doc: add third-party libraries sections to using_chainer and using_pytorch topics

doc/using_chainer.rst

@@ -99,6 +99,16 @@ inadvertently run your training code at the wrong point in execution.
 
 For more on training environment variables, please visit https://github.com/aws/sagemaker-containers.
 
+Using third-party libraries
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``chainer``, ``numpy``, and ``cupy``.
+For more information on the runtime environment, including specific package versions, see `SageMaker Chainer Docker containers <#sagemaker-chainer-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.
+A ``requirements.txt`` file is a text file that contains a list of items that are installed by using ``pip install``. You can also specify the version of an item to install.
+For information about the format of a ``requirements.txt`` file, see `Requirements Files <https://pip.pypa.io/en/stable/user_guide/#requirements-files>`__ in the pip documentation.
+
 Running a Chainer training script in SageMaker
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

doc/using_mxnet.rst

@@ -293,9 +293,12 @@ Lastly, if you were relying on the container launching a parameter server for us
 Using third-party libraries
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``mxnet``, ``numpy``, ``onnx``, and ``keras-mxnet``. For more information on the runtime environment, including specific package versions, see `SageMaker MXNet Containers <#sagemaker-mxnet-containers>`__.
+When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``mxnet``, ``numpy``, ``onnx``, and ``keras-mxnet``.
+For more information on the runtime environment, including specific package versions, see `SageMaker MXNet Containers <#sagemaker-mxnet-containers>`__.
 
-If there are other packages you want to use with your script, you can include a `requirements.txt <https://pip.pypa.io/en/stable/user_guide/#requirements-files>`__ file in the same directory as your training script to install other dependencies at runtime.
+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.
+A ``requirements.txt`` file is a text file that contains a list of items that are installed by using ``pip install``. You can also specify the version of an item to install.
+For information about the format of a ``requirements.txt`` file, see `Requirements Files <https://pip.pypa.io/en/stable/user_guide/#requirements-files>`__ in the pip documentation.
 
 Running an MXNet training script in SageMaker
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

doc/using_pytorch.rst

@@ -2,6 +2,8 @@
 Using PyTorch with the SageMaker Python SDK
 ===========================================
 
+.. contents::
+
 With PyTorch Estimators and Models, you can train and host PyTorch models on Amazon SageMaker.
 
 Supported versions of PyTorch: ``0.4.0``, ``1.0.0``.
@@ -10,20 +12,6 @@ We recommend that you use the latest supported version, because that's where we
 
 You can visit the PyTorch repository at https://github.com/pytorch/pytorch.
 
-Table of Contents
------------------
-
-1. `Training with PyTorch <#training-with-pytorch>`__
-2. `PyTorch Estimators <#pytorch-estimators>`__
-3. `Distributed PyTorch Training <#distributed-pytorch-training>`__
-4. `Saving models <#saving-models>`__
-5. `Deploying PyTorch Models <#deploying-pytorch-models>`__
-6. `SageMaker PyTorch Model Server <#sagemaker-pytorch-model-server>`__
-7. `Working with Existing Model Data and Training Jobs <#working-with-existing-model-data-and-training-jobs>`__
-8. `PyTorch Training Examples <#pytorch-training-examples>`__
-9. `SageMaker PyTorch Docker Containers <#sagemaker-pytorch-docker-containers>`__
-
-
 Training with PyTorch
 ------------------------
 
@@ -122,6 +110,16 @@ when instantiating PyTorch Estimator.
 
 For more on training environment variables, please visit `SageMaker Containers <https://github.com/aws/sagemaker-containers>`_.
 
+Using third-party libraries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``torch``, ``torchvisopm``, and ``numpy``.
+For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <#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.
+A ``requirements.txt`` file is a text file that contains a list of items that are installed by using ``pip install``. You can also specify the version of an item to install.
+For information about the format of a ``requirements.txt`` file, see `Requirements Files <https://pip.pypa.io/en/stable/user_guide/#requirements-files>`__ in the pip documentation.
+
 Running a PyTorch training script in SageMaker
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~