.env.example

# MRI Preprocessing Deployment Configuration

# Required paths (data volumes only — code is baked into the container)
DATA_DIRECTORY_PATH=/path/to/raw/dicom
NIFTI_DIRECTORY_PATH=/path/output/nifti
RAS_DIRECTORY_PATH=/path/output/ras
COREG_DIRECTORY_PATH=/path/output/coreg
INPUTS_DIRECTORY_PATH=/path/output/inputs

# Container / runtime settings
COMPOSE_PROJECT_NAME=mri_control
CONTAINER_NAME=control
CONDA_ENV_NAME=mri_preproc

# Registry configuration (for Forgejo / self-hosted OCI registry on HPC)
# Leave REGISTRY_URL blank for plain Docker use (local build-and-run).
REGISTRY_URL=
IMAGE_REPOSITORY=nickl99/preprocessing
# Image variant:
#   latest      → CPU-only reg_f3d (default; runs on any node, no GPU needed)
#   latest-gpu  → CUDA-linked reg_f3d (requires a GPU node; start_control.sh
#                 adds --nv automatically when nvidia-smi is available)
# For stable runs pin to the release tag you deployed with, e.g. v1.0.0 / v1.0.0-gpu
IMAGE_TAG=latest
# Docker builds only (docker-compose): set to ON for a local CUDA-linked image
# USE_CUDA=OFF

# Local .sif cache location for Singularity/Apptainer (pulled from the
# registry reference above when missing), e.g. ./control_system/mri.sif
SIF_PATH=./control_system/mri_preprocessing.sif

# HPC (Singularity/Apptainer) note: the .sif is a read-only squashfs, so the
# five *output* paths above (nifti/RAS/coreg/inputs) plus the data base and the
# log dir MUST each be backed by a writable host directory. start_control.sh
# handles this automatically for you — it creates ./mri_data_base as the
# writable base for /FL_system/data (step-03 pickles + Data_table*.csv + tmp
# scratch) and mounts it first, then overlays the five per-purpose dirs on top.
# So on HPC you typically need nothing extra here. If you launch the .sif by
# hand instead, bind a writable dir per in-container path (see README,
# "HPC / Singularity-Apptainer Path Contract"); the pipeline will otherwise
# fail with a readable error that names the exact --bind to add.