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## DeepLabCut
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DeepLabCut is a scientific Python toolbox for markerless pose estimation.
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Prioritize correctness, preservation of user data, and public API compatibility.
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Prioritize correctness, preservation of user data, backward compatibility,
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reproducibility, and public API compatibility.
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DeepLabCut is PyTorch-first. TensorFlow is being deprecated, but legacy support
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remains. Do not introduce new TensorFlow-first functionality, and preserve
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existing TensorFlow compatibility unless a task explicitly changes its support
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status.
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DeepLabCut is PyTorch-first. Treat PyTorch as the primary backend for new
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functionality. Preserve legacy TensorFlow behavior where the current support
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policy still requires it, but do not introduce new TensorFlow-first
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functionality.
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## Guidelines
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- Treat aliases, deprecations, and default values as intentional user-facing
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behavior. Prefer aliases and explicit deprecation warnings over silently
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renaming or removing inputs.
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- Prefer `pathlib.Path`; do not assume POSIX paths. Isolate and document
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intentionally platform-specific behavior.
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- Prefer `pathlib.Path`; do not assume POSIX paths or that `Path.resolve()` is
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universally safe across filesystems. Isolate and document intentionally
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platform-specific behavior.
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- Preserve optional dependency boundaries. Do not assume that a GUI,
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TensorFlow, CUDA, a GPU, or a display is available.
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- Avoid heavy or backend-specific imports at package import time.
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identities, and project metadata unless the task explicitly requires
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changing them.
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- Use repository tooling where available. Treat repository source, tests, and documentation as primary evidence. When they disagree, identify the inconsistency rather than silently choosing one. When available, use `llms.txt`,
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`knowledge/manifest.json`, `docs.jsonl`, and `api.jsonl` as discovery indexes.
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