Jason RasmussenandCodex
11900d5a8c
* chore: add CLAUDE.md * chore: pr feedback * chore: contributing guideline rules * chore: pr feedback (cherry picked from commit fc30140e6ec313c94405e7ca4fbab8096dc1c6c7)
15 lines
1.1 KiB
Markdown
15 lines
1.1 KiB
Markdown
# machine-learning/AGENTS.md
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Python 3.12 FastAPI service running ONNX models for CLIP, facial recognition and OCR, managed with uv.
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## Rules
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- Install with `uv sync --extra cpu`; run tools through `uv run` or the mise tasks.
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- `mise //machine-learning:check` is `mypy --strict`, and ruff enforces line length 120. Code must pass both.
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- `immich_ml/main.py` is the app (`/predict`, `/ping`); `pipeline.py` composes the Clip, FacialRecognition and Ocr pipelines.
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## Skills
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- **Add or change a model**: models live in `immich_ml/models/{clip,facial_recognition,ocr}/` and extend `InferenceModel` in `models/base.py`, which downloads weights with Hugging Face `snapshot_download` into the cache folder. Loaded models are kept in `models/cache.py` `ModelCache`, keyed by class, name and graph, with TTL eviction. Execution backends (ONNX Runtime, RKNN, Arm NN) are in `immich_ml/sessions/`.
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- **Run tests**: everything is in `test_main.py`. One class with `uv run pytest test_main.py -k TestCache`, one test with `test_main.py::TestCLIP::test_name`. `mise //machine-learning:test-full` downloads real models and runs the prediction endpoint tests.
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