f d8782f08784e32dc2d1437eba136aac9374b2218 # timeout=10
Commit message: "Adjusting to use the updated InputBlockV2"
 > git rev-list --no-walk b350c60f8e1c8ee61580862394e3a52ecfe720e5 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins3214591760436895500.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 695 items
tests/unit/config/test_schema.py .... tests/unit/datasets/test_advertising.py .s tests/unit/datasets/test_ecommerce.py ..sss tests/unit/datasets/test_entertainment.py ....sss. tests/unit/datasets/test_social.py . tests/unit/datasets/test_synthetic.py ...... tests/unit/implicit/test_implicit.py . tests/unit/lightfm/test_lightfm.py . tests/unit/tf/test_core.py ...... tests/unit/tf/test_dataset.py ................ tests/unit/tf/test_public_api.py . tests/unit/tf/blocks/test_cross.py ........... tests/unit/tf/blocks/test_dlrm.py .......... tests/unit/tf/blocks/test_interactions.py ... tests/unit/tf/blocks/test_mlp.py ............. tests/unit/tf/blocks/test_optimizer.py ..................... tests/unit/tf/blocks/retrieval/test_base.py . tests/unit/tf/blocks/retrieval/test_matrix_factorization.py tests/unit/tf/blocks/retrieval/test_two_tower.py tests/unit/tf/blocks/sampling/test_cross_batch.py . tests/unit/tf/blocks/sampling/test_in_batch.py . tests/unit/tf/core/test_aggregation.py ......... tests/unit/tf/core/test_base.py .. tests/unit/tf/core/test_combinators.py tests/unit/tf/core/test_encoder.py . tests/unit/tf/core/test_index.py ... tests/unit/tf/core/test_prediction.py .. tests/unit/tf/core/test_tabular.py .... tests/unit/tf/examples/test_01_getting_started.py . tests/unit/tf/examples/test_02_dataschema.py . tests/unit/tf/examples/test_03_exploring_diffe tests/unit/tf/examples/test_04_export_ranking_models.py . tests/unit/tf/examples/test_05_export_retrieval_model.py . tests/unit/tf/examples/test_06_advanced_own_architecture.py tests/unit/tf/examples/test_07_train_traditional_models.py tests/unit/tf/examples/test_usecase_ecommerce_ tests/unit/tf/examples/test_usecase_pretrained tests/unit/tf/inputs/test_continuous.py ..... tests/unit/tf/inputs/test_embedding.py ..... tests/unit/tf/inputs/test_tabular.py .................. tests/unit/tf/layers/test_queue.py .............. tests/unit/tf/losses/test_losses.py ....................... tests/unit/tf/metrics/test_metrics_popularity.py ..... tests/unit/tf/metrics/test_metrics_topk.py tests/unit/tf/models/test_base.py s................. tests/unit/tf/models/test_benchmark.py .. tests/unit/tf/models/test_ranking.py tests/unit/tf/models/test_retrieval.py tests/unit/tf/outputs/test_base.py ..... tests/unit/tf/outputs/test_classification.py ...... tests/unit/tf/outputs/test_contrastive.py ......... tests/unit/tf/outputs/test_regression.py .. tests/unit/tf/outputs/test_sampling.py .... tests/unit/tf/prediction_tasks/test_classification.py .. tests/unit/tf/prediction_tasks/test_multi_task.py tests/unit/tf/prediction_tasks/test_next_item.py ..... tests/unit/tf/prediction_tasks/test_regression.py ..... tests/unit/tf/prediction_tasks/test_retrieval.py . tests/unit/tf/prediction_tasks/test_sampling.py ...... tests/unit/tf/transforms/test_bias.py .. tests/unit/tf/transforms/test_features.py ................. tests/unit/tf/transforms/test_negative_sampling.py tests/unit/tf/transforms/test_noise.py ..... tests/unit/tf/transforms/test_tensor.py .. tests/unit/tf/utils/test_batch.py .... tests/unit/tf/utils/test_tf_utils.py ..... tests/unit/torch/test_dataset.py ......... tests/unit/torch/test_public_api.py . tests/unit/torch/block/test_base.py .... tests/unit/torch/block/test_mlp.py . tests/unit/torch/features/test_continuous.py .. tests/unit/torch/features/test_embedding.py .............. tests/unit/torch/features/test_tabular.py .... tests/unit/torch/model/test_head.py ............ tests/unit/torch/model/test_model.py .. tests/unit/torch/tabular/test_aggregation.py ........ tests/unit/torch/tabular/test_tabular.py ... tests/unit/torch/tabular/test_transformations.py ....... tests/unit/utils/test_schema_utils.py tests/unit/xgb/test_xgboost.py ....................

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=================================== FAILURES ===================================

___________________ test_deepfm_model_only_categ_feats[True] ___________________
self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fd1db6ee540>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: argument of type 'Tags' is not iterable
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
self = SequentialBlock(

(layers): List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): '... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): 'item_category'

(2): 'user_id'

..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build(self, input_shape=None):
    """Builds the sequential block

    Parameters
    ----------
    input_shape : tf.TensorShape, optional
        The input shape, by default None
    """
    self._maybe_propagate_context(input_shape)


  build_sequentially(self, self.layers, input_shape)


merlin/models/tf/core/combinators.py:129:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:
            layer.build(input_shape)
        except TypeError:
            t, v, tb = sys.exc_info()
            if isinstance(input_shape, dict) and isinstance(last_layer, TabularBlock):
                v = TypeError(
                    f"Couldn't build {layer}, "
                    f"did you forget to add aggregation to {last_layer}?"
                )


          six.reraise(t, v, tb)


merlin/models/tf/core/combinators.py:780:

tp = <class 'TypeError'>, value = None, tb = None
def reraise(tp, value, tb=None):
    try:
        if value is None:
            value = tp()
        if value.__traceback__ is not tb:


          raise value.with_traceback(tb)


../../../.local/lib/python3.8/site-packages/six.py:702:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fd1db6ee540>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build Filter(

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       ), did you forget to add aggregation to ParallelBlock(

E         (parallel_layers): Dict(

E           (categorical): ParallelBlock(

E             (parallel_layers): Dict(

E               (item_id): EmbeddingTable(

E                 (features): Dict(

E                   (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ITEM: 'item'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.02570032, -0.03179889, -0.03721094, ...,  0.01357832,

E                           -0.02899024,  0.01453921],

E                          [-0.04399979, -0.00753646, -0.00720472, ...,  0.00024997,

E                            0.01565896, -0.02869811],

E                          [ 0.02614701, -0.03707584, -0.02421354, ...,  0.02661935,

E                            0.04587359, -0.00313843],

E                          ...,

E                          [-0.01801817,  0.04142593,  0.02776836, ..., -0.01339476,

E                           -0.04107499, -0.0064096 ],

E                          [-0.04987758, -0.03873288, -0.030273  , ..., -0.01512405,

E                           -0.01584437, -0.02466037],

E                          [-0.0137626 ,  0.04578525, -0.02953109, ..., -0.01456221,

E                           -0.00472139,  0.0140526 ]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E               (item_category): EmbeddingTable(

E                 (features): Dict(

E                   (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                   array([[-0.00171213, -0.01755921,  0.02857446, ...,  0.02301586,

E                           -0.01788441,  0.03623377],

E                          [ 0.00862727, -0.03390821,  0.00515376, ..., -0.01194553,

E                            0.03372042, -0.0090534 ],

E                          [ 0.00476586,  0.0073352 , -0.02046644, ..., -0.00370144,

E                            0.00360762, -0.00312662],

E                          ...,

E                          [-0.00357788,  0.03606515,  0.01531923, ..., -0.01783171,

E                           -0.04155123,  0.01742039],

E                          [ 0.0191812 ,  0.04684928, -0.00022608, ...,  0.02704518,

E                            0.01896758, -0.03436905],

E                          [-0.01931492,  0.0356341 , -0.0218243 , ..., -0.00832837,

E                           -0.03270398, -0.00795592]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E               (user_id): EmbeddingTable(

E                 (features): Dict(

E                   (user_id): ColumnSchema(name='user_id', tags={<Tags.USER: 'user'>, <Tags.CATEGORICAL: 'categorical'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[ 0.03487933,  0.01901175, -0.0284261 , ..., -0.01840165,

E                            0.01250464, -0.00717173],

E                          [ 0.01682284,  0.03735187,  0.01156843, ..., -0.00881452,

E                           -0.02928144,  0.00875034],

E                          [-0.00974071, -0.0131126 ,  0.03639458, ...,  0.00978114,

E                           -0.01984922, -0.04836261],

E                          ...,

E                          [-0.02130981, -0.0425184 ,  0.03600109, ...,  0.0156843 ,

E                           -0.01499469,  0.018743  ],

E                          [ 0.01336529,  0.0117893 ,  0.02674155, ..., -0.03907121,

E                           -0.04320374,  0.00016158],

E                          [-0.02595156, -0.02762016, -0.00011653, ...,  0.01858344,

E                           -0.03044537, -0.03146877]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7fd1c11d2a00>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_only_categ_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:171:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:717: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1007: in call

self._maybe_build(inputs)

merlin/models/tf/models/base.py:867: in _maybe_build

super()._maybe_build(inputs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:2759: in _maybe_build

self.build(input_shapes)  # pylint:disable=not-callable

merlin/models/tf/models/base.py:893: in build

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:703: in reraise

raise value

merlin/models/tf/models/base.py:885: in build

layer.build(input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/tabular.py:313: in build

output_shapes = self.compute_call_output_shape(output_shapes)

merlin/models/tf/core/tabular.py:582: in compute_call_output_shape

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582: in 

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build SequentialBlock(

E         (layers): List(

E           (0): ParallelBlock(

E             (parallel_layers): Dict(

E               (categorical): ParallelBlock(

E                 (parallel_layers): Dict(

E                   (item_id): EmbeddingTable(

E                     (features): Dict(

E                       (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ITEM: 'item'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.02570032, -0.03179889, -0.03721094, ...,  0.01357832,

E                               -0.02899024,  0.01453921],

E                              [-0.04399979, -0.00753646, -0.00720472, ...,  0.00024997,

E                                0.01565896, -0.02869811],

E                              [ 0.02614701, -0.03707584, -0.02421354, ...,  0.02661935,

E                                0.04587359, -0.00313843],

E                              ...,

E                              [-0.01801817,  0.04142593,  0.02776836, ..., -0.01339476,

E                               -0.04107499, -0.0064096 ],

E                              [-0.04987758, -0.03873288, -0.030273  , ..., -0.01512405,

E                               -0.01584437, -0.02466037],

E                              [-0.0137626 ,  0.04578525, -0.02953109, ..., -0.01456221,

E                               -0.00472139,  0.0140526 ]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                   (item_category): EmbeddingTable(

E                     (features): Dict(

E                       (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                       array([[-0.00171213, -0.01755921,  0.02857446, ...,  0.02301586,

E                               -0.01788441,  0.03623377],

E                              [ 0.00862727, -0.03390821,  0.00515376, ..., -0.01194553,

E                                0.03372042, -0.0090534 ],

E                              [ 0.00476586,  0.0073352 , -0.02046644, ..., -0.00370144,

E                                0.00360762, -0.00312662],

E                              ...,

E                              [-0.00357788,  0.03606515,  0.01531923, ..., -0.01783171,

E                               -0.04155123,  0.01742039],

E                              [ 0.0191812 ,  0.04684928, -0.00022608, ...,  0.02704518,

E                                0.01896758, -0.03436905],

E                              [-0.01931492,  0.0356341 , -0.0218243 , ..., -0.00832837,

E                               -0.03270398, -0.00795592]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                   (user_id): EmbeddingTable(

