Python utilities for compliant Azure machine learning
Project description
Shrike: Compliant Azure ML Utilities
Compliant Machine Learning (sometimes also called eyes-off Machine Learning) is the practice of training, validating and deploying machine learning models without seeing the underlying private data. It is needed by many enterprises to satisfy the strict compliance and privacy guarantees that they provide to their customers.
The shrike library is a set of Python utilities for compliant machine
learning, with a special emphasis on running experiments in the
Azure Machine Learning platform (a.k.a. Azure ML). This
library contains three elements, which are:
shrike.compliant_logging: utlities 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.
Documentation
For the full documentation of shrike with detailed examples and API reference,
please see the docs page.
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 installwithout any extras:
pip install shrike
- If you are planning on signing and registering components, please
pip installwith[build]:
pip install shrike[build]
- If you are planning on submitting Azure ML pipelines, please
pip installwith[pipeline]:
pip install shrike[pipeline]
- If you would like to contribute to the source code, please
pip installwith all the dependencies:
pip install shrike[pipeline,build,dev]
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 aml-ds@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.
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.
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
Built Distribution
File details
Details for the file shrike-1.6.2.tar.gz.
File metadata
- Download URL: shrike-1.6.2.tar.gz
- Upload date:
- Size: 113.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.6.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | a42d30eabeff0d0e7932d45571cda1dee849965e062d70c6850fc4ce941ecaba |
|
| MD5 | a67516b5d7aa48c63a0cda58a3035077 |
|
| BLAKE2b-256 | 8ed03d0fc1ed04d1d82d5754f6b6eb0117542797e9bb71ff2408c6f13e247be9 |
File details
Details for the file shrike-1.6.2-py3-none-any.whl.
File metadata
- Download URL: shrike-1.6.2-py3-none-any.whl
- Upload date:
- Size: 121.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.6.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | fbe353bad4fb150e473e8980668c456c79ab63ef96ec505f8c8f076594ce6f71 |
|
| MD5 | 9a8187bc3a68d440541df269019b9c38 |
|
| BLAKE2b-256 | 0cafc389a634ec2de085c8e16cca44c8ccb219c3f3e249199e77ac6bafa19c7d |
