SageMaker MXNet Training Toolkit is an open-source library for using MXNet to train models on Amazon SageMaker. For inference, see SageMaker MXNet Inference Toolkit. For the Dockerfiles used for building SageMaker MXNet Containers, see AWS Deep Learning Containers. For information on running MXNet jobs on Amazon SageMaker, please refer to the SageMaker Python SDK documentation.

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

One of the multiple ways to setup a virtual environment

# use a package virtualenv
# create a virtualenv
virtualenv -p python3 <name of env>
# activate the virtualenv
source <name of env>/bin/activate

pip install --upgrade .[test]

To run specific test

tox -- -k test/unit/test_training.py::test_train_for_distributed_scheduler

To run an entire file

tox -- test/unit/test_training.py

To run all tests within a folder [e.g. integration/local/]

Note: To run integration tests locally, one needs to build an image. To trigger image build, use -B flag.

tox -- test/integration/local

You can also run them in parallel:

tox -- -n auto test/integration/local

To run for specific interpreter [Python environment], use the -e flag

tox -e py37 -- test/unit/test_training.py

Make sure to provide AWS account ID, Region, Docker base name & Tag. Docker Registry is composed of (aws_id, region) Image URI is composed of (docker_registry, docker_base_name, tag)

Resulting Image URI is composed as: {aws_id}.dkr.ecr.{region}.amazonaws.com/{docker_base_name}:{tag}

tox -- --aws-id <aws_id> --region <region> --docker-base-name <docker_base_name> --tag <tag> test/integration/sagemaker

For more details, refer conftest.py

SageMaker MXNet Training Toolkit 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/