E                     (features): Dict(

E                       (user_id): ColumnSchema(name='user_id', tags={<Tags.USER: 'user'>, <Tags.CATEGORICAL: 'categorical'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[ 0.03487933,  0.01901175, -0.0284261 , ..., -0.01840165,

E                                0.01250464, -0.00717173],

E                              [ 0.01682284,  0.03735187,  0.01156843, ..., -0.00881452,

E                               -0.02928144,  0.00875034],

E                              [-0.00974071, -0.0131126 ,  0.03639458, ...,  0.00978114,

E                               -0.01984922, -0.04836261],

E                              ...,

E                              [-0.02130981, -0.0425184 ,  0.03600109, ...,  0.0156843 ,

E                               -0.01499469,  0.018743  ],

E                              [ 0.01336529,  0.0117893 ,  0.02674155, ..., -0.03907121,

E                               -0.04320374,  0.00016158],

E                              [-0.02595156, -0.02762016, -0.00011653, ...,  0.01858344,

E                               -0.03044537, -0.03146877]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E           (1): Filter(

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       ), did you forget to add aggregation to Filter(

E         (feature_names): List(

E           (0): 'item_id'

E           (1): 'item_category'

E           (2): 'user_id'

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError

__________________ test_deepfm_model_only_categ_feats[False] ___________________
self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fd1b86e6ae0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: argument of type 'Tags' is not iterable
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
self = SequentialBlock(

(layers): List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): '... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): 'item_category'

(2): 'user_id'

..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build(self, input_shape=None):
    """Builds the sequential block

    Parameters
    ----------
    input_shape : tf.TensorShape, optional
        The input shape, by default None
    """
    self._maybe_propagate_context(input_shape)


  build_sequentially(self, self.layers, input_shape)


merlin/models/tf/core/combinators.py:129:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:
            layer.build(input_shape)
        except TypeError:
            t, v, tb = sys.exc_info()
            if isinstance(input_shape, dict) and isinstance(last_layer, TabularBlock):
                v = TypeError(
                    f"Couldn't build {layer}, "
                    f"did you forget to add aggregation to {last_layer}?"
                )


          six.reraise(t, v, tb)


merlin/models/tf/core/combinators.py:780:

tp = <class 'TypeError'>, value = None, tb = None
def reraise(tp, value, tb=None):
    try:
        if value is None:
            value = tp()
        if value.__traceback__ is not tb:


          raise value.with_traceback(tb)


../../../.local/lib/python3.8/site-packages/six.py:702:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fd1b86e6ae0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build Filter(

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       ), did you forget to add aggregation to ParallelBlock(

E         (parallel_layers): Dict(

E           (categorical): ParallelBlock(

E             (parallel_layers): Dict(

E               (item_id): EmbeddingTable(

E                 (features): Dict(

E                   (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ITEM: 'item'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.03019184, -0.02111769,  0.01050959, ..., -0.0172928 ,

E                            0.00180059, -0.045084  ],

E                          [-0.02480277, -0.01521372,  0.02216304, ..., -0.04987577,

E                           -0.00641552, -0.02169349],

E                          [-0.02918756, -0.02672333, -0.02868438, ..., -0.02571416,

E                            0.03517436, -0.00967953],

E                          ...,

E                          [ 0.03259308, -0.02479436, -0.00282454, ..., -0.02942575,

E                           -0.01611086, -0.04680791],

E                          [-0.01399783, -0.01902272,  0.0045493 , ..., -0.04091265,

E                            0.02443952, -0.0302704 ],

E                          [ 0.00040155,  0.01888806,  0.01079621, ..., -0.03706405,

E                           -0.0367962 ,  0.01322072]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E               (item_category): EmbeddingTable(

E                 (features): Dict(

E                   (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                   array([[ 0.03009479,  0.03900156, -0.02817373, ...,  0.00038097,

E                            0.03044956, -0.04745512],

E                          [-0.03989201, -0.0167386 ,  0.00648766, ..., -0.02095594,

E                           -0.00021695,  0.03996665],

E                          [ 0.03626612,  0.04377914,  0.03230296, ..., -0.03567819,

E                           -0.03715755,  0.0365119 ],

E                          ...,

E                          [ 0.04226034,  0.03046766, -0.04382597, ..., -0.02501919,

E                           -0.01451756,  0.02671708],

E                          [-0.04766364, -0.03410469,  0.0293314 , ...,  0.03606859,

E                           -0.0374441 , -0.03671223],

E                          [-0.02736839, -0.02183306, -0.04594931, ..., -0.01534285,

E                            0.00578789, -0.00043597]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E               (user_id): EmbeddingTable(

E                 (features): Dict(

E                   (user_id): ColumnSchema(name='user_id', tags={<Tags.USER: 'user'>, <Tags.CATEGORICAL: 'categorical'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.02361941, -0.03001617,  0.01258189, ...,  0.03452733,

E                           -0.00601999, -0.04937288],

E                          [-0.02313321,  0.01118822,  0.02567822, ...,  0.00511445,

E                           -0.026209  ,  0.00258926],

E                          [ 0.02692063, -0.02748579, -0.02021652, ...,  0.03992691,

E                           -0.00202364, -0.03304167],

E                          ...,

E                          [-0.03796924,  0.03143172,  0.02334212, ..., -0.02866315,

E                            0.0450588 , -0.01824418],

E                          [-0.02232841,  0.00791576, -0.02536985, ...,  0.0101485 ,

E                           -0.00764833,  0.04040624],

E                          [-0.02146883, -0.00375839, -0.02736113, ..., -0.03641828,

E                            0.04995141,  0.02966121]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7fd1c22308b0>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_only_categ_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:171:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:717: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1007: in call

self._maybe_build(inputs)

merlin/models/tf/models/base.py:867: in _maybe_build

super()._maybe_build(inputs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:2759: in _maybe_build

self.build(input_shapes)  # pylint:disable=not-callable

merlin/models/tf/models/base.py:893: in build

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:703: in reraise

raise value

merlin/models/tf/models/base.py:885: in build

layer.build(input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/tabular.py:313: in build

output_shapes = self.compute_call_output_shape(output_shapes)

merlin/models/tf/core/tabular.py:582: in compute_call_output_shape

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582: in 

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build SequentialBlock(

E         (layers): List(

E           (0): ParallelBlock(

E             (parallel_layers): Dict(

E               (categorical): ParallelBlock(

E                 (parallel_layers): Dict(

E                   (item_id): EmbeddingTable(

E                     (features): Dict(

E                       (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ITEM: 'item'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.03019184, -0.02111769,  0.01050959, ..., -0.0172928 ,

E                                0.00180059, -0.045084  ],

E                              [-0.02480277, -0.01521372,  0.02216304, ..., -0.04987577,

E                               -0.00641552, -0.02169349],

E                              [-0.02918756, -0.02672333, -0.02868438, ..., -0.02571416,

E                                0.03517436, -0.00967953],

E                              ...,

E                              [ 0.03259308, -0.02479436, -0.00282454, ..., -0.02942575,

E                               -0.01611086, -0.04680791],

E                              [-0.01399783, -0.01902272,  0.0045493 , ..., -0.04091265,

E                                0.02443952, -0.0302704 ],

E                              [ 0.00040155,  0.01888806,  0.01079621, ..., -0.03706405,

E                               -0.0367962 ,  0.01322072]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                   (item_category): EmbeddingTable(

E                     (features): Dict(

E                       (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                       array([[ 0.03009479,  0.03900156, -0.02817373, ...,  0.00038097,

E                                0.03044956, -0.04745512],

E                              [-0.03989201, -0.0167386 ,  0.00648766, ..., -0.02095594,

E                               -0.00021695,  0.03996665],

E                              [ 0.03626612,  0.04377914,  0.03230296, ..., -0.03567819,

E                               -0.03715755,  0.0365119 ],

E                              ...,

E                              [ 0.04226034,  0.03046766, -0.04382597, ..., -0.02501919,

E                               -0.01451756,  0.02671708],

E                              [-0.04766364, -0.03410469,  0.0293314 , ...,  0.03606859,

E                               -0.0374441 , -0.03671223],

E                              [-0.02736839, -0.02183306, -0.04594931, ..., -0.01534285,

E                                0.00578789, -0.00043597]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                   (user_id): EmbeddingTable(

E                     (features): Dict(

E                       (user_id): ColumnSchema(name='user_id', tags={<Tags.USER: 'user'>, <Tags.CATEGORICAL: 'categorical'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.02361941, -0.03001617,  0.01258189, ...,  0.03452733,

E                               -0.00601999, -0.04937288],

E                              [-0.02313321,  0.01118822,  0.02567822, ...,  0.00511445,

E                               -0.026209  ,  0.00258926],

E                              [ 0.02692063, -0.02748579, -0.02021652, ...,  0.03992691,

E                               -0.00202364, -0.03304167],

E                              ...,

E                              [-0.03796924,  0.03143172,  0.02334212, ..., -0.02866315,

E                                0.0450588 , -0.01824418],

E                              [-0.02232841,  0.00791576, -0.02536985, ...,  0.0101485 ,

E                               -0.00764833,  0.04040624],

E                              [-0.02146883, -0.00375839, -0.02736113, ..., -0.03641828,

E                                0.04995141,  0.02966121]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E           (1): Filter(

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       ), did you forget to add aggregation to Filter(

E         (feature_names): List(

E           (0): 'item_id'

E           (1): 'item_category'

E           (2): 'user_id'

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError

______________ test_deepfm_model_categ_and_continuous_feats[True] ______________
self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fd1c32c08b0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: argument of type 'Tags' is not iterable
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
self = SequentialBlock(

(layers): List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): '...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): 'item_category'

(2): 'user_id'

...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build(self, input_shape=None):
    """Builds the sequential block

    Parameters
    ----------
    input_shape : tf.TensorShape, optional
        The input shape, by default None
    """
    self._maybe_propagate_context(input_shape)


  build_sequentially(self, self.layers, input_shape)


merlin/models/tf/core/combinators.py:129:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:
            layer.build(input_shape)
        except TypeError:
            t, v, tb = sys.exc_info()
            if isinstance(input_shape, dict) and isinstance(last_layer, TabularBlock):
                v = TypeError(
                    f"Couldn't build {layer}, "
                    f"did you forget to add aggregation to {last_layer}?"
                )


          six.reraise(t, v, tb)


merlin/models/tf/core/combinators.py:780:

tp = <class 'TypeError'>, value = None, tb = None
def reraise(tp, value, tb=None):
    try:
        if value is None:
            value = tp()
        if value.__traceback__ is not tb:


          raise value.with_traceback(tb)


../../../.local/lib/python3.8/site-packages/six.py:702:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fd1c32c08b0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build Filter(

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       ), did you forget to add aggregation to ParallelBlock(

E         (parallel_layers): Dict(

E           (categorical): ParallelBlock(

E             (parallel_layers): Dict(

E               (item_id): EmbeddingTable(

E                 (features): Dict(

E                   (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ITEM: 'item'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.02054526, -0.02200582,  0.01605621, ...,  0.02267637,

E                            0.01410104, -0.00604937],

E                          [-0.01742599,  0.04021536, -0.02411727, ...,  0.01386498,

E                            0.03677467,  0.01996145],

E                          [ 0.00269372, -0.03342003, -0.04908162, ...,  0.04889261,

E                            0.01890564,  0.03716913],

E                          ...,

E                          [-0.03502411, -0.03122339,  0.03508973, ..., -0.03769619,

E                           -0.03154981, -0.01263622],

E                          [-0.04772219,  0.01338226,  0.00763812, ..., -0.00482909,

E                           -0.0085258 ,  0.00261904],

E                          [-0.01138081, -0.04893042,  0.00227406, ...,  0.00839734,

E                           -0.04513972,  0.00374116]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E               (item_category): EmbeddingTable(

