digits-api-ml is a large suite of API endpoints that directly respond with the inference of a given machine learning function.

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📝 Code Properties ✨

The below stack describes a static request->response oriented ML API

📁 Library	⚙ Purpose	📎 Version
Python	Base	3.10.8
transformers	Machine Learning	4.23.1
AutoDD	Social Media Analysis	2.1.4

You will want to know about each of these in depth by the above order.

🎬 Environment ✨

There is presently no dynamic configuration, this is due to the way the script initalizes and thusfar there is no need for private or consumer keys.

pip install -r requirements.txt

📜 Provided Scripts ✨

Digits AI provides some useful scripts to help you quick start.

Requirements provided in requirements.txt.

💡 start

uvicorn main:app --reload --port 7000 --host=127.0.0.1

🏰 Service Mesh API ✨

digits-api-ml is responsible for all machine learning pre- and post-processing.

• 🔌 Interfacing [ http ]
  • All API routing is done through http requests. It's possible sockets play a role one day.
• 🩺 Monitoring [ http ]
  • This API intrinsically reports to digits-api-main
• 🧮 Preprocessing [ python ]
  • Tremendous processing occurs through this API.

💎 Goals ✨

immediate

• Initialize beautiful README.md
• Add queue/jobs!
• Describe service API role
• Add husky pre-commit
• Create mesh diagram
• Explain preprocessor philosophy
• Contextualize hardware requirements

long-term

• Docker images
• Kubernetes deployment

🙋 Contribution

Proper commit message format is required for automated changelog generation. Examples:

[<emoji>] [revert: ?]<type>[(scope)?]: <message>

💥 feat(compiler): add 'comments' option
🐛 fix(compiler): fix some bug
📝 docs(compiler): add some docs
🌷 UI(compiler): better styles
🏰 chore(compiler): Made some changes to the scaffolding
🌐 locale(compiler): Made a small contribution to internationalization

Other commit types: refactor, perf, workflow, build, CI, typos, tests, types, wip, release, dep