https://github.com/aws/sagemaker-tensorflow-training-toolkit

Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.
https://github.com/aws/sagemaker-tensorflow-training-toolkit

aws docker sagemaker tensorflow

Last synced: about 1 month ago
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Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.

• Host: GitHub
• URL: https://github.com/aws/sagemaker-tensorflow-training-toolkit
• Owner: aws
• License: apache-2.0
• Created: 2018-01-23T17:41:21.000Z (almost 7 years ago)
• Default Branch: tf-2
• Last Pushed: 2023-04-10T08:52:31.000Z (over 1 year ago)
• Last Synced: 2024-10-04T21:05:14.732Z (about 1 month ago)
• Topics: aws, docker, sagemaker, tensorflow
• Language: Python
• Homepage:
• Size: 14.1 MB
• Stars: 270
• Watchers: 54
• Forks: 160
• Open Issues: 9
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
=====================================

SageMaker TensorFlow Training Toolkit

=====================================

The SageMaker TensorFlow Training Toolkit is an open source library for making the

TensorFlow framework run on `Amazon SageMaker `__.

This repository also contains Dockerfiles which install this library, TensorFlow, and dependencies

for building SageMaker TensorFlow images.

For information on running TensorFlow jobs on SageMaker:

- `SageMaker Python SDK documentation `__

- `SageMaker Notebook Examples `__

Table of Contents

-----------------

#. `Getting Started <#getting-started>`__

#. `Building your Image <#building-your-image>`__

#. `Running the tests <#running-the-tests>`__

Getting Started

---------------

Prerequisites

~~~~~~~~~~~~~

Make sure you have installed all of the following prerequisites on your

development machine:

- `Docker `__

For Testing on GPU

^^^^^^^^^^^^^^^^^^

-  `Nvidia-Docker `__

Recommended

^^^^^^^^^^^

-  A Python environment management tool. (e.g.

   `PyEnv `__,

   `VirtualEnv `__)

Building your Image

-------------------

`Amazon SageMaker `__

utilizes Docker containers to run all training jobs & inference endpoints.

The Docker images are built from the Dockerfiles specified in

`docker/ `__.

The Dockerfiles are grouped based on TensorFlow version and separated

based on Python version and processor type.

The Dockerfiles for TensorFlow 2.0+ are available in the

`tf-2 `__ branch.

To build the images, first copy the files under

`docker/build_artifacts/ `__

to the folder container the Dockerfile you wish to build.

::

    # Example for building a TF 2.1 image with Python 3

    cp docker/build_artifacts/* docker/2.1.0/py3/.

After that, go to the directory containing the Dockerfile you wish to build,

and run ``docker build`` to build the image.

::

    # Example for building a TF 2.1 image for CPU with Python 3

    cd docker/2.1.0/py3

    docker build -t tensorflow-training:2.1.0-cpu-py3 -f Dockerfile.cpu .

Don't forget the period at the end of the ``docker build`` command!

Running the tests

-----------------

Running the tests requires installation of the SageMaker TensorFlow Training Toolkit code and its test

dependencies.

::

    git clone https://github.com/aws/sagemaker-tensorflow-container.git

    cd sagemaker-tensorflow-container

    pip install -e .[test]

Tests are defined in

`test/ `__

and include unit, integration and functional tests.

Unit Tests

~~~~~~~~~~

If you want to run unit tests, then use:

::

    # All test instructions should be run from the top level directory

    pytest test/unit

Integration Tests

~~~~~~~~~~~~~~~~~

Running integration tests require `Docker `__ and `AWS

credentials `__,

as the integration tests make calls to a couple AWS services. The integration and functional

tests require configurations specified within their respective

`conftest.py `__.Make sure to update the account-id and region at a minimum.

Integration tests on GPU require `Nvidia-Docker `__.

Before running integration tests:

#. Build your Docker image.

#. Pass in the correct pytest arguments to run tests against your Docker image.

If you want to run local integration tests, then use:

::

    # Required arguments for integration tests are found in test/integ/conftest.py

    pytest test/integration --docker-base-name  \

                            --tag  \

                            --framework-version  \

                            --processor 

::

    # Example

    pytest test/integration --docker-base-name preprod-tensorflow \

                            --tag 1.0 \

                            --framework-version 1.4.1 \

                            --processor cpu

Functional Tests

~~~~~~~~~~~~~~~~

Functional tests are removed from the current branch, please see them in older branch `r1.0 `__.

Contributing

------------

Please read

`CONTRIBUTING.md `__

for details on our code of conduct, and the process for submitting pull

requests to us.

License

-------

SageMaker TensorFlow Containers is licensed under the Apache 2.0 License. It is copyright 2018

Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at:

http://aws.amazon.com/apache2.0/