nv-generate-mr-brain-finetune

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: Show more Installs 532 Repository nvidia/skills GitHub Stars 1.3K First Seen May 31, 2026 Security Audits Gen Agent Trust Hub Pass Socket Pass Snyk Warn