E                 (features): Dict(

E                   (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                   array([[-0.01045848,  0.04803827, -0.01155933, ..., -0.04183086,

E                            0.04058427, -0.01171237],

E                          [ 0.04373075,  0.00500919,  0.04929531, ..., -0.03939838,

E                            0.00203785, -0.00989903],

E                          [ 0.04189702, -0.03120966, -0.00791519, ...,  0.00858233,

E                            0.01543109,  0.01549872],

E                          ...,

E                          [ 0.00184605,  0.0434628 , -0.02273744, ...,  0.04810428,

E                           -0.02350572, -0.00790094],

E                          [ 0.04012373, -0.03322871,  0.0497464 , ..., -0.01007406,

E                           -0.00045862,  0.00152151],

E                          [-0.02862725, -0.01967434, -0.00228672, ...,  0.00557036,

E                           -0.03056601, -0.03539287]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E               (user_id): EmbeddingTable(

E                 (features): Dict(

E                   (user_id): ColumnSchema(name='user_id', tags={<Tags.USER: 'user'>, <Tags.CATEGORICAL: 'categorical'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[ 0.00854038, -0.00232061,  0.00140734, ...,  0.04226798,

E                           -0.03381544, -0.02190081],

E                          [ 0.03141161,  0.01365862, -0.01205138, ...,  0.0265334 ,

E                           -0.03197428,  0.03824944],

E                          [-0.03385307, -0.0046339 ,  0.01335522, ...,  0.04823765,

E                            0.04733351,  0.02277596],

E                          ...,

E                          [-0.04601424,  0.01994986,  0.04879292, ..., -0.00574926,

E                            0.04242582, -0.00810363],

E                          [ 0.01967518, -0.03233056, -0.00338495, ...,  0.04127854,

E                           -0.02612364,  0.03764292],

E                          [-0.0034511 ,  0.04024262,  0.02186977, ...,  0.04592418,

E                           -0.00961372,  0.02344227]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7fd1ba555c40>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_categ_and_continuous_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "user_age", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:186:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:717: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1007: in call

self._maybe_build(inputs)

merlin/models/tf/models/base.py:867: in _maybe_build

super()._maybe_build(inputs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:2759: in _maybe_build

self.build(input_shapes)  # pylint:disable=not-callable

merlin/models/tf/models/base.py:893: in build

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:703: in reraise

raise value

merlin/models/tf/models/base.py:885: in build

layer.build(input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/tabular.py:313: in build

output_shapes = self.compute_call_output_shape(output_shapes)

merlin/models/tf/core/tabular.py:582: in compute_call_output_shape

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582: in 

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build SequentialBlock(

E         (layers): List(

E           (0): ParallelBlock(

E             (parallel_layers): Dict(

E               (categorical): ParallelBlock(

E                 (parallel_layers): Dict(

E                   (item_id): EmbeddingTable(

E                     (features): Dict(

E                       (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ITEM: 'item'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.02054526, -0.02200582,  0.01605621, ...,  0.02267637,

E                                0.01410104, -0.00604937],

E                              [-0.01742599,  0.04021536, -0.02411727, ...,  0.01386498,

E                                0.03677467,  0.01996145],

E                              [ 0.00269372, -0.03342003, -0.04908162, ...,  0.04889261,

E                                0.01890564,  0.03716913],

E                              ...,

E                              [-0.03502411, -0.03122339,  0.03508973, ..., -0.03769619,

E                               -0.03154981, -0.01263622],

E                              [-0.04772219,  0.01338226,  0.00763812, ..., -0.00482909,

E                               -0.0085258 ,  0.00261904],

E                              [-0.01138081, -0.04893042,  0.00227406, ...,  0.00839734,

E                               -0.04513972,  0.00374116]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                   (item_category): EmbeddingTable(

E                     (features): Dict(

E                       (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                       array([[-0.01045848,  0.04803827, -0.01155933, ..., -0.04183086,

E                                0.04058427, -0.01171237],

E                              [ 0.04373075,  0.00500919,  0.04929531, ..., -0.03939838,

E                                0.00203785, -0.00989903],

E                              [ 0.04189702, -0.03120966, -0.00791519, ...,  0.00858233,

E                                0.01543109,  0.01549872],

E                              ...,

E                              [ 0.00184605,  0.0434628 , -0.02273744, ...,  0.04810428,

E                               -0.02350572, -0.00790094],

E                              [ 0.04012373, -0.03322871,  0.0497464 , ..., -0.01007406,

E                               -0.00045862,  0.00152151],

E                              [-0.02862725, -0.01967434, -0.00228672, ...,  0.00557036,

E                               -0.03056601, -0.03539287]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                   (user_id): EmbeddingTable(

E                     (features): Dict(

E                       (user_id): ColumnSchema(name='user_id', tags={<Tags.USER: 'user'>, <Tags.CATEGORICAL: 'categorical'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[ 0.00854038, -0.00232061,  0.00140734, ...,  0.04226798,

E                               -0.03381544, -0.02190081],

E                              [ 0.03141161,  0.01365862, -0.01205138, ...,  0.0265334 ,

E                               -0.03197428,  0.03824944],

E                              [-0.03385307, -0.0046339 ,  0.01335522, ...,  0.04823765,

E                                0.04733351,  0.02277596],

E                              ...,

E                              [-0.04601424,  0.01994986,  0.04879292, ..., -0.00574926,

E                                0.04242582, -0.00810363],

E                              [ 0.01967518, -0.03233056, -0.00338495, ...,  0.04127854,

E                               -0.02612364,  0.03764292],

E                              [-0.0034511 ,  0.04024262,  0.02186977, ...,  0.04592418,

E                               -0.00961372,  0.02344227]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E           (1): Filter(

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       ), did you forget to add aggregation to Filter(

E         (feature_names): List(

E           (0): 'item_id'

E           (1): 'item_category'

E           (2): 'user_id'

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError

_____________ test_deepfm_model_categ_and_continuous_feats[False] ______________
self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fd1d07e0bd0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: argument of type 'Tags' is not iterable
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
self = SequentialBlock(

(layers): List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): '...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): 'item_category'

(2): 'user_id'

...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build(self, input_shape=None):
    """Builds the sequential block

    Parameters
    ----------
    input_shape : tf.TensorShape, optional
        The input shape, by default None
    """
    self._maybe_propagate_context(input_shape)


  build_sequentially(self, self.layers, input_shape)


merlin/models/tf/core/combinators.py:129:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:
            layer.build(input_shape)
        except TypeError:
            t, v, tb = sys.exc_info()
            if isinstance(input_shape, dict) and isinstance(last_layer, TabularBlock):
                v = TypeError(
                    f"Couldn't build {layer}, "
                    f"did you forget to add aggregation to {last_layer}?"
                )


          six.reraise(t, v, tb)


merlin/models/tf/core/combinators.py:780:

tp = <class 'TypeError'>, value = None, tb = None
def reraise(tp, value, tb=None):
    try:
        if value is None:
            value = tp()
        if value.__traceback__ is not tb:


          raise value.with_traceback(tb)


../../../.local/lib/python3.8/site-packages/six.py:702:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fd1d07e0bd0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build Filter(

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       ), did you forget to add aggregation to ParallelBlock(

E         (parallel_layers): Dict(

E           (categorical): ParallelBlock(

E             (parallel_layers): Dict(

E               (item_id): EmbeddingTable(

E                 (features): Dict(

E                   (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ITEM: 'item'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[ 0.00095159, -0.00780176, -0.01070255, ..., -0.0245008 ,

E                           -0.00115737, -0.02886876],

E                          [-0.012412  ,  0.0399057 ,  0.04028242, ..., -0.04560817,

E                            0.00926073, -0.00812765],

E                          [-0.0196968 ,  0.025419  ,  0.00797405, ...,  0.04023785,

E                           -0.0137983 , -0.03792955],

E                          ...,

E                          [ 0.00676475,  0.00952966, -0.01445646, ..., -0.00510135,

E                           -0.04126855, -0.04733052],

E                          [-0.02780222, -0.03965948,  0.04582984, ..., -0.02401545,

E                           -0.04722666,  0.02736635],

E                          [-0.02108997,  0.00042298,  0.04603405, ..., -0.03562424,

E                            0.01985692,  0.0199568 ]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E               (item_category): EmbeddingTable(

E                 (features): Dict(

E                   (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                   array([[ 0.03682753, -0.00775306,  0.00989783, ...,  0.04243133,

E                            0.02013233,  0.0142022 ],

E                          [ 0.03489197, -0.00297128, -0.02943789, ...,  0.02520556,

E                            0.04500607, -0.03690022],

E                          [-0.00748165, -0.01145886,  0.02205738, ...,  0.026886  ,

E                           -0.00793109,  0.03001534],

E                          ...,

E                          [ 0.03647201, -0.02389548,  0.03249105, ..., -0.04324914,

E                            0.02238042, -0.01105363],

E                          [-0.0121203 , -0.02577748, -0.03620006, ...,  0.01091584,

E                           -0.0377016 ,  0.04898537],

E                          [-0.03664935,  0.01268804,  0.03041216, ..., -0.04956597,

E                            0.00527128, -0.00412567]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E               (user_id): EmbeddingTable(

E                 (features): Dict(

E                   (user_id): ColumnSchema(name='user_id', tags={<Tags.USER: 'user'>, <Tags.CATEGORICAL: 'categorical'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.03058578, -0.00037177, -0.04440222, ..., -0.0143258 ,

E                            0.0078275 , -0.02509269],

E                          [ 0.00060018,  0.02608771,  0.04238523, ...,  0.01086897,

E                            0.02085884,  0.01716653],

E                          [-0.02398338, -0.0076279 , -0.0233204 , ..., -0.02491845,

E                           -0.01284405,  0.04893791],

E                          ...,

E                          [ 0.00118636, -0.02557309,  0.04411072, ...,  0.01656295,

E                           -0.00796812, -0.03289662],

E                          [ 0.02188356, -0.01895338, -0.01765849, ..., -0.038915  ,

E                           -0.02888047,  0.01209231],

E                          [ 0.01988271,  0.04559529, -0.0447374 , ...,  0.0016938 ,

E                           -0.01167984, -0.00597036]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7fd1c163a970>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_categ_and_continuous_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "user_age", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:186:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:717: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1007: in call

self._maybe_build(inputs)

merlin/models/tf/models/base.py:867: in _maybe_build

super()._maybe_build(inputs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:2759: in _maybe_build

self.build(input_shapes)  # pylint:disable=not-callable

merlin/models/tf/models/base.py:893: in build

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:703: in reraise

raise value

merlin/models/tf/models/base.py:885: in build

layer.build(input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/tabular.py:313: in build

output_shapes = self.compute_call_output_shape(output_shapes)

merlin/models/tf/core/tabular.py:582: in compute_call_output_shape

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582: in 

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build SequentialBlock(

E         (layers): List(

E           (0): ParallelBlock(

E             (parallel_layers): Dict(

E               (categorical): ParallelBlock(

E                 (parallel_layers): Dict(

E                   (item_id): EmbeddingTable(

E                     (features): Dict(

E                       (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ITEM: 'item'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[ 0.00095159, -0.00780176, -0.01070255, ..., -0.0245008 ,

