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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 <https://sagemaker.readthedocs.io>_.
Installing SageMaker Python SDK <#installing-the-sagemaker-python-sdk>__Using the SageMaker Python SDK <https://sagemaker.readthedocs.io/en/stable/overview.html>__Using MXNet <https://sagemaker.readthedocs.io/en/stable/using_mxnet.html>__Using TensorFlow <https://sagemaker.readthedocs.io/en/stable/using_tf.html>__Using Chainer <https://sagemaker.readthedocs.io/en/stable/using_chainer.html>__Using PyTorch <https://sagemaker.readthedocs.io/en/stable/using_pytorch.html>__Using Scikit-learn <https://sagemaker.readthedocs.io/en/stable/using_sklearn.html>__Using XGBoost <https://sagemaker.readthedocs.io/en/stable/using_xgboost.html>__SageMaker Reinforcement Learning Estimators <https://sagemaker.readthedocs.io/en/stable/using_rl.html>__SageMaker SparkML Serving <#sagemaker-sparkml-serving>__Amazon SageMaker Built-in Algorithm Estimators <src/sagemaker/amazon/README.rst>__Using SageMaker AlgorithmEstimators <https://sagemaker.readthedocs.io/en/stable/overview.html#using-sagemaker-algorithmestimators>__Consuming SageMaker Model Packages <https://sagemaker.readthedocs.io/en/stable/overview.html#consuming-sagemaker-model-packages>__BYO Docker Containers with SageMaker Estimators <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-docker-containers-with-sagemaker-estimators>__SageMaker Automatic Model Tuning <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-automatic-model-tuning>__SageMaker Batch Transform <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-batch-transform>__Secure Training and Inference with VPC <https://sagemaker.readthedocs.io/en/stable/overview.html#secure-training-and-inference-with-vpc>__BYO Model <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-model>__Inference Pipelines <https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines>__Amazon SageMaker Operators in Apache Airflow <https://sagemaker.readthedocs.io/en/stable/using_workflow.html>__SageMaker Autopilot <src/sagemaker/automl/README.rst>__Model Monitoring <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html>__SageMaker Debugger <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html>__SageMaker Processing <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html>__The SageMaker Python SDK is built to PyPI and the latest version of the SageMaker Python SDK can be installed with pip as follows ::
pip install sagemaker==<Latest version from pyPI from https://pypi.org/project/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 VersionsSageMaker Python SDK is tested on:
Telemetry
The ``sagemaker`` library has telemetry enabled to help us better understand user needs, diagnose issues, and deliver new features. This telemetry tracks the usage of various SageMaker functions.
If you prefer to opt out of telemetry, you can easily do so by setting the ``TelemetryOptOut`` parameter to ``true`` in the SDK defaults configuration. For detailed instructions, please visit `Configuring and using defaults with the SageMaker Python SDK <https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk>`__.
AWS PermissionsAs 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 <https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html>__.
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 testsSageMaker Python SDK has unit tests and integration tests.
You can install the libraries needed to run the tests by running :code:pip install --upgrade .[test] or, for Zsh users: :code: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 <#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/unitIntegration tests
To run the integration tests, the following prerequisites must be met
SageMakerRole.
It should have the AmazonSageMakerFullAccess policy attached as well as a policy with the necessary permissions to use Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei-setup.html>__.aws ecr create-repository --repository-name remote-function-dummy-containerWe 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/integYou can also run them in parallel:
::
tox -- -n auto tests/integGit 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 docsSetup 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.txtClone/fork the repo, and install your local version:
::
pip install --upgrade .Then cd into the sagemaker-python-sdk/doc directory and run:
::
make htmlYou 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 8000View the website by visiting http://localhost:8000
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.
.. code:: python
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:
.. code:: python
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_.
.. _SageMaker SparkML Serving Container: https://github.com/aws/sagemaker-sparkml-serving-container