Furen Xiao
5e525c07c8
This commit introduces a new script, `setup_impact_konfai.sh`, which facilitates a one-time setup for the IMPACT registration pipeline (impact-reg-konfai). The script builds a self-contained shim that includes: - A glibc 2.35 loader and libraries extracted from the niftyreg:2.1.1-ubuntu Docker image. - A self-contained LibTorch 2.8.0 (cu128) library. - A wrapper script that runs the stock elastix-impact binary through the glibc 2.35 loader with the appropriate library paths. The setup ensures compatibility with hosts that do not meet the required glibc and LibTorch versions, allowing the pipeline to function correctly. The script also verifies the installation and provides usage instructions.
25 lines
No EOL
1.6 KiB
Markdown
25 lines
No EOL
1.6 KiB
Markdown
# AGENTS.md
|
|
|
|
## Environment
|
|
|
|
This project uses a conda environment named `longitudinal-313` (Python 3.13).
|
|
|
|
Activate with:
|
|
|
|
```bash
|
|
source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal-313
|
|
```
|
|
|
|
Key packages: torch 2.14 (+cu126), torchvision, monai 1.6 (pip, Pipeline C), numpy, scipy, pandas, scikit-learn, scikit-image, matplotlib, nibabel, SimpleITK. Registration toolkits (benchmark, see REGISTRATION_BENCHMARK.md): `itk` + `itk-elastix`, `antspyx`, niftyreg 1.5.69 (`conda install -c conda-forge niftyreg`, provides `reg_aladin`), `impact-reg-konfai` (pip; IMPACT — on this host the elastix binary needs the one-time shim from `scripts/setup_impact_konfai.sh`), plus the Docker image `freesurfer/synthmorph`.
|
|
|
|
## Project
|
|
|
|
Longitudinal (repeated-measures) analysis of medical imaging data. Repo is at an early stage — see README.md for any project notes.
|
|
|
|
**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).
|
|
|
|
## Conventions
|
|
|
|
- All Python commands must run inside the `longitudinal-313` conda environment.
|
|
- 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).
|
|
- No lint/test tooling is configured yet; run scripts directly with `python <script>` from the repo root. |