E                               -0.00115737, -0.02886876],

E                              [-0.012412  ,  0.0399057 ,  0.04028242, ..., -0.04560817,

E                                0.00926073, -0.00812765],

E                              [-0.0196968 ,  0.025419  ,  0.00797405, ...,  0.04023785,

E                               -0.0137983 , -0.03792955],

E                              ...,

E                              [ 0.00676475,  0.00952966, -0.01445646, ..., -0.00510135,

E                               -0.04126855, -0.04733052],

E                              [-0.02780222, -0.03965948,  0.04582984, ..., -0.02401545,

E                               -0.04722666,  0.02736635],

E                              [-0.02108997,  0.00042298,  0.04603405, ..., -0.03562424,

E                                0.01985692,  0.0199568 ]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                   (item_category): EmbeddingTable(

E                     (features): Dict(

E                       (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                       array([[ 0.03682753, -0.00775306,  0.00989783, ...,  0.04243133,

E                                0.02013233,  0.0142022 ],

E                              [ 0.03489197, -0.00297128, -0.02943789, ...,  0.02520556,

E                                0.04500607, -0.03690022],

E                              [-0.00748165, -0.01145886,  0.02205738, ...,  0.026886  ,

E                               -0.00793109,  0.03001534],

E                              ...,

E                              [ 0.03647201, -0.02389548,  0.03249105, ..., -0.04324914,

E                                0.02238042, -0.01105363],

E                              [-0.0121203 , -0.02577748, -0.03620006, ...,  0.01091584,

E                               -0.0377016 ,  0.04898537],

E                              [-0.03664935,  0.01268804,  0.03041216, ..., -0.04956597,

E                                0.00527128, -0.00412567]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                   (user_id): EmbeddingTable(

E                     (features): Dict(

E                       (user_id): ColumnSchema(name='user_id', tags={<Tags.USER: 'user'>, <Tags.CATEGORICAL: 'categorical'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.03058578, -0.00037177, -0.04440222, ..., -0.0143258 ,

E                                0.0078275 , -0.02509269],

E                              [ 0.00060018,  0.02608771,  0.04238523, ...,  0.01086897,

E                                0.02085884,  0.01716653],

E                              [-0.02398338, -0.0076279 , -0.0233204 , ..., -0.02491845,

E                               -0.01284405,  0.04893791],

E                              ...,

E                              [ 0.00118636, -0.02557309,  0.04411072, ...,  0.01656295,

E                               -0.00796812, -0.03289662],

E                              [ 0.02188356, -0.01895338, -0.01765849, ..., -0.038915  ,

E                               -0.02888047,  0.01209231],

E                              [ 0.01988271,  0.04559529, -0.0447374 , ...,  0.0016938 ,

E                               -0.01167984, -0.00597036]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E           (1): Filter(

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       ), did you forget to add aggregation to Filter(

E         (feature_names): List(

E           (0): 'item_id'

E           (1): 'item_category'

E           (2): 'user_id'

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError

=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/outputs/test_contrastive.py: 2 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filewsv8v6ax.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:566: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

===== 4 failed, 680 passed, 11 skipped, 1045 warnings in 975.70s (0:16:15) =====

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins9793061876429141845.sh

…oding is called with sparse=False for CI machine)

…e hot encoding representation of categ features)

… support), fixing bugs and adding tests to FMBlock

nvidia-merlin-bot · 2022-09-20T20:24:36Z

Click to view CI Results

GitHub pull request #717 of commit db03880e65b5ddd7714c516cb06d6e797fae062c, no merge conflicts.
Running as SYSTEM
Setting status of db03880e65b5ddd7714c516cb06d6e797fae062c to PENDING with url https://10.20.13.93:8080/job/merlin_models/1307/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse db03880e65b5ddd7714c516cb06d6e797fae062c^{commit} # timeout=10
Checking out Revision db03880e65b5ddd7714c516cb06d6e797fae062c (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f db03880e65b5ddd7714c516cb06d6e797fae062c # timeout=10
Commit message: "Fixed formating issue"
 > git rev-list --no-walk d6d33101186fd7c07ba304b1d944d49dcc8dbffc # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins7522390991320777298.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (2.8.2)
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Requirement already satisfied: tornado>=6.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: zipp>=3.1.0; python_version < "3.10" in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0; python_version < "3.9"->jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 695 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  8%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py ...                            [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 26%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 30%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 31%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 31%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 31%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 32%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 32%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 32%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 32%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 32%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 32%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 33%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 38%]

.....                                                                    [ 38%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 41%]

tests/unit/tf/layers/test_queue.py ..............                        [ 43%]

tests/unit/tf/losses/test_losses.py .......................              [ 46%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 47%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 50%]

tests/unit/tf/models/test_base.py s.................                     [ 53%]

tests/unit/tf/models/test_benchmark.py ..                                [ 53%]

tests/unit/tf/models/test_ranking.py ..............FFFF................  [ 58%]

tests/unit/tf/models/test_retrieval.py ................................  [ 63%]

tests/unit/tf/outputs/test_base.py .....                                 [ 64%]

tests/unit/tf/outputs/test_classification.py ......                      [ 64%]

tests/unit/tf/outputs/test_contrastive.py .........                      [ 66%]

tests/unit/tf/outputs/test_regression.py ..                              [ 66%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 67%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 67%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 69%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 70%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 71%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 71%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 72%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 72%]

tests/unit/tf/transforms/test_features.py s............................. [ 76%]

.................                                                        [ 79%]

tests/unit/tf/transforms/test_negative_sampling.py ..........            [ 80%]

tests/unit/tf/transforms/test_noise.py .....                             [ 81%]

tests/unit/tf/transforms/test_tensor.py ..                               [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 82%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 84%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 85%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 91%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=================================== FAILURES ===================================

___________________ test_deepfm_model_only_categ_feats[True] ___________________
self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fdcf1748860>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: argument of type 'Tags' is not iterable
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
self = SequentialBlock(

(layers): List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): '... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): 'item_category'

(2): 'user_id'

..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build(self, input_shape=None):
    """Builds the sequential block

    Parameters
    ----------
    input_shape : tf.TensorShape, optional
        The input shape, by default None
    """
    self._maybe_propagate_context(input_shape)


  build_sequentially(self, self.layers, input_shape)


merlin/models/tf/core/combinators.py:129:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:
            layer.build(input_shape)
        except TypeError:
            t, v, tb = sys.exc_info()
            if isinstance(input_shape, dict) and isinstance(last_layer, TabularBlock):
                v = TypeError(
                    f"Couldn't build {layer}, "
                    f"did you forget to add aggregation to {last_layer}?"
                )


          six.reraise(t, v, tb)


merlin/models/tf/core/combinators.py:780:

tp = <class 'TypeError'>, value = None, tb = None
def reraise(tp, value, tb=None):
    try:
        if value is None:
            value = tp()
        if value.__traceback__ is not tb:


          raise value.with_traceback(tb)


../../../.local/lib/python3.8/site-packages/six.py:702:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fdcf1748860>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build Filter(

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       ), did you forget to add aggregation to ParallelBlock(

E         (parallel_layers): Dict(

E           (categorical): ParallelBlock(

E             (parallel_layers): Dict(

E               (item_id): EmbeddingTable(

E                 (features): Dict(

E                   (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.02570032, -0.03179889, -0.03721094, ...,  0.01357832,

E                           -0.02899024,  0.01453921],

E                          [-0.04399979, -0.00753646, -0.00720472, ...,  0.00024997,

E                            0.01565896, -0.02869811],

E                          [ 0.02614701, -0.03707584, -0.02421354, ...,  0.02661935,

E                            0.04587359, -0.00313843],

E                          ...,

E                          [-0.01801817,  0.04142593,  0.02776836, ..., -0.01339476,

E                           -0.04107499, -0.0064096 ],

E                          [-0.04987758, -0.03873288, -0.030273  , ..., -0.01512405,

E                           -0.01584437, -0.02466037],

E                          [-0.0137626 ,  0.04578525, -0.02953109, ..., -0.01456221,

E                           -0.00472139,  0.0140526 ]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E               (item_category): EmbeddingTable(

E                 (features): Dict(

E                   (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                   array([[-0.00171213, -0.01755921,  0.02857446, ...,  0.02301586,

E                           -0.01788441,  0.03623377],

E                          [ 0.00862727, -0.03390821,  0.00515376, ..., -0.01194553,

E                            0.03372042, -0.0090534 ],

E                          [ 0.00476586,  0.0073352 , -0.02046644, ..., -0.00370144,

E                            0.00360762, -0.00312662],

E                          ...,

E                          [-0.00357788,  0.03606515,  0.01531923, ..., -0.01783171,

E                           -0.04155123,  0.01742039],

E                          [ 0.0191812 ,  0.04684928, -0.00022608, ...,  0.02704518,

E                            0.01896758, -0.03436905],

E                          [-0.01931492,  0.0356341 , -0.0218243 , ..., -0.00832837,

E                           -0.03270398, -0.00795592]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E               (user_id): EmbeddingTable(

E                 (features): Dict(

E                   (user_id): ColumnSchema(name='user_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.USER: 'user'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[ 0.03487933,  0.01901175, -0.0284261 , ..., -0.01840165,

E                            0.01250464, -0.00717173],

E                          [ 0.01682284,  0.03735187,  0.01156843, ..., -0.00881452,

E                           -0.02928144,  0.00875034],

E                          [-0.00974071, -0.0131126 ,  0.03639458, ...,  0.00978114,

E                           -0.01984922, -0.04836261],

E                          ...,

E                          [-0.02130981, -0.0425184 ,  0.03600109, ...,  0.0156843 ,

E                           -0.01499469,  0.018743  ],

E                          [ 0.01336529,  0.0117893 ,  0.02674155, ..., -0.03907121,

E                           -0.04320374,  0.00016158],

E                          [-0.02595156, -0.02762016, -0.00011653, ...,  0.01858344,

E                           -0.03044537, -0.03146877]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7fdcf1f669a0>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_only_categ_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:171:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:717: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1007: in call

self._maybe_build(inputs)

merlin/models/tf/models/base.py:867: in _maybe_build

super()._maybe_build(inputs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:2759: in _maybe_build

self.build(input_shapes)  # pylint:disable=not-callable

merlin/models/tf/models/base.py:893: in build

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:703: in reraise

raise value

merlin/models/tf/models/base.py:885: in build

layer.build(input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/tabular.py:313: in build

output_shapes = self.compute_call_output_shape(output_shapes)

merlin/models/tf/core/tabular.py:582: in compute_call_output_shape

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582: in 

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build SequentialBlock(

E         (layers): List(

E           (0): ParallelBlock(

E             (parallel_layers): Dict(

E               (categorical): ParallelBlock(

E                 (parallel_layers): Dict(

E                   (item_id): EmbeddingTable(

E                     (features): Dict(

E                       (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.02570032, -0.03179889, -0.03721094, ...,  0.01357832,

E                               -0.02899024,  0.01453921],

E                              [-0.04399979, -0.00753646, -0.00720472, ...,  0.00024997,

E                                0.01565896, -0.02869811],

E                              [ 0.02614701, -0.03707584, -0.02421354, ...,  0.02661935,

E                                0.04587359, -0.00313843],

E                              ...,

E                              [-0.01801817,  0.04142593,  0.02776836, ..., -0.01339476,

E                               -0.04107499, -0.0064096 ],

E                              [-0.04987758, -0.03873288, -0.030273  , ..., -0.01512405,

E                               -0.01584437, -0.02466037],

E                              [-0.0137626 ,  0.04578525, -0.02953109, ..., -0.01456221,

E                               -0.00472139,  0.0140526 ]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                   (item_category): EmbeddingTable(

E                     (features): Dict(

E                       (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                       array([[-0.00171213, -0.01755921,  0.02857446, ...,  0.02301586,

E                               -0.01788441,  0.03623377],

E                              [ 0.00862727, -0.03390821,  0.00515376, ..., -0.01194553,

E                                0.03372042, -0.0090534 ],

E                              [ 0.00476586,  0.0073352 , -0.02046644, ..., -0.00370144,

E                                0.00360762, -0.00312662],

E                              ...,

E                              [-0.00357788,  0.03606515,  0.01531923, ..., -0.01783171,

E                               -0.04155123,  0.01742039],

E                              [ 0.0191812 ,  0.04684928, -0.00022608, ...,  0.02704518,

E                                0.01896758, -0.03436905],

E                              [-0.01931492,  0.0356341 , -0.0218243 , ..., -0.00832837,

E                               -0.03270398, -0.00795592]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                   (user_id): EmbeddingTable(

E                     (features): Dict(

E                       (user_id): ColumnSchema(name='user_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.USER: 'user'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[ 0.03487933,  0.01901175, -0.0284261 , ..., -0.01840165,

E                                0.01250464, -0.00717173],

E                              [ 0.01682284,  0.03735187,  0.01156843, ..., -0.00881452,

E                               -0.02928144,  0.00875034],

E                              [-0.00974071, -0.0131126 ,  0.03639458, ...,  0.00978114,

E                               -0.01984922, -0.04836261],

E                              ...,

E                              [-0.02130981, -0.0425184 ,  0.03600109, ...,  0.0156843 ,

E                               -0.01499469,  0.018743  ],

E                              [ 0.01336529,  0.0117893 ,  0.02674155, ..., -0.03907121,

E                               -0.04320374,  0.00016158],

E                              [-0.02595156, -0.02762016, -0.00011653, ...,  0.01858344,

E                               -0.03044537, -0.03146877]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E           (1): Filter(

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       ), did you forget to add aggregation to Filter(

E         (feature_names): List(

E           (0): 'item_id'

E           (1): 'item_category'

E           (2): 'user_id'

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError

__________________ test_deepfm_model_only_categ_feats[False] ___________________
self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fdcdb739770>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: argument of type 'Tags' is not iterable
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
self = SequentialBlock(

(layers): List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): '... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): 'item_category'

(2): 'user_id'

..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build(self, input_shape=None):
    """Builds the sequential block

    Parameters
    ----------
    input_shape : tf.TensorShape, optional
        The input shape, by default None
    """
    self._maybe_propagate_context(input_shape)


  build_sequentially(self, self.layers, input_shape)


merlin/models/tf/core/combinators.py:129:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:
            layer.build(input_shape)
        except TypeError:
            t, v, tb = sys.exc_info()
            if isinstance(input_shape, dict) and isinstance(last_layer, TabularBlock):
                v = TypeError(
                    f"Couldn't build {layer}, "
                    f"did you forget to add aggregation to {last_layer}?"
                )


          six.reraise(t, v, tb)


merlin/models/tf/core/combinators.py:780:

tp = <class 'TypeError'>, value = None, tb = None
def reraise(tp, value, tb=None):
    try:
        if value is None:
            value = tp()
        if value.__traceback__ is not tb:


          raise value.with_traceback(tb)


../../../.local/lib/python3.8/site-packages/six.py:702:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)..._feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fdcdb739770>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build Filter(

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       ), did you forget to add aggregation to ParallelBlock(

E         (parallel_layers): Dict(

E           (categorical): ParallelBlock(

E             (parallel_layers): Dict(

E               (item_id): EmbeddingTable(

E                 (features): Dict(

E                   (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.03019184, -0.02111769,  0.01050959, ..., -0.0172928 ,

E                            0.00180059, -0.045084  ],

E                          [-0.02480277, -0.01521372,  0.02216304, ..., -0.04987577,

E                           -0.00641552, -0.02169349],

E                          [-0.02918756, -0.02672333, -0.02868438, ..., -0.02571416,

E                            0.03517436, -0.00967953],

E                          ...,

E                          [ 0.03259308, -0.02479436, -0.00282454, ..., -0.02942575,

E                           -0.01611086, -0.04680791],

E                          [-0.01399783, -0.01902272,  0.0045493 , ..., -0.04091265,

E                            0.02443952, -0.0302704 ],

E                          [ 0.00040155,  0.01888806,  0.01079621, ..., -0.03706405,

E                           -0.0367962 ,  0.01322072]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E               (item_category): EmbeddingTable(

E                 (features): Dict(

E                   (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                   array([[ 0.03009479,  0.03900156, -0.02817373, ...,  0.00038097,

E                            0.03044956, -0.04745512],

E                          [-0.03989201, -0.0167386 ,  0.00648766, ..., -0.02095594,

E                           -0.00021695,  0.03996665],

E                          [ 0.03626612,  0.04377914,  0.03230296, ..., -0.03567819,

E                           -0.03715755,  0.0365119 ],

E                          ...,

E                          [ 0.04226034,  0.03046766, -0.04382597, ..., -0.02501919,

E                           -0.01451756,  0.02671708],

E                          [-0.04766364, -0.03410469,  0.0293314 , ...,  0.03606859,

E                           -0.0374441 , -0.03671223],

E                          [-0.02736839, -0.02183306, -0.04594931, ..., -0.01534285,

E                            0.00578789, -0.00043597]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E               (user_id): EmbeddingTable(

E                 (features): Dict(

E                   (user_id): ColumnSchema(name='user_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.USER: 'user'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.02361941, -0.03001617,  0.01258189, ...,  0.03452733,

E                           -0.00601999, -0.04937288],

E                          [-0.02313321,  0.01118822,  0.02567822, ...,  0.00511445,

E                           -0.026209  ,  0.00258926],

E                          [ 0.02692063, -0.02748579, -0.02021652, ...,  0.03992691,

E                           -0.00202364, -0.03304167],

E                          ...,

E                          [-0.03796924,  0.03143172,  0.02334212, ..., -0.02866315,

E                            0.0450588 , -0.01824418],

E                          [-0.02232841,  0.00791576, -0.02536985, ...,  0.0101485 ,

E                           -0.00764833,  0.04040624],

E                          [-0.02146883, -0.00375839, -0.02736113, ..., -0.03641828,

E                            0.04995141,  0.02966121]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7fdcd99d1c40>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_only_categ_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:171:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:717: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1007: in call

self._maybe_build(inputs)

merlin/models/tf/models/base.py:867: in _maybe_build

super()._maybe_build(inputs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:2759: in _maybe_build

self.build(input_shapes)  # pylint:disable=not-callable

merlin/models/tf/models/base.py:893: in build

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:703: in reraise

raise value

merlin/models/tf/models/base.py:885: in build

layer.build(input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/tabular.py:313: in build

output_shapes = self.compute_call_output_shape(output_shapes)

merlin/models/tf/core/tabular.py:582: in compute_call_output_shape

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582: in 

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(... 1])

)

(_feature_dtypes): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build SequentialBlock(

E         (layers): List(

E           (0): ParallelBlock(

E             (parallel_layers): Dict(

E               (categorical): ParallelBlock(

E                 (parallel_layers): Dict(

E                   (item_id): EmbeddingTable(

E                     (features): Dict(

E                       (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.03019184, -0.02111769,  0.01050959, ..., -0.0172928 ,

E                                0.00180059, -0.045084  ],

E                              [-0.02480277, -0.01521372,  0.02216304, ..., -0.04987577,

E                               -0.00641552, -0.02169349],

E                              [-0.02918756, -0.02672333, -0.02868438, ..., -0.02571416,

E                                0.03517436, -0.00967953],

E                              ...,

E                              [ 0.03259308, -0.02479436, -0.00282454, ..., -0.02942575,

E                               -0.01611086, -0.04680791],

E                              [-0.01399783, -0.01902272,  0.0045493 , ..., -0.04091265,

E                                0.02443952, -0.0302704 ],

E                              [ 0.00040155,  0.01888806,  0.01079621, ..., -0.03706405,

E                               -0.0367962 ,  0.01322072]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                   (item_category): EmbeddingTable(

E                     (features): Dict(

E                       (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                       array([[ 0.03009479,  0.03900156, -0.02817373, ...,  0.00038097,

E                                0.03044956, -0.04745512],

E                              [-0.03989201, -0.0167386 ,  0.00648766, ..., -0.02095594,

E                               -0.00021695,  0.03996665],

E                              [ 0.03626612,  0.04377914,  0.03230296, ..., -0.03567819,

E                               -0.03715755,  0.0365119 ],

E                              ...,

E                              [ 0.04226034,  0.03046766, -0.04382597, ..., -0.02501919,

E                               -0.01451756,  0.02671708],

E                              [-0.04766364, -0.03410469,  0.0293314 , ...,  0.03606859,

E                               -0.0374441 , -0.03671223],

E                              [-0.02736839, -0.02183306, -0.04594931, ..., -0.01534285,

E                                0.00578789, -0.00043597]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                   (user_id): EmbeddingTable(

E                     (features): Dict(

E                       (user_id): ColumnSchema(name='user_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.USER: 'user'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.02361941, -0.03001617,  0.01258189, ...,  0.03452733,

E                               -0.00601999, -0.04937288],

E                              [-0.02313321,  0.01118822,  0.02567822, ...,  0.00511445,

E                               -0.026209  ,  0.00258926],

E                              [ 0.02692063, -0.02748579, -0.02021652, ...,  0.03992691,

E                               -0.00202364, -0.03304167],

E                              ...,

E                              [-0.03796924,  0.03143172,  0.02334212, ..., -0.02866315,

E                                0.0450588 , -0.01824418],

E                              [-0.02232841,  0.00791576, -0.02536985, ...,  0.0101485 ,

E                               -0.00764833,  0.04040624],

E                              [-0.02146883, -0.00375839, -0.02736113, ..., -0.03641828,

E                                0.04995141,  0.02966121]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                     )

E                   )

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E           (1): Filter(

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       ), did you forget to add aggregation to Filter(

E         (feature_names): List(

E           (0): 'item_id'

E           (1): 'item_category'

E           (2): 'user_id'

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError

______________ test_deepfm_model_categ_and_continuous_feats[True] ______________
self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fdcf23be3b0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: argument of type 'Tags' is not iterable
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
self = SequentialBlock(

(layers): List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): '...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): 'item_category'

(2): 'user_id'

...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build(self, input_shape=None):
    """Builds the sequential block

    Parameters
    ----------
    input_shape : tf.TensorShape, optional
        The input shape, by default None
    """
    self._maybe_propagate_context(input_shape)


  build_sequentially(self, self.layers, input_shape)


merlin/models/tf/core/combinators.py:129:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:
            layer.build(input_shape)
        except TypeError:
            t, v, tb = sys.exc_info()
            if isinstance(input_shape, dict) and isinstance(last_layer, TabularBlock):
                v = TypeError(
                    f"Couldn't build {layer}, "
                    f"did you forget to add aggregation to {last_layer}?"
                )


          six.reraise(t, v, tb)


merlin/models/tf/core/combinators.py:780:

tp = <class 'TypeError'>, value = None, tb = None
def reraise(tp, value, tb=None):
    try:
        if value is None:
            value = tp()
        if value.__traceback__ is not tb:


          raise value.with_traceback(tb)


../../../.local/lib/python3.8/site-packages/six.py:702:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fdcf23be3b0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build Filter(

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       ), did you forget to add aggregation to ParallelBlock(

E         (parallel_layers): Dict(

E           (categorical): ParallelBlock(

E             (parallel_layers): Dict(

E               (item_id): EmbeddingTable(

E                 (features): Dict(

E                   (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.02054526, -0.02200582,  0.01605621, ...,  0.02267637,

