NV-Generate-MR-Brain-Finetune

Purpose

• Used for finetuning the NV-Generate-CTMR rflow-mr-brain diffusion UNet from user-supplied NIfTI training volumes.
• Not for clinical interpretation, regulatory use, or approving synthetic data for production training.
• The wrapper stages the config glue locally and delegates execution to existing upstream scripts: scripts.diff_model_create_training_data, scripts.diff_model_train, and optionally scripts.diff_model_infer. It does not execute the notebook.
• Manifest I/O: inputs are datalist and data_base_dir; outputs are finetuned_checkpoint, optional inference_outputs, and result_json.
• The underlying training contract is the upstream config/env JSON (the same one driven from cell [10] of train_diff_unet_tutorial.ipynb). The wrapper stages those JSON files for you and exposes the most-tuned fields as CLI flags; the sections below document the fields, their defaults, and how to monitor/tune a run.

Instructions

• Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
• Run scripts/run_mr_brain_finetune.py from the Medical AI Skills repo root.
• If a host agent exposes run_script, use run_script("scripts/run_mr_brain_finetune.py", args=[...]); otherwise run the Bash/Python command below.
• Use --preflight first when checking a new datalist; remove --preflight only when the user explicitly wants to launch GPU finetuning.
• For a staged preflight input bundle directory, use BUNDLE/preflight_datalist.json as the datalist and BUNDLE/preflight_dataset as --data-base-dir when those files are present.

Examples

Validate and stage a preflight finetune check from an input bundle (the recommended first step — no GPU, no training). This is the single canonical command; replace INPUT_BUNDLE and OUT_DIR with your paths: