SageMaker PyTorch Serving Container

SageMaker PyTorch Serving Container is an open source library for making the PyTorch framework run on Amazon SageMaker.

This repository also contains Dockerfiles which install this library, PyTorch, and dependencies for building SageMaker PyTorch images.

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

For information on running PyTorch jobs on SageMaker: SageMaker PyTorch Estimators and Models.

For notebook examples: SageMaker Notebook Examples.

Table of Contents

1. Getting Started
2. Building your Image
3. 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 Docker files are grouped based on PyTorch version and separated based on Python version and processor type.

The Docker images, used to run training & inference jobs, are built from both corresponding "base" and "final" Dockerfiles.

Base Images

The "base" Dockerfile encompass the installation of the framework and all of the dependencies needed.

Tagging scheme is based on <PyTorch_version>-<processor>-py<python_version>. (e.g.1.0.0-cpu-py3)

All "final" Dockerfiles build images using base images that use the tagging scheme above.

If you want to build your base docker image, then use:

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

# CPU
docker build -t pytorch-base:<PyTorch_version>-cpu-py<python_version> -f docker/<PyTorch_version>/base/Dockerfile.cpu --build-arg py_version=<python_version> .

# GPU
docker build -t pytorch-base:<PyTorch_version>-gpu-py<python_version> -f docker/<PyTorch_version>/base/Dockerfile.gpu --build-arg py_version=<python_version> .

# Example

# CPU
docker build -t pytorch-base:1.0.0-cpu-py3 -f docker/1.0.0/base/Dockerfile.cpu --build-arg py_version=3 .

# GPU
docker build -t pytorch-base:1.0.0-gpu-py3 -f docker/1.0.0/base/Dockerfile.gpu --build-arg py_version=3 .

Final Images

The "final" Dockerfiles encompass the installation of the SageMaker specific support code.

All "final" Dockerfiles use base images for building.

These "base" images are specified with the naming convention of pytorch-base:<PyTorch_version>-<processor>-py<python_version>.

Before building "final" images:

Build your "base" image. Make sure it is named and tagged in accordance with your "final" Dockerfile.

# Create the SageMaker PyTorch Serving Container Python package.
cd sagemaker-pytorch-container
python setup.py bdist_wheel

If you want to build "final" Docker images, then use:

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

# CPU
docker build -t <image_name>:<tag> -f docker/<PyTorch_version>/final/Dockerfile.cpu --build-arg py_version=<python_version> .

# GPU
docker build -t <image_name>:<tag> -f docker/<PyTorch_version>/final/Dockerfile.gpu --build-arg py_version=<python_version> .

# Example

# CPU
docker build -t preprod-pytorch:1.0.0-cpu-py3 -f docker/1.0.0/final/Dockerfile.cpu --build-arg py_version=3 .

# GPU
docker build -t preprod-pytorch:1.0.0-gpu-py3 -f docker/1.0.0/final/Dockerfile.gpu --build-arg py_version=3 .

Running the tests

Running the tests requires installation of the SageMaker PyTorch Serving Container code and its test dependencies.

git clone https://github.com/aws/sagemaker-pytorch-container.git
cd sagemaker-pytorch-container
pip install -e .[test]

Tests are defined in test/ and include unit, local integration, and SageMaker integration 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

# or you can use tox to run unit tests as well as flake8 and code coverage

tox

Local Integration Tests

Running local integration tests require Docker and AWS credentials, as the local integration tests make calls to a couple 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 --docker-base-name <your_docker_image> \
                  --tag <your_docker_image_tag> \
                  --py-version <2_or_3> \
                  --framework-version <PyTorch_version> \
                  --processor <cpu_or_gpu>

# Example
pytest test/integration/local --docker-base-name preprod-pytorch \
                  --tag 1.0 \
                  --py-version 3 \
                  --framework-version 1.0.0 \
                  --processor cpu

SageMaker Integration Tests

SageMaker integration tests require your Docker image to be within an Amazon ECR repository.

The Docker base name is your ECR repository namespace.

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 --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 --aws-id 12345678910 \
                       --docker-base-name preprod-pytorch \
                       --instance-type ml.m4.xlarge \
                       --tag 1.0

Contributing

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

License

SageMaker PyTorch Serving Container 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/