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SageMaker

SageMaker Python SDK

Latest Version Supported Python Versions Code style: black Documentation Status

SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well.

For detailed documentation, including the API reference, see Read the Docs.

Table of Contents

  1. Installing SageMaker Python SDK
  2. Using the SageMaker Python SDK
  3. Using MXNet
  4. Using TensorFlow
  5. Using Chainer
  6. Using PyTorch
  7. Using Scikit-learn
  8. Using XGBoost
  9. SageMaker Reinforcement Learning Estimators
  10. SageMaker SparkML Serving
  11. Amazon SageMaker Built-in Algorithm Estimators
  12. Using SageMaker AlgorithmEstimators
  13. Consuming SageMaker Model Packages
  14. BYO Docker Containers with SageMaker Estimators
  15. SageMaker Automatic Model Tuning
  16. SageMaker Batch Transform
  17. Secure Training and Inference with VPC
  18. BYO Model
  19. Inference Pipelines
  20. Amazon SageMaker Operators in Apache Airflow
  21. SageMaker Autopilot
  22. Model Monitoring
  23. SageMaker Debugger
  24. SageMaker Processing

Installing the SageMaker Python SDK

The SageMaker Python SDK is built to PyPI and can be installed with pip as follows:

pip install sagemaker

You can install from source by cloning this repository and running a pip install command in the root directory of the repository:

git clone https://github.com/aws/sagemaker-python-sdk.git
cd sagemaker-python-sdk
pip install .

Supported Operating Systems

SageMaker Python SDK supports Unix/Linux and Mac.

Supported Python Versions

SageMaker Python SDK is tested on:

  • Python 3.8
  • Python 3.9
  • Python 3.10

AWS Permissions

As a managed service, Amazon SageMaker performs operations on your behalf on the AWS hardware that is managed by Amazon SageMaker. Amazon SageMaker can perform only operations that the user permits. You can read more about which permissions are necessary in the AWS Documentation.

The SageMaker Python SDK should not require any additional permissions aside from what is required for using SageMaker. However, if you are using an IAM role with a path in it, you should grant permission for iam:GetRole.

Licensing

SageMaker Python SDK is licensed under the Apache 2.0 License. It is copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at: http://aws.amazon.com/apache2.0/

Running tests

SageMaker Python SDK has unit tests and integration tests.

You can install the libraries needed to run the tests by running pip install --upgrade .[test] or, for Zsh users: pip install --upgrade .\[test\]

Unit tests

We run unit tests with tox, which is a program that lets you run unit tests for multiple Python versions, and also make sure the code fits our style guidelines. We run tox with all of our supported Python versions, so to run unit tests with the same configuration we do, you need to have interpreters for those Python versions installed.

To run the unit tests with tox, run:

tox tests/unit

Integration tests

To run the integration tests, the following prerequisites must be met

  1. AWS account credentials are available in the environment for the boto3 client to use.
  2. The AWS account has an IAM role named SageMakerRole. It should have the AmazonSageMakerFullAccess policy attached as well as a policy with the necessary permissions to use Elastic Inference.
  3. To run remote_function tests, dummy ecr repo should be created. It can be created by running -
    aws ecr create-repository --repository-name remote-function-dummy-container

We recommend selectively running just those integration tests you'd like to run. You can filter by individual test function names with:

tox -- -k 'test_i_care_about'

You can also run all of the integration tests by running the following command, which runs them in sequence, which may take a while:

tox -- tests/integ

You can also run them in parallel:

tox -- -n auto tests/integ

Git Hooks

to enable all git hooks in the .githooks directory, run these commands in the repository directory:

find .git/hooks -type l -exec rm {} \;
find .githooks -type f -exec ln -sf ../../{} .git/hooks/ \;

To enable an individual git hook, simply move it from the .githooks/ directory to the .git/hooks/ directory.

Building Sphinx docs

Setup a Python environment, and install the dependencies listed in doc/requirements.txt:

# conda
conda create -n sagemaker python=3.7
conda activate sagemaker
conda install sphinx=3.1.1 sphinx_rtd_theme=0.5.0

# pip
pip install -r doc/requirements.txt

Clone/fork the repo, and install your local version:

pip install --upgrade .

Then cd into the sagemaker-python-sdk/doc directory and run:

make html

You can edit the templates for any of the pages in the docs by editing the .rst files in the doc directory and then running make html again.

Preview the site with a Python web server:

cd _build/html
python -m http.server 8000

View the website by visiting http://localhost:8000

SageMaker SparkML Serving

With SageMaker SparkML Serving, you can now perform predictions against a SparkML Model in SageMaker. In order to host a SparkML model in SageMaker, it should be serialized with MLeap library.

For more information on MLeap, see https://github.com/combust/mleap .

Supported major version of Spark: 3.3 (MLeap version - 0.20.0)

Here is an example on how to create an instance of SparkMLModel class and use deploy() method to create an endpoint which can be used to perform prediction against your trained SparkML Model.

sparkml_model = SparkMLModel(model_data='s3://path/to/model.tar.gz', env={'SAGEMAKER_SPARKML_SCHEMA': schema})
model_name = 'sparkml-model'
endpoint_name = 'sparkml-endpoint'
predictor = sparkml_model.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge', endpoint_name=endpoint_name)

Once the model is deployed, we can invoke the endpoint with a CSV payload like this:

payload = 'field_1,field_2,field_3,field_4,field_5'
predictor.predict(payload)

For more information about the different content-type and Accept formats as well as the structure of the schema that SageMaker SparkML Serving recognizes, please see SageMaker SparkML Serving Container.