doc: add link to container dependencies in using PyTorch topic

Author: eslesar-aws

doc/using_mxnet.rst

@@ -797,97 +797,6 @@ The following are optional arguments. When you create an ``MXNet`` object, you c
       }
     }
 
-**************************
-SageMaker MXNet Containers
-**************************
-
-=======
-
-Required arguments
-==================
-
-The following are required arguments to the ``MXNet`` constructor. When you create an MXNet object, you must include these in the constructor, either positionally or as keyword arguments.
-
--  ``entry_point`` Path (absolute or relative) to the Python file which
-   should be executed as the entry point to training.
--  ``role`` An AWS IAM role (either name or full ARN). The Amazon
-   SageMaker training jobs and APIs that create Amazon SageMaker
-   endpoints use this role to access training data and model artifacts.
-   After the endpoint is created, the inference code might use the IAM
-   role, if accessing AWS resource.
--  ``train_instance_count`` Number of Amazon EC2 instances to use for
-   training.
--  ``train_instance_type`` Type of EC2 instance to use for training, for
-   example, 'ml.c4.xlarge'.
-
-Optional arguments
-==================
-
-The following are optional arguments. When you create an ``MXNet`` object, you can specify these as keyword arguments.
-
--  ``source_dir`` Path (absolute or relative) to a directory with any
-   other training source code dependencies including the entry point
-   file. Structure within this directory will be preserved when training
-   on SageMaker.
--  ``dependencies (list[str])`` A list of paths to directories (absolute or relative) with
-   any additional libraries that will be exported to the container (default: ``[]``).
-   The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
-   If the ``source_dir`` points to S3, code will be uploaded and the S3 location will be used
-   instead. For example, the following call
-
-   >>> MXNet(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
-
-   results in the following inside the container:
-
-   .. code::
-
-       opt/ml/code
-         ├── train.py
-         ├── common
-         └── virtual-env
-
--  ``hyperparameters`` Hyperparameters that will be used for training.
-   Will be made accessible as a dict[str, str] to the training code on
-   SageMaker. For convenience, accepts other types besides str, but
-   str() will be called on keys and values to convert them before
-   training.
--  ``py_version`` Python version you want to use for executing your
-   model training code. Valid values: 'py2' and 'py3'.
--  ``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).
--  ``train_max_run`` Timeout in seconds for training, after which Amazon
-   SageMaker terminates the job regardless of its current status.
--  ``input_mode`` The input mode that the algorithm supports. Valid
-   modes: 'File' - Amazon SageMaker copies the training dataset from the
-   S3 location to a directory in the Docker container. 'Pipe' - Amazon
-   SageMaker streams data directly from S3 to the container via a Unix
-   named pipe.
--  ``output_path`` Location where you want the training result (model artifacts and optional output files) saved.
-   This should be an S3 location unless you're using Local Mode, which also supports local output paths.
-   If not specified, results are stored to a default S3 bucket.
--  ``output_kms_key`` Optional KMS key ID to optionally encrypt training
-   output with.
--  ``job_name`` Name to assign for the training job that the fit()
-   method launches. If not specified, the estimator generates a default
-   job name, based on the training image name and current timestamp
--  ``image_name`` An alternative docker image to use for training and
-   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 MXNet Docker Containers
-   <#sagemaker-mxnet-docker-containers>`_ for details on what the Official images support
-   and where to find the source code to build your custom image.
--  ``distributions`` For versions 1.3 and above only.
-   Specifies information for how to run distributed training.
-   To launch a parameter server during training, set this argument to:
-
-.. code::
-
-    {
-      'parameter_server': {
-        'enabled': True
-      }
-    }
 
 **************************
 SageMaker MXNet Containers

doc/using_pytorch.rst

@@ -658,4 +658,6 @@ The following are optional arguments. When you create a ``PyTorch`` object, you
 SageMaker PyTorch Docker containers 
 ***********************************
 
-For information about SageMaker PyTorch containers, see  `the SageMaker PyTorch containers repository <https://github.com/aws/sagemaker-pytorch-containers>`_.
+For information about SageMaker PyTorch containers, see  `the SageMaker PyTorch containers repository <https://github.com/aws/sagemaker-pytorch-containers>`_.
+
+For information about SageMaker PyTorch container dependencies, see `SageMaker PyTorch Containers <https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch#sagemaker-pytorch-docker-containers>`_.