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Get Started Free →nvidia/nv-generate-mr-brain-finetune
Used for finetuning NV-Generate-CTMR MR-brain diffusion UNet from a NIfTI datalist. Not for clinical or production data approval.
Passed 19 of 20 test cases with the skill, and 4 of 20 without it.
and 25 more in the full report
17 fixed4 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
If NV_GENERATE_ROOT is not explicitly set in the user's environment, where does the MR brain fine-tuning script automatically search for the default NV-Generate-CTMR repository checkout?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies .workbench_data/upstreams/NV-Generate-CTMR as the fallback directory path when NV_GENERATE_ROOT is unset. | Missed | Passed |
The instructions your model reads when the skill loads.
rflow-mr-brain diffusion UNet from user-supplied NIfTI training volumes.scripts.diff_model_create_training_data, scripts.diff_model_train, and optionally scripts.diff_model_infer. It does not execute the notebook.datalist and data_base_dir; outputs are finetuned_checkpoint, optional inference_outputs, and result_json.[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.skill_manifest.yaml before changing arguments, side effects, or validation gates.scripts/run_mr_brain_finetune.py from the Medical AI Skills repo root.run_script, use run_script("scripts/run_mr_brain_finetune.py", args=[...]); otherwise run the Bash/Python command below.--preflight first when checking a new datalist; remove --preflight only when the user explicitly wants to launch GPU finetuning.BUNDLE/preflight_datalist.json as the datalist and BUNDLE/preflight_dataset as --data-base-dir when those files are present.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:
export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7}" && \
python skills/nv-generate-mr-brain-finetune/scripts/run_mr_brain_finetune.py \
INPUT_BUNDLE/preflight_datalist.json \
--data-base-dir INPUT_BUNDLE/preflight_dataset \
--output-dir OUT_DIR \
--modality mri_t1 \
--preflight
For real GPU finetuning and other variations, see Usage below.
| Script | Purpose | Arguments |
|---|---|---|
scripts/run_mr_brain_finetune.py | Primary entrypoint declared by skill_manifest.yaml. | DATALIST.json --data-base-dir DATA_DIR --output-dir OUT_DIR [--epochs N] [--modality mri_t1] [--num-gpus N] [--no-amp] [--model-config FILE] [--run-inference] [--preflight] |
NV_GENERATE_ROOT may point to the caller's local checkout and
must contain scripts/diff_model_create_training_data.py,test cases passed, vs 4 / 20 without
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