25 lines
No EOL
1.3 KiB
Markdown
25 lines
No EOL
1.3 KiB
Markdown
# AGENTS.md
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## Environment
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This project uses a conda environment named `longitudinal` (Python 3.14).
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Activate with:
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```bash
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source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal
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```
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Key packages: torch 2.14 (+cu126), torchvision, monai 1.6 (pip, Pipeline C), numpy, scipy, pandas, scikit-learn, scikit-image, matplotlib, nibabel, SimpleITK.
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## Project
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Longitudinal (repeated-measures) analysis of medical imaging data. Repo is at an early stage — see README.md for any project notes.
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**All path config lives in `config/paths.json`** — the external data roots the code reads or writes (`data`, `lee`, `m6`, `ntuh_register_inv`). Code resolves them through `src.common` (`PATHS` dict, `path(name)` lookup, `DATA`/`d()` for `data/...` paths). To move any dataset, only edit `config/paths.json`; the repo root is derived from the code location (override with `LONGITUDINAL_ROOT` if needed).
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## Conventions
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- All Python commands must run inside the `longitudinal` conda environment.
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- GPU is available (CUDA 12.6); use `torch.device('cuda')` when appropriate. Multi-GPU stages of Pipelines A and C run via `torchrun --standalone --nproc_per_node N` (rank = GPU).
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- No lint/test tooling is configured yet; run scripts directly with `python <script>` from the repo root. |