Amazon SageMaker RL Containers

A set of Dockerfiles that enables Reinforcement Learning (RL) solutions to be used in SageMaker.

The SageMaker team uses this repository to build its official RL images. On how to use any of these images on SageMaker, see Python SDK. For end users, this repository is typically of interest if you need implementation details of the official image, or if you want to use it to build your own customized RL image.

For information on running RL jobs on SageMaker: SageMaker RLEstimators.

For notebook examples: SageMaker Notebook Examples.

Table of Contents

1. Getting Started
2. Building Your Image
3. Running the Tests
4. RL Images Provided by SageMaker

Getting Started

Prerequisites

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

• Docker

For Testing on GPU

• Nvidia-Docker2

Recommended

• A Python environment management tool (e.g. PyEnv, VirtualEnv)

Building Your Image

Amazon SageMaker utilizes Docker containers to run all training jobs and inference endpoints.

The Docker images are built from the Dockerfiles specified in coach/docker and ray/docker.

The Docker files are grouped based on RL toolkit (Coach or Ray), toolkit version and separated based on framework, e.g.: coach/docker/0.11.0/Dockerfile.mxnet.

All Dockerfiles use deep learning framework images provided by SageMaker as their "base" images.

These "base" images are specified with the following naming convention:

520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-<framework>:<framework_version>-<processor>-py3

• <framework> can be tensorflow-scriptmode (with <framework_version> 1.11.0 or higher depending on the toolkit requirements) or mxnet (with <framework_version> 1.3.0 or higher depending on the toolkit requirements);
• <processor> can be cpu or gpu;
• for valid <region> values please see list of supported SageMaker regions.

Before building images:

Pull deep learning framework "base" image, which require Docker, AWS credentials, and AWS CLI.

# Login into SageMaker ECR account
$(aws ecr get-login --no-include-email --region <region> --registry-ids 520713654638)
# Pull docker image from ECR
docker pull 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-<framework>:<framework_version>-<processor>-py3

# Example

$(aws ecr get-login --no-include-email --region us-west-2 --registry-ids 520713654638)

# CPU TensorFlow image
docker pull 520713654638.dkr.ecr.us-west-2.amazonaws.com/sagemaker-tensorflow-scriptmode:1.11.0-cpu-py3

# GPU MXNet image
docker pull 520713654638.dkr.ecr.us-west-2.amazonaws.com/sagemaker-mxnet:1.3.0-gpu-py3

To build RL Docker image:

# All build instructions assume you're building from the root directory of the sagemaker-rl-container.

# CPU
docker build -t <image_name>:<tag> -f <rl_toolkit>docker/<rl_toolkit_version>/Dockerfile.<framework> --build-arg processor=<cpu_or_gpu> .

# GPU
docker build -t <image_name>:<tag> -f <rl_toolkit>/docker/<rl_toolkit_version>/Dockerfile.<framework> --build-arg processor=<cpu_or_gpu> .

# Example

# Ray TensorFlow CPU
docker build -t tf-ray:0.5.3-cpu-py3 -f ray/docker/0.5.3/Dockerfile.tf --build-arg processor=cpu .

# Coach TensorFlow GPU
docker build -t tf-coach:0.11.0-gpu-py3 -f coach/docker/0.11.0/Dockerfile.tf --build-arg processor=gpu .

# Coach MXNet CPU
docker build -t mxnet-coach:0.11.0-cpu-py3 -f coach/docker/0.11.0/Dockerfile.mxnet --build-arg processor=cpu .

Running the Tests

Running the tests requires installation of test dependencies.

git clone https://github.com/aws/sagemaker-rl-container.git
cd sagemaker-rl-container
pip install .

Tests are defined in test/ and include local integration and SageMaker integration tests.

Local Integration Tests

Running local integration tests require Docker and AWS credentials, as the local integration tests make calls to a couple of AWS services. The local integration tests and SageMaker integration tests require configurations specified within their respective conftest.py.

Local integration tests on GPU require Nvidia-Docker.

Before running local integration tests:

1. Build your Docker image.
2. 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/conftest.py
pytest test/integration/local --toolkit <toolkit_to_run_tests_for> \
                              --docker-base-name <your_docker_image> \
                              --tag <your_docker_image_tag> \
                              --processor <cpu_or_gpu>

# Example
pytest test/integration/local --toolkit coach \
                              --docker-base-name custom-rl-coach-image \
                              --tag 1.0 \
                              --processor cpu

SageMaker Integration Tests

SageMaker integration tests require your Docker image to be within an Amazon ECR repository <https://docs .aws.amazon.com/AmazonECS/latest/developerguide/ECS_Console_Repositories.html>__.

The Docker base name is your ECR repository namespace <https://docs.aws.amazon .com/AmazonECR/latest/userguide/Repositories.html>__.

The instance type is your specified Amazon SageMaker Instance Type that the SageMaker integration test will run on.

Before running SageMaker integration tests:

1. Build your Docker image.
2. Push the image to your ECR repository.
3. Pass in the correct pytest arguments to run tests on SageMaker against the image within your ECR repository.

If you want to run a SageMaker integration end to end test on Amazon SageMaker, then use:

# Required arguments for integration tests are found in test/conftest.py
pytest test/integration/sagemaker --toolkit <toolkit_to_run_tests_for> \
                                  --aws-id <your_aws_id> \
                                  --docker-base-name <your_docker_image> \
                                  --instance-type <amazon_sagemaker_instance_type> \
                                  --tag <your_docker_image_tag> \

# Example
pytest test/integration/sagemaker --toolkit coach \
                                  --aws-id 12345678910 \
                                   --docker-base-name custom-rl-coach-image \
                                  --instance-type ml.m4.xlarge \
                                  --tag 1.0

RL Images Provided by SageMaker

MXNet Coach Images:

• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-mxnet:coach0.11-cpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-mxnet:coach0.11.0-cpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-mxnet:coach0.11-gpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-mxnet:coach0.11.0-gpu-py3

TensorFlow Coach Images:

• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:coach0.10-cpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:coach0.10.1-cpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:coach0.10-gpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:coach0.10.1-gpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:coach0.11-cpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:coach0.11.0-cpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:coach0.11-gpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:coach0.11.0-gpu-py3

TensorFlow Ray Images:

• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:ray0.5-cpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:ray0.5.3-cpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:ray0.5-gpu-py3
• 520713654638.dkr.ecr.<region>.amazonaws.com/sagemaker-rl-tensorflow:ray0.5.3-gpu-py3

List of supported SageMaker regions.

Contributing

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

License

This library is licensed under the Apache 2.0 License.