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CHANGELOG.md

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# Changelog
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## v2.229.0 (2024-08-15)
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### Features
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* Support for ModelBuilder In_Process Mode (1/2)
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* Pulling in dependencies (in_process mode) using conda environment
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* Add optional CodeArtifact login to FrameworkProcessing job script
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* implemented security-monitoring to send metrics to CW #1510
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### Bug Fixes and Other Changes
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* alt configs model deployment and training issues
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* fix keras extension in integ test
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* update image_uri_configs 08-13-2024 07:17:54 PST
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* trn1 instance family does not support volume size
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* Update model.py
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* removed log statement
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* update image_uri_configs 08-09-2024 07:18:00 PST
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* Added torchrun compatibility for distributet training across multiple GPUs in a single node (single instance)
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* BiasConfig type hint
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* add model monitor image accounts for ap-southeast-5 and eu-central-2
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* aligned UTC times with PST
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* ensure hpt jobs inherit tags from config
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* add JumpStart PDT and OSU regions
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* chore(deps): bump certifi in /src/sagemaker/serve/utils
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* Updates for DJL 0.29.0 release
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* chore(deps): bump apache-airflow from 2.9.2 to 2.9.3 in /requirements/extras
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* chore(deps): bump torch from 2.0.1 to 2.2.0 in /tests/data/serve_resources/mlflow/pytorch
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* avoided printing stack trace and escaped input
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* removing kwargs as this is breaking predictor_cls param for mode…
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## v2.228.0 (2024-08-06)
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### Features

VERSION

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2.228.1.dev0
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2.229.1.dev0

tests/data/tensorflow_mnist/mnist_v2.py

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@@ -198,7 +198,7 @@ def main(args):
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if args.current_host == args.hosts[0]:
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ckpt_manager.save()
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net.save("/opt/ml/model/1.keras")
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net.save("/opt/ml/model/1")
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if __name__ == "__main__":

tests/integ/test_tf.py

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@@ -85,6 +85,11 @@ def test_mnist_with_checkpoint_config(
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tensorflow_training_latest_version,
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tensorflow_training_latest_py_version,
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):
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if Version(tensorflow_training_latest_version) >= Version("2.16"):
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pytest.skip(
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"This test is failing in TensorFlow 2.16 beacuse of an upstream bug: "
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"https://github.com/tensorflow/io/issues/2039"
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)
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checkpoint_s3_uri = "s3://{}/checkpoints/tf-{}".format(
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sagemaker_session.default_bucket(), sagemaker_timestamp()
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)
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tensorflow_training_latest_version,
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tensorflow_training_latest_py_version,
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):
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if Version(tensorflow_training_latest_version) >= Version("2.16"):
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pytest.skip(
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"This test is failing in TensorFlow 2.16 beacuse of an upstream bug: "
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"https://github.com/tensorflow/io/issues/2039"
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)
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_create_and_fit_estimator(
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sagemaker_session,
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tensorflow_training_latest_version,

tests/integ/test_tuner.py

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import numpy as np
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import pytest
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from botocore.exceptions import ClientError
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from packaging.version import Version
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import tests.integ
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from sagemaker import KMeans, LDA, RandomCutForest, image_uris
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tensorflow_training_latest_version,
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tensorflow_training_latest_py_version,
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):
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if Version(tensorflow_training_latest_version) >= Version("2.16"):
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pytest.skip(
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"This test is failing in TensorFlow 2.16 beacuse of an upstream bug: "
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"https://github.com/tensorflow/io/issues/2039"
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)
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resource_path = os.path.join(DATA_DIR, "tensorflow_mnist")
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script_path = "mnist.py"
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tensorflow_training_latest_py_version,
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):
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"""Test Tensorflow multi-instance using the same VpcConfig for training and inference"""
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if Version(tensorflow_training_latest_version) >= Version("2.16"):
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pytest.skip(
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"This test is failing in TensorFlow 2.16 beacuse of an upstream bug: "
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"https://github.com/tensorflow/io/issues/2039"
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)
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instance_type = cpu_instance_type
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instance_count = 2
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