WIP: split serving container

.gitignore

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+.idea/

CHANGELOG.md

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+# Changelog
+
+## v1.0.5 (2019-08-05)
+
+### Bug fixes and other changes
+
+ * upgrade sagemaker-container version
+ * unmark 2 deploy tests
+ * update p2 restricted regions
+
+## v1.0.6 (2019-06-21)
+
+### Bug fixes and other changes
+
+ * unmark 2 deploy tests
+
+## v1.0.5 (2019-06-20)
+
+### Bug fixes and other changes
+
+ * update p2 restricted regions
+
+## v1.0.4 (2019-06-19)
+
+### Bug fixes and other changes
+
+ * skip tests in gpu instance restricted regions
+
+## v1.0.3 (2019-06-18)
+
+### Bug fixes and other changes
+
+ * modify buildspecs and tox files
+
+## v1.0.2 (2019-06-17)
+
+### Bug fixes and other changes
+
+ * freeze dependency versions
+
+## v1.0.1 (2019-06-13)
+
+### Bug fixes and other changes
+
+ * add buildspec-release file and upgrade cuda version
+ * upgrade PyTorch to 1.1
+ * disable test_mnist_gpu for py2 for now
+ * fix broken line of buildspec
+ * prevent hidden errors in buildspec
+ * Add AWS CodeBuild buildspec for pull request
+ * Bump minimum SageMaker Containers version to 2.4.6 and pin SageMaker Python SDK to 1.18.16
+ * fix broken link in README
+ * Add timeout to test_mnist_gpu test
+ * Use dummy role in tests and update local failure integ test
+ * Use the SageMaker Python SDK for local serving integ tests
+ * Use the SageMaker Python SDK for local integ distributed training tests
+ * Use the SageMaker Python SDK for local integ single-machine training tests
+ * Pin fastai version to 1.0.39 in CPU dockerfile
+ * Use the SageMaker Python SDK for SageMaker integration tests
+ * Add missing rendering dependencies for opencv and a simple test.
+ * Add opencv support.
+ * Freeze PyYAML version to avoid conflict with Docker Compose
+ * Unfreeze numpy version.
+ * Freeze TorchVision to 0.2.1
+ * Specify region when creating S3 resource in integ tests
+ * Read framework version from Python SDK for integ test default
+ * Fix unicode display problem in py2 container
+ * freeze pip <=18.1, fastai == 1.0.39, numpy <= 1.15.4
+ * Add support for fastai (https://github.com/fastai/fastai) library.
+ * Remove "requsests" from tests dependencies to avoid regular conflicts with "requests" package from "sagemaker" dependencies.
+ * Add support for PyTorch-1.0.

README.rst

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+
+===========================
+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 <https://github.com/aws/sagemaker-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
+<https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch>`__.
+
+For notebook examples: `SageMaker Notebook
+Examples <https://github.com/awslabs/amazon-sagemaker-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 <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-pytorch-container/tree/master/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 <https://github.com/aws/sagemaker-pytorch-container/blob/master/docker/1.0.0/final/Dockerfile.cpu#L2>`__.
+
+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/ <https://github.com/aws/sagemaker-pytorch-container/tree/master/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 <https://www.docker.com/>`__ and `AWS
+credentials <https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html>`__,
+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 <https://github.com/aws/sagemaker-pytorch-container/blob/master/test/conftest.py>`__.
+
+Local integration tests on GPU require `Nvidia-Docker <https://github.com/NVIDIA/nvidia-docker>`__.
+
+Before running local 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/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 <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
+<https://aws.amazon.com/sagemaker/pricing/instance-types/>`__ that the SageMaker integration test will run on.
+
+Before running SageMaker integration tests:
+
+#. Build your Docker image.
+#. Push the image to your ECR repository.
+#. 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 <https://aws.amazon.com/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 <https://github.com/aws/sagemaker-pytorch-container/blob/master/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/

VERSION

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+1.0.6.dev0