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Add framework_version to all TensorFlowModel examples #5038

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Feb 17, 2025
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Add framework_version to all TensorFlowModel examples #5038

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85f13fa
Add framework_version to all TensorFlowModel examples
Feb 14, 2025
04edd79
update framework_version to x.x.x
Feb 14, 2025
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4 changes: 2 additions & 2 deletions doc/frameworks/tensorflow/deploying_tensorflow_serving.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@ If you already have existing model artifacts in S3, you can skip training and de

from sagemaker.tensorflow import TensorFlowModel

model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')
model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole', framework_version='x.x.x')

predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge')

Expand All @@ -74,7 +74,7 @@ Python-based TensorFlow serving on SageMaker has support for `Elastic Inference

from sagemaker.tensorflow import TensorFlowModel

model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')
model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole', framework_version='x.x.x')

predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge', accelerator_type='ml.eia1.medium')

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13 changes: 8 additions & 5 deletions doc/frameworks/tensorflow/using_tf.rst
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Expand Up @@ -468,7 +468,7 @@ If you already have existing model artifacts in S3, you can skip training and de

from sagemaker.tensorflow import TensorFlowModel

model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')
model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole', framework_version='x.x.x')

predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge')

Expand All @@ -478,7 +478,7 @@ Python-based TensorFlow serving on SageMaker has support for `Elastic Inference

from sagemaker.tensorflow import TensorFlowModel

model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')
model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole', framework_version='x.x.x')

predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge', accelerator_type='ml.eia1.medium')

Expand Down Expand Up @@ -767,7 +767,8 @@ This customized Python code must be named ``inference.py`` and is specified thro

model = TensorFlowModel(entry_point='inference.py',
model_data='s3://mybucket/model.tar.gz',
role='MySageMakerRole')
role='MySageMakerRole',
framework_version='x.x.x')

In the example above, ``inference.py`` is assumed to be a file inside ``model.tar.gz``. If you want to use a local file instead, you must add the ``source_dir`` argument. See the documentation on `TensorFlowModel <https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/sagemaker.tensorflow.html#sagemaker.tensorflow.model.TensorFlowModel>`_.

Expand Down Expand Up @@ -923,7 +924,8 @@ processing. There are 2 ways to do this:
model = TensorFlowModel(entry_point='inference.py',
dependencies=['requirements.txt'],
model_data='s3://mybucket/model.tar.gz',
role='MySageMakerRole')
role='MySageMakerRole',
framework_version='x.x.x')


2. If you are working in a network-isolation situation or if you don't
Expand All @@ -941,7 +943,8 @@ processing. There are 2 ways to do this:
model = TensorFlowModel(entry_point='inference.py',
dependencies=['/path/to/folder/named/lib'],
model_data='s3://mybucket/model.tar.gz',
role='MySageMakerRole')
role='MySageMakerRole',
framework_version='x.x.x')

For more information, see: https://github.com/aws/sagemaker-tensorflow-serving-container#prepost-processing

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