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Copy file name to clipboardExpand all lines: doc/frameworks/tensorflow/upgrade_from_legacy.rst
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Upgrade from Legacy TensorFlow Support
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######################################
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With v2 of the SageMaker Python SDK, support for legacy SageMaker TensorFlow images has been deprecated.
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With version 2.0 or later of the SageMaker Python SDK, support for legacy SageMaker TensorFlow images has been deprecated.
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This guide explains how to upgrade your SageMaker Python SDK usage.
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For more information about using TensorFlow with the SageMaker Python SDK, see `Use TensorFlow with the SageMaker Python SDK <using_tf.html>`_.
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Continue with Legacy Versions
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*****************************
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While not recommended, you can still use a legacy TensorFlow version with v2 of the SageMaker Python SDK.
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While not recommended, you can still use a legacy TensorFlow version with version 2.0 or later of the SageMaker Python SDK.
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In order to do so, you need to change how a few parameters are defined.
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Training
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========
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When creating an estimator, v2 requires the following changes:
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When creating an estimator, the Python SDK version 2.0 or later requires the following changes:
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#. Explicitly specify the ECR image URI via ``image_name``.
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To determine the URI, you can use :func:`sagemaker.fw_utils.create_image_uri`.
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from sagemaker.tensorflow import TensorFlow
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# v1
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# v1.x
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estimator = TensorFlow(
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...
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source_dir="code",
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requirements_file="requirements.txt",
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)
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# v2
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# v2.0 or later
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estimator = TensorFlow(
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...
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source_dir="code",
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Inference
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=========
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Using a legacy TensorFlow version for endpoints and batch transform can be achieved with v2 of the SageMaker Python SDK with some minor changes to your code.
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Using a legacy TensorFlow version for endpoints and batch transform can be achieved with version 2.0 or later of the SageMaker Python SDK with some minor changes to your code.
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From an Estimator
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-----------------
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