The experimental endpoint of tf.keras.experimental.SidecarEvaluator was deprecated and is available as tf.keras.utils.SidecarEvaluator

PiperOrigin-RevId: 422581351

Author: tensorflower-gardener

site/en/guide/migrate/evaluator.ipynb

@@ -67,7 +67,7 @@
       "source": [
         "Evaluation is a critical part of measuring and benchmarking models.\n",
         "\n",
-        "This guide demonstrates how to migrate evaluator tasks from TensorFlow 1 to  TensorFlow 2. In Tensorflow 1  this functionality is implemented by `tf.estimator.train_and_evaluate`, when the API is running distributedly. In Tensorflow 2, you can use the built-in `tf.keras.experimental.SidecarEvaluator`, or a custom evaluation loop on the evaluator task.\n",
+        "This guide demonstrates how to migrate evaluator tasks from TensorFlow 1 to  TensorFlow 2. In Tensorflow 1  this functionality is implemented by `tf.estimator.train_and_evaluate`, when the API is running distributedly. In Tensorflow 2, you can use the built-in `tf.keras.utils.SidecarEvaluator`, or a custom evaluation loop on the evaluator task.\n",
         "\n",
         "There are simple serial evaluation options in both TensorFlow 1 (`tf.estimator.Estimator.evaluate`) and TensorFlow 2 (`Model.fit(..., validation_data=(...))` or `Model.evaluate`). The evaluator task is preferable when you would like your workers not switching between training and evaluation, and built-in evaluation in `Model.fit` is preferable when you would like your evaluation to be distributed.\n"
       ]
@@ -193,7 +193,7 @@
       "source": [
         "## TensorFlow 2: Evaluating a Keras model\n",
         "\n",
-        "In TensorFlow 2, if you use the Keras `Model.fit` API for training, you can evaluate the model with `tf.keras.experimental.SidecarEvaluator`. You can also visualize the evaluation metrics in Tensorboard which is not shown in this guide.\n",
+        "In TensorFlow 2, if you use the Keras `Model.fit` API for training, you can evaluate the model with `tf.keras.utils.SidecarEvaluator`. You can also visualize the evaluation metrics in Tensorboard which is not shown in this guide.\n",
         "\n",
         "To help demonstrate this, let's first start by defining and training the model:\n"
       ]
@@ -240,7 +240,7 @@
         "id": "AhU3VTYZoDh-"
       },
       "source": [
-        "Then, evaluate the model using `tf.keras.experimental.SidecarEvaluator`. In real training, it's recommended to use a separate job to conduct the evaluation to free up worker resources for training."
+        "Then, evaluate the model using `tf.keras.utils.SidecarEvaluator`. In real training, it's recommended to use a separate job to conduct the evaluation to free up worker resources for training."
       ]
     },
     {
@@ -254,7 +254,7 @@
         "data = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
         "data = data.batch(64)\n",
         "\n",
-        "tf.keras.experimental.SidecarEvaluator(\n",
+        "tf.keras.utils.SidecarEvaluator(\n",
         "    model=model,\n",
         "    data=data,\n",
         "    checkpoint_dir=log_dir,\n",
@@ -270,7 +270,7 @@
       "source": [
         "## Next steps\n",
         "\n",
-        "- To learn more about sidecar evaluation consider reading the `tf.keras.experimental.SidecarEvaluator` API docs.\n",
+        "- To learn more about sidecar evaluation consider reading the `tf.keras.utils.SidecarEvaluator` API docs.\n",
         "- To consider alternating training and evaluation in Keras consider reading about [other built-in methods](https://www.tensorflow.org/guide/keras/train_and_evaluate)."
       ]
     }