Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.
The SageMaker TensorFlow Training Toolkit is an open source library for making the
TensorFlow framework run on Amazon SageMaker <https://aws.amazon.com/documentation/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 <https://sagemaker.readthedocs.io/en/stable/using_tf.html>
__SageMaker Notebook Examples <https://github.com/awslabs/amazon-sagemaker-examples>
__#. Getting Started <#getting-started>
__
#. Building your Image <#building-your-image>
__
#. Running the tests <#running-the-tests>
__
Prerequisites
Make sure you have installed all of the following prerequisites on your
development machine:
- `Docker <https://www.docker.com/>`__
For Testing on GPU
^^^^^^^^^^^^^^^^^^
- `Nvidia-Docker <https://github.com/NVIDIA/nvidia-docker>`__
Recommended
^^^^^^^^^^^
- A Python environment management tool. (e.g.
`PyEnv <https://github.com/pyenv/pyenv>`__,
`VirtualEnv <https://virtualenv.pypa.io/en/stable/>`__)
Building your Image
-------------------
`Amazon SageMaker <https://aws.amazon.com/documentation/sagemaker/>`__
utilizes Docker containers to run all training jobs & inference endpoints.
The Docker images are built from the Dockerfiles specified in
`docker/ <https://github.com/aws/sagemaker-tensorflow-containers/tree/master/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 <https://github.com/aws/sagemaker-tensorflow-container/tree/tf-2>`__ branch.
To build the images, first copy the files under
`docker/build_artifacts/ <https://github.com/aws/sagemaker-tensorflow-container/tree/tf-2/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/ <https://github.com/aws/sagemaker-tensorflow-container/tree/master/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 <https://www.docker.com/>
__ and AWS credentials <https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html>
,
as the integration tests make calls to a couple AWS services. The integration and functional
tests require configurations specified within their respective
conftest.py <https://github.com/aws/sagemaker-tensorflow-containers/blob/master/test/integration/conftest.py>
.Make sure to update the account-id and region at a minimum.
Integration tests on GPU require Nvidia-Docker <https://github.com/NVIDIA/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 <your_docker_image> \
--tag <your_docker_image_tag> \
--framework-version <tensorflow_version> \
--processor <cpu_or_gpu>
::
# 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 <https://github.com/aws/sagemaker-tensorflow-container/tree/r1.0#functional-tests>`__.
Contributing
------------
Please read
`CONTRIBUTING.md <https://github.com/aws/sagemaker-tensorflow-containers/blob/master/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/