E                            0.01410104, -0.00604937],

E                          [-0.01742599,  0.04021536, -0.02411727, ...,  0.01386498,

E                            0.03677467,  0.01996145],

E                          [ 0.00269372, -0.03342003, -0.04908162, ...,  0.04889261,

E                            0.01890564,  0.03716913],

E                          ...,

E                          [-0.03502411, -0.03122339,  0.03508973, ..., -0.03769619,

E                           -0.03154981, -0.01263622],

E                          [-0.04772219,  0.01338226,  0.00763812, ..., -0.00482909,

E                           -0.0085258 ,  0.00261904],

E                          [-0.01138081, -0.04893042,  0.00227406, ...,  0.00839734,

E                           -0.04513972,  0.00374116]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E               (item_category): EmbeddingTable(

E                 (features): Dict(

E                   (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                   array([[-0.01045848,  0.04803827, -0.01155933, ..., -0.04183086,

E                            0.04058427, -0.01171237],

E                          [ 0.04373075,  0.00500919,  0.04929531, ..., -0.03939838,

E                            0.00203785, -0.00989903],

E                          [ 0.04189702, -0.03120966, -0.00791519, ...,  0.00858233,

E                            0.01543109,  0.01549872],

E                          ...,

E                          [ 0.00184605,  0.0434628 , -0.02273744, ...,  0.04810428,

E                           -0.02350572, -0.00790094],

E                          [ 0.04012373, -0.03322871,  0.0497464 , ..., -0.01007406,

E                           -0.00045862,  0.00152151],

E                          [-0.02862725, -0.01967434, -0.00228672, ...,  0.00557036,

E                           -0.03056601, -0.03539287]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E               (user_id): EmbeddingTable(

E                 (features): Dict(

E                   (user_id): ColumnSchema(name='user_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.USER: 'user'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[ 0.00854038, -0.00232061,  0.00140734, ...,  0.04226798,

E                           -0.03381544, -0.02190081],

E                          [ 0.03141161,  0.01365862, -0.01205138, ...,  0.0265334 ,

E                           -0.03197428,  0.03824944],

E                          [-0.03385307, -0.0046339 ,  0.01335522, ...,  0.04823765,

E                            0.04733351,  0.02277596],

E                          ...,

E                          [-0.04601424,  0.01994986,  0.04879292, ..., -0.00574926,

E                            0.04242582, -0.00810363],

E                          [ 0.01967518, -0.03233056, -0.00338495, ...,  0.04127854,

E                           -0.02612364,  0.03764292],

E                          [-0.0034511 ,  0.04024262,  0.02186977, ...,  0.04592418,

E                           -0.00961372,  0.02344227]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7fdcf197c670>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_categ_and_continuous_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "user_age", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:186:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:717: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1007: in call

self._maybe_build(inputs)

merlin/models/tf/models/base.py:867: in _maybe_build

super()._maybe_build(inputs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:2759: in _maybe_build

self.build(input_shapes)  # pylint:disable=not-callable

merlin/models/tf/models/base.py:893: in build

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:703: in reraise

raise value

merlin/models/tf/models/base.py:885: in build

layer.build(input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/tabular.py:313: in build

output_shapes = self.compute_call_output_shape(output_shapes)

merlin/models/tf/core/tabular.py:582: in compute_call_output_shape

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582: in 

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build SequentialBlock(

E         (layers): List(

E           (0): ParallelBlock(

E             (parallel_layers): Dict(

E               (categorical): ParallelBlock(

E                 (parallel_layers): Dict(

E                   (item_id): EmbeddingTable(

E                     (features): Dict(

E                       (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.02054526, -0.02200582,  0.01605621, ...,  0.02267637,

E                                0.01410104, -0.00604937],

E                              [-0.01742599,  0.04021536, -0.02411727, ...,  0.01386498,

E                                0.03677467,  0.01996145],

E                              [ 0.00269372, -0.03342003, -0.04908162, ...,  0.04889261,

E                                0.01890564,  0.03716913],

E                              ...,

E                              [-0.03502411, -0.03122339,  0.03508973, ..., -0.03769619,

E                               -0.03154981, -0.01263622],

E                              [-0.04772219,  0.01338226,  0.00763812, ..., -0.00482909,

E                               -0.0085258 ,  0.00261904],

E                              [-0.01138081, -0.04893042,  0.00227406, ...,  0.00839734,

E                               -0.04513972,  0.00374116]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                   (item_category): EmbeddingTable(

E                     (features): Dict(

E                       (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                       array([[-0.01045848,  0.04803827, -0.01155933, ..., -0.04183086,

E                                0.04058427, -0.01171237],

E                              [ 0.04373075,  0.00500919,  0.04929531, ..., -0.03939838,

E                                0.00203785, -0.00989903],

E                              [ 0.04189702, -0.03120966, -0.00791519, ...,  0.00858233,

E                                0.01543109,  0.01549872],

E                              ...,

E                              [ 0.00184605,  0.0434628 , -0.02273744, ...,  0.04810428,

E                               -0.02350572, -0.00790094],

E                              [ 0.04012373, -0.03322871,  0.0497464 , ..., -0.01007406,

E                               -0.00045862,  0.00152151],

E                              [-0.02862725, -0.01967434, -0.00228672, ...,  0.00557036,

E                               -0.03056601, -0.03539287]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                   (user_id): EmbeddingTable(

E                     (features): Dict(

E                       (user_id): ColumnSchema(name='user_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.USER: 'user'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[ 0.00854038, -0.00232061,  0.00140734, ...,  0.04226798,

E                               -0.03381544, -0.02190081],

E                              [ 0.03141161,  0.01365862, -0.01205138, ...,  0.0265334 ,

E                               -0.03197428,  0.03824944],

E                              [-0.03385307, -0.0046339 ,  0.01335522, ...,  0.04823765,

E                                0.04733351,  0.02277596],

E                              ...,

E                              [-0.04601424,  0.01994986,  0.04879292, ..., -0.00574926,

E                                0.04242582, -0.00810363],

E                              [ 0.01967518, -0.03233056, -0.00338495, ...,  0.04127854,

E                               -0.02612364,  0.03764292],

E                              [-0.0034511 ,  0.04024262,  0.02186977, ...,  0.04592418,

E                               -0.00961372,  0.02344227]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E           (1): Filter(

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       ), did you forget to add aggregation to Filter(

E         (feature_names): List(

E           (0): 'item_id'

E           (1): 'item_category'

E           (2): 'user_id'

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError

_____________ test_deepfm_model_categ_and_continuous_feats[False] ______________
self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fdcf18da1d0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: argument of type 'Tags' is not iterable
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
self = SequentialBlock(

(layers): List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): '...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): Filter(

(feature_names): List(

(0): 'item_id'

(1): 'item_category'

(2): 'user_id'

...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 1]), 'item_id': TensorShape([50, 1]), 'user_id': TensorShape([50, 1])}
def build(self, input_shape=None):
    """Builds the sequential block

    Parameters
    ----------
    input_shape : tf.TensorShape, optional
        The input shape, by default None
    """
    self._maybe_propagate_context(input_shape)


  build_sequentially(self, self.layers, input_shape)


merlin/models/tf/core/combinators.py:129:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:
            layer.build(input_shape)
        except TypeError:
            t, v, tb = sys.exc_info()
            if isinstance(input_shape, dict) and isinstance(last_layer, TabularBlock):
                v = TypeError(
                    f"Couldn't build {layer}, "
                    f"did you forget to add aggregation to {last_layer}?"
                )


          six.reraise(t, v, tb)


merlin/models/tf/core/combinators.py:780:

tp = <class 'TypeError'>, value = None, tb = None
def reraise(tp, value, tb=None):
    try:
        if value is None:
            value = tp()
        if value.__traceback__ is not tb:


          raise value.with_traceback(tb)


../../../.local/lib/python3.8/site-packages/six.py:702:

self = SequentialBlock(

(layers): List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): Paral...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

layers = List(

(0): ParallelBlock(

(parallel_layers): Dict(

(categorical): ParallelBlock(

(parallel_layers)...   (item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build_sequentially(self, layers, input_shape):
    """Build layers sequentially."""
    last_layer = None
    for layer in layers:
        try:


          layer.build(input_shape)


merlin/models/tf/core/combinators.py:772:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shapes = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def build(self, input_shapes):
    super().build(input_shapes)
    output_shapes = input_shapes
    if self.pre:
        self.pre.build(input_shapes)
        output_shapes = self.pre.compute_output_shape(input_shapes)


  output_shapes = self.compute_call_output_shape(output_shapes)


merlin/models/tf/core/tabular.py:313:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

input_shape = {'item_category': TensorShape([50, 16]), 'item_id': TensorShape([50, 16]), 'user_id': TensorShape([50, 16])}
def compute_call_output_shape(self, input_shape):
    if self.add_to_context:
        return {}


  outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}


merlin/models/tf/core/tabular.py:582:

.0 = <dict_itemiterator object at 0x7fdcf18da1d0>

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582:

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build Filter(

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       ), did you forget to add aggregation to ParallelBlock(

E         (parallel_layers): Dict(

E           (categorical): ParallelBlock(

E             (parallel_layers): Dict(

E               (item_id): EmbeddingTable(

E                 (features): Dict(

E                   (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[ 0.00095159, -0.00780176, -0.01070255, ..., -0.0245008 ,

E                           -0.00115737, -0.02886876],

E                          [-0.012412  ,  0.0399057 ,  0.04028242, ..., -0.04560817,

E                            0.00926073, -0.00812765],

E                          [-0.0196968 ,  0.025419  ,  0.00797405, ...,  0.04023785,

E                           -0.0137983 , -0.03792955],

E                          ...,

E                          [ 0.00676475,  0.00952966, -0.01445646, ..., -0.00510135,

E                           -0.04126855, -0.04733052],

E                          [-0.02780222, -0.03965948,  0.04582984, ..., -0.02401545,

E                           -0.04722666,  0.02736635],

E                          [-0.02108997,  0.00042298,  0.04603405, ..., -0.03562424,

E                            0.01985692,  0.0199568 ]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E               (item_category): EmbeddingTable(

E                 (features): Dict(

E                   (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                   array([[ 0.03682753, -0.00775306,  0.00989783, ...,  0.04243133,

E                            0.02013233,  0.0142022 ],

E                          [ 0.03489197, -0.00297128, -0.02943789, ...,  0.02520556,

E                            0.04500607, -0.03690022],

E                          [-0.00748165, -0.01145886,  0.02205738, ...,  0.026886  ,

E                           -0.00793109,  0.03001534],

E                          ...,

E                          [ 0.03647201, -0.02389548,  0.03249105, ..., -0.04324914,

E                            0.02238042, -0.01105363],

E                          [-0.0121203 , -0.02577748, -0.03620006, ...,  0.01091584,

E                           -0.0377016 ,  0.04898537],

E                          [-0.03664935,  0.01268804,  0.03041216, ..., -0.04956597,

E                            0.00527128, -0.00412567]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E               (user_id): EmbeddingTable(

E                 (features): Dict(

E                   (user_id): ColumnSchema(name='user_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.USER: 'user'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                 )

E                 (table): Embedding(

E                   (_feature_shapes): Dict(

E                     (item_id): TensorShape([50, 1])

E                     (item_category): TensorShape([50, 1])

E                     (user_id): TensorShape([50, 1])

E                     (user_age): TensorShape([50, 1])

E                   )

E                   (_feature_dtypes): Dict(

E                     (item_id): tf.int64

E                     (item_category): tf.int64

E                     (user_id): tf.int64

E                     (user_age): tf.int64

E                   )

E                   (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                   array([[-0.03058578, -0.00037177, -0.04440222, ..., -0.0143258 ,

