For Tesera users: run the following commands below. First, make sure the scripts in dp-process-bin are in your path, e.g. by running PATH=/fullpath-to/dp-process-bin/:$PATH. Then, run the commands below:
`
eval "$(./config/config_env.sh)"
TAG=$VERSION build-container.sh
TAG=$VERSION deploy-container.sh
`
---
SageMaker Scikit-learn Container is an open source library for making the Scikit-learn framework run on Amazon SageMaker.
This repository also contains Dockerfiles which install this library, Scikit-learn, and dependencies for building SageMaker Scikit-learn images.
The SageMaker team uses this repository to build its official Scikit-learn 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 Scikit-learn image.
For information on running Scikit-learn jobs on SageMaker: SageMaker SKLearn Estimators and Models.
For notebook examples: SageMaker Notebook Examples.
Make sure you have installed all of the following prerequisites on your development machine:
- A Python environment management tool (e.g. PyEnv, VirtualEnv)
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 Scikit-learn 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.
The "base" Dockerfile encompass the installation of the framework and all of the dependencies needed.
Tagging scheme is based on <Scikit-learn_version>-<SageMaker_version>. (e.g. 1.2-1)
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-scikit-learn-container. # CPU docker build -t sklearn-base:<Scikit-learn_version>-<SageMaker_version> -f docker/<Scikit-learn_version>-<SageMaker_version>/base/Dockerfile.cpu .
# Example # CPU docker build -t sklearn-base:1.2-1 -f docker/1.2-1/base/Dockerfile.cpu .
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 sklearn-base:<Scikit-learn_version>-<SageMaker_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 Scikit-learn Container Python package. 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-scikit-learn-container. # CPU docker build -t <image_name>:<tag> -f docker/<Scikit-learn_version>-<SageMaker_version>/final/Dockerfile.cpu .
# Example # CPU docker build -t preprod-sklearn:1.2-1 -f docker/1.2-1/final/Dockerfile.cpu .
Running the tests requires installation of the SageMaker Scikit-learn Container code and its test dependencies.
git clone https://github.com/aws/sagemaker-scikit-learn-container.git cd sagemaker-scikit-learn-container pip install -e .[test]
Tests are defined in test/ and include unit, local integration, and SageMaker integration 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
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.
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 --docker-base-name <your_docker_image> \
--tag <your_docker_image_tag> \
--py-version <2_or_3> \
--framework-version <Scikit-learn_version>
# Example pytest test/integration --docker-base-name preprod-sklearn\--tag 1.2-1\--py-version 3\--framework-version 1.2-1
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:
- 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, 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-sklearn \
--instance-type ml.m4.xlarge \
--tag 1.0
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
SageMaker Scikit-learn 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/