feRay
Feature stores have unique needs. This is an opinionated framework for building and managing feature stores on top of:
Core Concepts:
- enable rapid experimentation and iteraton with memoization
- a feature can have sub-features (feature containers)
- data and code version are tracked on the record level
- orchestration of distributed compute incl. bootstrap of environments
This is the raw library - independent of any specific orchestrator.
Further reading material
TODO link docs here
- https://docs.dagster.io/guides/build/assets/asset-versioning-and-caching
- https://gafni.dev/projects/sanas-ai-dagster-ray/
- https://dagster.io/blog/unlocking-flexible-pipelines-customizing-asset-decorator
- https://www.youtube.com/watch?v=HPqQSR0BoUQ
- https://georgheiler.com/post/paas-as-implementation-detail/
- https://www.samsara.com/blog/building-a-modern-machine-learning-platform-with-ray
2 maintainers
Release files for feray 1.0.0
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| feray-1.0.0.tar.gz | 2.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| feray-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.6 kB
Release files / feray-1.0.0.tar.gz
| Download URL | feray-1.0.0.tar.gz |
|---|---|
| Size | 2.4 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Transparency logRelease files / feray-1.0.0-py3-none-any.whl
| Download URL | feray-1.0.0-py3-none-any.whl |
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| Size | 3.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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