E                            0.0078275 , -0.02509269],

E                          [ 0.00060018,  0.02608771,  0.04238523, ...,  0.01086897,

E                            0.02085884,  0.01716653],

E                          [-0.02398338, -0.0076279 , -0.0233204 , ..., -0.02491845,

E                           -0.01284405,  0.04893791],

E                          ...,

E                          [ 0.00118636, -0.02557309,  0.04411072, ...,  0.01656295,

E                           -0.00796812, -0.03289662],

E                          [ 0.02188356, -0.01895338, -0.01765849, ..., -0.038915  ,

E                           -0.02888047,  0.01209231],

E                          [ 0.01988271,  0.04559529, -0.0447374 , ...,  0.0016938 ,

E                           -0.01167984, -0.00597036]], dtype=float32)>

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError
During handling of the above exception, another exception occurred:
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7fdce9ce9580>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_deepfm_model_categ_and_continuous_feats(music_streaming_data, run_eagerly):
    music_streaming_data.schema = music_streaming_data.schema.select_by_name(
        ["item_id", "item_category", "user_id", "user_age", "click"]
    )
    model = ml.DeepFMModel(
        music_streaming_data.schema,
        embedding_dim=16,
        deep_block=ml.MLPBlock([16]),
        prediction_tasks=ml.BinaryClassificationTask("click"),
    )


  testing_utils.model_test(model, music_streaming_data, run_eagerly=run_eagerly)


tests/unit/tf/models/test_ranking.py:186:

merlin/models/tf/utils/testing_utils.py:89: in model_test

losses = model.fit(dataset, batch_size=50, epochs=epochs, steps_per_epoch=1)

merlin/models/tf/models/base.py:717: in fit

return super().fit(**fit_kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:1358: in fit

data_handler = data_adapter.get_data_handler(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1401: in get_data_handler

return DataHandler(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:1151: in init

self._adapter = adapter_cls(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:926: in init

super(KerasSequenceAdapter, self).init(

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:808: in init

model.distribute_strategy.run(

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py:809: in 

lambda x: model(x, training=False), args=(concrete_x,))

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:1007: in call

self._maybe_build(inputs)

merlin/models/tf/models/base.py:867: in _maybe_build

super()._maybe_build(inputs)

/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py:2759: in _maybe_build

self.build(input_shapes)  # pylint:disable=not-callable

merlin/models/tf/models/base.py:893: in build

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:703: in reraise

raise value

merlin/models/tf/models/base.py:885: in build

layer.build(input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:521: in build

layer.build(layer_input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/combinators.py:129: in build

build_sequentially(self, self.layers, input_shape)

merlin/models/tf/core/combinators.py:780: in build_sequentially

six.reraise(t, v, tb)

../../../.local/lib/python3.8/site-packages/six.py:702: in reraise

raise value.with_traceback(tb)

merlin/models/tf/core/combinators.py:772: in build_sequentially

layer.build(input_shape)

merlin/models/tf/core/tabular.py:313: in build

output_shapes = self.compute_call_output_shape(output_shapes)

merlin/models/tf/core/tabular.py:582: in compute_call_output_shape

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

merlin/models/tf/core/tabular.py:582: in 

outputs = {k: v for k, v in input_shape.items() if self.check_feature(k)}

self = Filter(

(_feature_shapes): Dict(

(item_id): TensorShape([50, 1])

(item_category): TensorShape([50, 1])

(...es): Dict(

(item_id): tf.int64

(item_category): tf.int64

(user_id): tf.int64

(user_age): tf.int64

)

)

feature_name = 'item_id'
def check_feature(self, feature_name) -> bool:
    if self.exclude:
        return feature_name not in self.feature_names


  return feature_name in self.feature_names


E       TypeError: Couldn't build SequentialBlock(

E         (layers): List(

E           (0): ParallelBlock(

E             (parallel_layers): Dict(

E               (categorical): ParallelBlock(

E                 (parallel_layers): Dict(

E                   (item_id): EmbeddingTable(

E                     (features): Dict(

E                       (item_id): ColumnSchema(name='item_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>, <Tags.ITEM_ID: 'item_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[ 0.00095159, -0.00780176, -0.01070255, ..., -0.0245008 ,

E                               -0.00115737, -0.02886876],

E                              [-0.012412  ,  0.0399057 ,  0.04028242, ..., -0.04560817,

E                                0.00926073, -0.00812765],

E                              [-0.0196968 ,  0.025419  ,  0.00797405, ...,  0.04023785,

E                               -0.0137983 , -0.03792955],

E                              ...,

E                              [ 0.00676475,  0.00952966, -0.01445646, ..., -0.00510135,

E                               -0.04126855, -0.04733052],

E                              [-0.02780222, -0.03965948,  0.04582984, ..., -0.02401545,

E                               -0.04722666,  0.02736635],

E                              [-0.02108997,  0.00042298,  0.04603405, ..., -0.03562424,

E                                0.01985692,  0.0199568 ]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                   (item_category): EmbeddingTable(

E                     (features): Dict(

E                       (item_category): ColumnSchema(name='item_category', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.ITEM: 'item'>}, properties={'domain': {'min': 0, 'max': 100}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(101, 16) dtype=float32, numpy=

E                       array([[ 0.03682753, -0.00775306,  0.00989783, ...,  0.04243133,

E                                0.02013233,  0.0142022 ],

E                              [ 0.03489197, -0.00297128, -0.02943789, ...,  0.02520556,

E                                0.04500607, -0.03690022],

E                              [-0.00748165, -0.01145886,  0.02205738, ...,  0.026886  ,

E                               -0.00793109,  0.03001534],

E                              ...,

E                              [ 0.03647201, -0.02389548,  0.03249105, ..., -0.04324914,

E                                0.02238042, -0.01105363],

E                              [-0.0121203 , -0.02577748, -0.03620006, ...,  0.01091584,

E                               -0.0377016 ,  0.04898537],

E                              [-0.03664935,  0.01268804,  0.03041216, ..., -0.04956597,

E                                0.00527128, -0.00412567]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                   (user_id): EmbeddingTable(

E                     (features): Dict(

E                       (user_id): ColumnSchema(name='user_id', tags={<Tags.CATEGORICAL: 'categorical'>, <Tags.USER: 'user'>, <Tags.USER_ID: 'user_id'>, <Tags.ID: 'id'>}, properties={'domain': {'min': 0, 'max': 10000}}, dtype=dtype('int64'), is_list=False, is_ragged=False)

E                     )

E                     (table): Embedding(

E                       (_feature_shapes): Dict(

E                         (item_id): TensorShape([50, 1])

E                         (item_category): TensorShape([50, 1])

E                         (user_id): TensorShape([50, 1])

E                         (user_age): TensorShape([50, 1])

E                       )

E                       (_feature_dtypes): Dict(

E                         (item_id): tf.int64

E                         (item_category): tf.int64

E                         (user_id): tf.int64

E                         (user_age): tf.int64

E                       )

E                       (embeddings): <tf.Variable 'model/embeddings:0' shape=(10001, 16) dtype=float32, numpy=

E                       array([[-0.03058578, -0.00037177, -0.04440222, ..., -0.0143258 ,

E                                0.0078275 , -0.02509269],

E                              [ 0.00060018,  0.02608771,  0.04238523, ...,  0.01086897,

E                                0.02085884,  0.01716653],

E                              [-0.02398338, -0.0076279 , -0.0233204 , ..., -0.02491845,

E                               -0.01284405,  0.04893791],

E                              ...,

E                              [ 0.00118636, -0.02557309,  0.04411072, ...,  0.01656295,

E                               -0.00796812, -0.03289662],

E                              [ 0.02188356, -0.01895338, -0.01765849, ..., -0.038915  ,

E                               -0.02888047,  0.01209231],

E                              [ 0.01988271,  0.04559529, -0.0447374 , ...,  0.0016938 ,

E                               -0.01167984, -0.00597036]], dtype=float32)>

E                     )

E                     (_feature_shapes): Dict(

E                       (item_id): TensorShape([50, 1])

E                       (item_category): TensorShape([50, 1])

E                       (user_id): TensorShape([50, 1])

E                       (user_age): TensorShape([50, 1])

E                     )

E                     (_feature_dtypes): Dict(

E                       (item_id): tf.int64

E                       (item_category): tf.int64

E                       (user_id): tf.int64

E                       (user_age): tf.int64

E                     )

E                   )

E                 )

E                 (_feature_shapes): Dict(

E                   (item_id): TensorShape([50, 1])

E                   (item_category): TensorShape([50, 1])

E                   (user_id): TensorShape([50, 1])

E                   (user_age): TensorShape([50, 1])

E                 )

E                 (_feature_dtypes): Dict(

E                   (item_id): tf.int64

E                   (item_category): tf.int64

E                   (user_id): tf.int64

E                   (user_age): tf.int64

E                 )

E               )

E             )

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E           (1): Filter(

E             (_feature_shapes): Dict(

E               (item_id): TensorShape([50, 1])

E               (item_category): TensorShape([50, 1])

E               (user_id): TensorShape([50, 1])

E               (user_age): TensorShape([50, 1])

E             )

E             (_feature_dtypes): Dict(

E               (item_id): tf.int64

E               (item_category): tf.int64

E               (user_id): tf.int64

E               (user_age): tf.int64

E             )

E           )

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       ), did you forget to add aggregation to Filter(

E         (feature_names): List(

E           (0): 'item_id'

E           (1): 'item_category'

E           (2): 'user_id'

E         )

E         (_feature_shapes): Dict(

E           (item_id): TensorShape([50, 1])

E           (item_category): TensorShape([50, 1])

E           (user_id): TensorShape([50, 1])

E           (user_age): TensorShape([50, 1])

E         )

E         (_feature_dtypes): Dict(

E           (item_id): tf.int64

E           (item_category): tf.int64

E           (user_id): tf.int64

E           (user_age): tf.int64

E         )

E       )?
merlin/models/tf/core/tabular.py:590: TypeError

=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/outputs/test_contrastive.py: 2 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_file3wsfxz8d.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:566: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

===== 4 failed, 680 passed, 11 skipped, 1045 warnings in 971.87s (0:16:11) =====

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins3287177093624312041.sh

nvidia-merlin-bot · 2022-09-20T20:41:11Z

Click to view CI Results

GitHub pull request #717 of commit f75d96fefea201605f6b4cf6afeac4de9468e9df, no merge conflicts.
Running as SYSTEM
Setting status of f75d96fefea201605f6b4cf6afeac4de9468e9df to PENDING with url https://10.20.13.93:8080/job/merlin_models/1308/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse f75d96fefea201605f6b4cf6afeac4de9468e9df^{commit} # timeout=10
Checking out Revision f75d96fefea201605f6b4cf6afeac4de9468e9df (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f f75d96fefea201605f6b4cf6afeac4de9468e9df # timeout=10
Commit message: "Fixed failing test"
 > git rev-list --no-walk db03880e65b5ddd7714c516cb06d6e797fae062c # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins14711923962040376517.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 695 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  8%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py ...                            [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 26%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 30%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 31%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 31%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 31%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 32%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 32%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 32%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 32%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 32%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 32%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 33%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 38%]

.....                                                                    [ 38%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 41%]

tests/unit/tf/layers/test_queue.py ..............                        [ 43%]

tests/unit/tf/losses/test_losses.py .......................              [ 46%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 47%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 50%]

tests/unit/tf/models/test_base.py s.................                     [ 53%]

tests/unit/tf/models/test_benchmark.py ..                                [ 53%]

tests/unit/tf/models/test_ranking.py ..................................  [ 58%]

tests/unit/tf/models/test_retrieval.py ................................  [ 63%]

tests/unit/tf/outputs/test_base.py .....                                 [ 64%]

tests/unit/tf/outputs/test_classification.py ......                      [ 64%]

tests/unit/tf/outputs/test_contrastive.py .........                      [ 66%]

tests/unit/tf/outputs/test_regression.py ..                              [ 66%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 67%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 67%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 69%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 70%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 71%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 71%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 72%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 72%]

tests/unit/tf/transforms/test_features.py s............................. [ 76%]

