Skip to main content

Python utilities for compliant Azure machine learning

Project description

Shrike: incubation for Azure ML

CodeQL docs python Component Governance Python versions code style: black codecov PyPI - Downloads PyPI version license: MIT

The shrike library is a set of Python utilities for running experiments in the Azure Machine Learning platform (a.k.a. Azure ML). This library contains four elements, which are:

  • shrike.compliant_logging: utilities for compliant logging and exception handling;
  • shrike.pipeline: helper code for managing, validating and submitting Azure ML pipelines based on azure-ml-component (a.k.a. the Component SDK);
  • shrike.build: helper code for packaging, building, validating, signing and registering Azure ML components.
  • shrike.spark: utilities for running jobs, especially those leveraging Spark .NET, in HDInsight and later Synapse.

Documentation

For the full documentation of shrike with detailed examples and API reference, please see the docs page.

For a list of problems (along with guidance and solutions) designed specifically to help you learn how to use shrike, please refer to the information in this README file (located in another GitHub repository).

Installation

The shrike library is publicly available in PyPi. There are three optional extra dependencies: pipeline, build, and dev. The pipeline dependency is for submitting Azure ML pipelines, build is for signing and registering components, and dev is for the development environment of shrike.

  • If you are only planning on using the compliant-logging feature, please pip install without any extras:
pip install shrike
  • If you are planning on signing and registering components, please pip install with [build]:
pip install shrike[build]
  • If you are planning on submitting Azure ML pipelines, please pip install with [pipeline]:
pip install shrike[pipeline]
  • If you would like to contribute to the source code, please pip install with all the dependencies:
pip install shrike[pipeline,build,dev]

Alternatively, for local development, you may use the Conda environment defined in environment.yml. It pins the appropriate versions of pip, Python, and installs all shrike together with all extras as an editable package.

:warning: If you are using a ZSH terminal, please consider adding quotes, e.g., pip install "shrike[pipeline,build,dev]" to avoid the accidental shell expansion.

Migration from aml-build-tooling, aml-ds-pipeline-contrib, and confidential-ml-utils

If you have been using the aml-build-tooling, aml-ds-pipeline-contrib, or confidential-ml-utils libraries, please use the migration script (migration.py) to convert your repo or files and adopt the shrike package with one simple command:

python migraton.py --input_path PATH/TO/YOUR/REPO/OR/FILE

:warning: This command will update files in-place. Please make a copy of your repo/file if you do not want to do so.

Need Support?

If you have any feature requests, technical questions, or find any bugs, please do not hesitate to reach out to us.

  • For bug reports and feature requests, you are welcome to open an issue.
  • If you are a Microsoft employee, please refer to the support page for details;
  • If you are outside Microsoft, please send an email to aims-team@microsoft.com.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

To contribute, please start by creating a self-assigned issue giving a high-level overview of what you'd like to do. Once any discussion there concludes, follow up with a PR.

Please send an email to aims-team@microsoft.com to request a "contributor" access to this repository, if you have difficulty in creating a branch. When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

shrike-1.23.0.tar.gz (164.4 kB view details)

Uploaded Source

Built Distribution

shrike-1.23.0-py3-none-any.whl (178.0 kB view details)

Uploaded Python 3

File details

Details for the file shrike-1.23.0.tar.gz.

File metadata

  • Download URL: shrike-1.23.0.tar.gz
  • Upload date:
  • Size: 164.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.13

File hashes

Hashes for shrike-1.23.0.tar.gz
Algorithm Hash digest
SHA256 86b09be0877a8ba44e15d9e8e101abef0a8bf000c8d57f00202dad34ce15de41
MD5 6d8c034b7a47893898ca70ea308ee382
BLAKE2b-256 fa0ab304c114cfa3f436f7fd4de75865c2da9bdfae9a704ccb9f18f8b0d0b4d8

See more details on using hashes here.

File details

Details for the file shrike-1.23.0-py3-none-any.whl.

File metadata

  • Download URL: shrike-1.23.0-py3-none-any.whl
  • Upload date:
  • Size: 178.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.13

File hashes

Hashes for shrike-1.23.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d98cdb0c9e2ad5e2348f2f693ed0366d502cbb47cd12c22dadf1fa02e320253d
MD5 19460a8bc27a579bdc3795f4917488c6
BLAKE2b-256 e5b53dad28d4e7956fb7f42d42c9b2bded1dd150b7b81328ae6535ad1d2f377c

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page