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Apr 14, 2020
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doc: update sagemaker pytorch containers to external link #1409

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fix: update sagemaker pytorch containers to external link
Apr 14, 2020
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Merge branch 'master' into pytorch-doc
laurenyu Apr 14, 2020
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6 changes: 3 additions & 3 deletions doc/using_pytorch.rst
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Expand Up @@ -134,7 +134,7 @@ Using third-party libraries
---------------------------

When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``torch``, ``torchvision``, and ``numpy``.
For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <#id4>`__.
For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch#sagemaker-pytorch-docker-containers>`_.

If there are other packages you want to use with your script, you can include a ``requirements.txt`` file in the same directory as your training script to install other dependencies at runtime. Both ``requirements.txt`` and your training script should be put in the same folder. You must specify this folder in ``source_dir`` argument when creating PyTorch estimator.

Expand Down Expand Up @@ -673,7 +673,7 @@ The following are optional arguments. When you create a ``PyTorch`` object, you
model training code.
- ``framework_version`` PyTorch version you want to use for executing
your model training code. You can find the list of supported versions
in `SageMaker PyTorch Docker Containers <#id4>`_.
in `SageMaker PyTorch Docker Containers <https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch#sagemaker-pytorch-docker-containers>`_.
- ``train_volume_size`` Size in GB of the EBS volume to use for storing
input data during training. Must be large enough to store training
data if input_mode='File' is used (which is the default).
Expand All @@ -698,7 +698,7 @@ The following are optional arguments. When you create a ``PyTorch`` object, you
serving. If specified, the estimator will use this image for training and
hosting, instead of selecting the appropriate SageMaker official image based on
framework_version and py_version. Refer to: `SageMaker PyTorch Docker Containers
<#id4>`_ for details on what the Official images support
<https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch#sagemaker-pytorch-docker-containers>`_ for details on what the Official images support
and where to find the source code to build your custom image.

***********************************
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