.................                                                        [ 79%]

tests/unit/tf/transforms/test_negative_sampling.py ..........            [ 80%]

tests/unit/tf/transforms/test_noise.py .....                             [ 81%]

tests/unit/tf/transforms/test_tensor.py ..                               [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 82%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 84%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 85%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 91%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/outputs/test_contrastive.py: 2 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_fileul8i1u_t.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:566: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 684 passed, 11 skipped, 1047 warnings in 976.12s (0:16:16) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins2058080911259813257.sh

gabrielspmoreira · 2022-09-20T20:42:14Z

@marcromeyn I have rebased this PR and did the necessary changes so that it uses your updated InputBlockV2

nvidia-merlin-bot · 2022-09-20T21:14:14Z

Click to view CI Results

GitHub pull request #717 of commit 3cdb11def0615cbdfff096c01e428c9f27c7ce38, no merge conflicts.
Running as SYSTEM
Setting status of 3cdb11def0615cbdfff096c01e428c9f27c7ce38 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1310/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse 3cdb11def0615cbdfff096c01e428c9f27c7ce38^{commit} # timeout=10
Checking out Revision 3cdb11def0615cbdfff096c01e428c9f27c7ce38 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 3cdb11def0615cbdfff096c01e428c9f27c7ce38 # timeout=10
Commit message: "Merge branch 'main' into ranking_models_inputs"
 > git rev-list --no-walk 826bc3a35aac38f47e917f78087d3d15091f8f6a # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins15098126964348223522.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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Requirement already satisfied: pkgutil-resolve-name>=1.3.10; python_version < "3.9" in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (1.3.10)
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Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 695 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  8%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py ...                            [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 26%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 30%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 31%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 31%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 31%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 32%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 32%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 32%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 32%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 32%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 32%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 33%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 38%]

.....                                                                    [ 38%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 41%]

tests/unit/tf/layers/test_queue.py ..............                        [ 43%]

tests/unit/tf/losses/test_losses.py .......................              [ 46%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 47%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 50%]

tests/unit/tf/models/test_base.py s.................                     [ 53%]

tests/unit/tf/models/test_benchmark.py ..                                [ 53%]

tests/unit/tf/models/test_ranking.py ..................................  [ 58%]

tests/unit/tf/models/test_retrieval.py ................................  [ 63%]

tests/unit/tf/outputs/test_base.py .....                                 [ 64%]

tests/unit/tf/outputs/test_classification.py ......                      [ 64%]

tests/unit/tf/outputs/test_contrastive.py .........                      [ 66%]

tests/unit/tf/outputs/test_regression.py ..                              [ 66%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 67%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 67%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 69%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 70%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 71%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 71%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 72%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 72%]

tests/unit/tf/transforms/test_features.py s............................. [ 76%]

.................                                                        [ 79%]

tests/unit/tf/transforms/test_negative_sampling.py ..........            [ 80%]

tests/unit/tf/transforms/test_noise.py .....                             [ 81%]

tests/unit/tf/transforms/test_tensor.py ..                               [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 82%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 84%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 85%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 91%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/outputs/test_contrastive.py: 2 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filelplad2f0.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:566: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 684 passed, 11 skipped, 1047 warnings in 977.66s (0:16:17) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins15965917917765665375.sh

nvidia-merlin-bot · 2022-09-20T22:04:03Z

Click to view CI Results

GitHub pull request #717 of commit d6df124e55bf66a73f5476d63b42d4efb25bb2fb, no merge conflicts.
Running as SYSTEM
Setting status of d6df124e55bf66a73f5476d63b42d4efb25bb2fb to PENDING with url https://10.20.13.93:8080/job/merlin_models/1313/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/717/*:refs/remotes/origin/pr/717/* # timeout=10
 > git rev-parse d6df124e55bf66a73f5476d63b42d4efb25bb2fb^{commit} # timeout=10
Checking out Revision d6df124e55bf66a73f5476d63b42d4efb25bb2fb (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f d6df124e55bf66a73f5476d63b42d4efb25bb2fb # timeout=10
Commit message: "Making DeepFM output layer configurable"
 > git rev-list --no-walk 6e9de9672319b896089c94ce3a094a9c7edf8cdc # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins13169389965715311271.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 695 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss                              [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  4%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_dataset.py ................                           [  7%]

tests/unit/tf/test_public_api.py .                                       [  7%]

tests/unit/tf/blocks/test_cross.py ...........                           [  8%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [ 10%]

tests/unit/tf/blocks/test_interactions.py ...                            [ 10%]

tests/unit/tf/blocks/test_mlp.py .................................       [ 15%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

.....................                                                    [ 23%]

tests/unit/tf/blocks/retrieval/test_base.py .                            [ 23%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ...........             [ 25%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 25%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 25%]

tests/unit/tf/core/test_aggregation.py .........                         [ 26%]

tests/unit/tf/core/test_base.py ..                                       [ 27%]

tests/unit/tf/core/test_combinators.py s...................              [ 30%]

tests/unit/tf/core/test_encoder.py .                                     [ 30%]

tests/unit/tf/core/test_index.py ...                                     [ 30%]

tests/unit/tf/core/test_prediction.py ..                                 [ 30%]

tests/unit/tf/core/test_tabular.py ....                                  [ 31%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 31%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 31%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 31%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 32%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 32%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 32%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 32%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 32%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 32%]

tests/unit/tf/inputs/test_continuous.py .....                            [ 33%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 38%]

.....                                                                    [ 38%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 41%]

tests/unit/tf/layers/test_queue.py ..............                        [ 43%]

tests/unit/tf/losses/test_losses.py .......................              [ 46%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 47%]

tests/unit/tf/metrics/test_metrics_topk.py .......................       [ 50%]

tests/unit/tf/models/test_base.py s.................                     [ 53%]

tests/unit/tf/models/test_benchmark.py ..                                [ 53%]

tests/unit/tf/models/test_ranking.py ..................................  [ 58%]

tests/unit/tf/models/test_retrieval.py ................................  [ 63%]

tests/unit/tf/outputs/test_base.py .....                                 [ 64%]

tests/unit/tf/outputs/test_classification.py ......                      [ 64%]

tests/unit/tf/outputs/test_contrastive.py .........                      [ 66%]

tests/unit/tf/outputs/test_regression.py ..                              [ 66%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 67%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 67%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 69%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 70%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 71%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 71%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 72%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 72%]

tests/unit/tf/transforms/test_features.py s............................. [ 76%]

.................                                                        [ 79%]

tests/unit/tf/transforms/test_negative_sampling.py ..........            [ 80%]

tests/unit/tf/transforms/test_noise.py .....                             [ 81%]

tests/unit/tf/transforms/test_tensor.py ..                               [ 81%]

tests/unit/tf/utils/test_batch.py ....                                   [ 82%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 82%]

tests/unit/torch/test_dataset.py .........                               [ 84%]

tests/unit/torch/test_public_api.py .                                    [ 84%]

tests/unit/torch/block/test_base.py ....                                 [ 84%]

tests/unit/torch/block/test_mlp.py .                                     [ 85%]

tests/unit/torch/features/test_continuous.py ..                          [ 85%]

tests/unit/torch/features/test_embedding.py ..............               [ 87%]

tests/unit/torch/features/test_tabular.py ....                           [ 87%]

tests/unit/torch/model/test_head.py ............                         [ 89%]

tests/unit/torch/model/test_model.py ..                                  [ 89%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 91%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 91%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 92%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41

/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 60 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 26 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 19 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 17 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 32 warnings

tests/unit/tf/outputs/test_base.py: 5 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 9 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_dataset.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 10 warnings

tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 2 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 4 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/models/test_retrieval.py: 54 warnings

tests/unit/tf/outputs/test_contrastive.py: 2 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_fileecgyyjtz.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:566: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html

=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========== 684 passed, 11 skipped, 1047 warnings in 981.71s (0:16:21) ==========

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins17077715439902186611.sh

gabrielspmoreira requested a review from marcromeyn September 6, 2022 00:29

gabrielspmoreira self-assigned this Sep 6, 2022

gabrielspmoreira added area/api area/ranking chore Maintenance for the repository breaking Breaking change labels Sep 6, 2022

gabrielspmoreira added this to the Merlin 22.09 milestone Sep 6, 2022

gabrielspmoreira removed the breaking Breaking change label Sep 6, 2022

gabrielspmoreira commented Sep 6, 2022

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gabrielspmoreira force-pushed the ranking_models_inputs branch from e4c777c to 53d1ec9 Compare September 8, 2022 20:31

gabrielspmoreira changed the title ~~Added support to InputBlockV2 to DCNModel and DeepFMModel~~ Added support to InputBlockV2 to DCNModel and DeepFMModel (refactored and fixed) Sep 9, 2022

gabrielspmoreira requested review from oliverholworthy and sararb September 9, 2022 03:52

oliverholworthy reviewed Sep 9, 2022

View reviewed changes

gabrielspmoreira force-pushed the ranking_models_inputs branch from ee83e4f to d8454eb Compare September 9, 2022 18:33

oliverholworthy reviewed Sep 12, 2022

View reviewed changes

gabrielspmoreira changed the title ~~Added support to InputBlockV2 to DCNModel and DeepFMModel (refactored and fixed)~~ Added support to InputBlockV2 DeepFMModel (refactored and fixed) and DCNModel Sep 12, 2022

gabrielspmoreira changed the title ~~Added support to InputBlockV2 DeepFMModel (refactored and fixed) and DCNModel~~ Added InputBlockV2 support to DeepFMModel (refactored and fixed) and DCNModel Sep 12, 2022

marcromeyn requested changes Sep 13, 2022

View reviewed changes

gabrielspmoreira force-pushed the ranking_models_inputs branch from 48b7e4a to 314efe7 Compare September 20, 2022 13:39

marcromeyn approved these changes Sep 20, 2022

View reviewed changes

gabrielspmoreira force-pushed the ranking_models_inputs branch from c55b32d to d8782f0 Compare September 20, 2022 18:44

gabrielspmoreira added 18 commits September 20, 2022 17:21

Added support to InputBlockV2 to DCNModel and DeepFMModel

739504d

Fixed error on Jenkins GPU CI tests (an error occurs when CategoryEnc…

1259147

…oding is called with sparse=False for CI machine)

Adjusting DeepFM to use InputBlockV2 for the first-order features (on…

edcd95d

…e hot encoding representation of categ features)

Removing the hack that set the schema of input block for DeepFMModel

5a72a78

Added ToSparseFeatures and ToDenseFeatures

3770cb8

Refactored DeepFMModel to decouple the FM layer and fix bugs

541cb66

Adjusting FMBlock

5b74aa1

Fixing lint issues

785bdce

Making DeepFM accept wide_inputs for more flexibility (e.g. multi-hot…

5935669

… support), fixing bugs and adding tests to FMBlock

Fixed import after merge

649a776

Implementing suggestions from Marc and Oliver

638ef8b

Implementing other suggestions from Marc

a10cc2c

Simplifying the api

35aa859

Fixed test

d9a9e1f

Adjusting to use the updated InputBlockV2

2bb5cf4

Fixed formating issue

1d2b14a

Fixed failing test

8ab8742

Making DeepFM output layer configurable

d6df124

gabrielspmoreira force-pushed the ranking_models_inputs branch from 3cdb11d to d6df124 Compare September 20, 2022 21:40

gabrielspmoreira merged commit 1cf436f into main Sep 20, 2022

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gabrielspmoreira commented Sep 6, 2022 •

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