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:

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-.workbench_data/upstreams/NV-Generate-CTMR}" && \
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.

Available Scripts

Prerequisites

• NV_GENERATE_ROOT may point to a current checkout of https://github.com/NVIDIA-Medtech/NV-Generate-CTMR containing scripts/diff_model_create_training_data.py, scripts/diff_model_train.py, and scripts/diff_model_infer.py.
• If NV_GENERATE_ROOT is unset, the wrapper searches .workbench_data/upstreams/NV-Generate-CTMR.
• CUDA_VISIBLE_DEVICES is optional and can be used to select the GPU for real training.
• Runtime requirements: NVIDIA CUDA GPU for real training, Python packages from the upstream requirements.txt, and downloaded MR-brain weights.
• Side effects: writes staged configs, embeddings, checkpoints, optional inference images, and logs under the caller-provided --output-dir; may write model caches under the upstream checkout and ~/.cache/huggingface/; may contact https://huggingface.co for model assets and https://github.com for the upstream checkout.
• The datalist is a MONAI-style JSON object with training[].image paths relative to --data-base-dir. training[].modality is optional and defaults to mri_t1.

1. Config and environment JSON (adapt to your data)

This is a thin wrapper around the upstream train_diff_unet_tutorial.ipynb flow. Each run performs four steps, delegating the heavy lifting to the model author's scripts:

1. Stage configs — copy the three config JSONs and rewrite only the run-specific paths and n_epochs (notebook cell 15).
2. python -m scripts.diff_model_create_training_data → latent *_emb.nii.gz embeddings (cell 17).
3. Write embedding sidecars — a <emb>.nii.gz.json per embedding with spacing/modality (and body-region indices when the model uses them). This is the one piece of glue that lives in the notebook (cell 19), not in upstream scripts/, and diff_model_train requires it; the skill owns it.
4. python -m scripts.diff_model_train (cell 21), optionally python -m scripts.diff_model_infer.

Tune by editing the config JSON, not by adding flags. All training/inference hyperparameters (lr, batch_size, cache_rate, inference dim/spacing/num_inference_steps/cfg_guidance_scale, …) live in config_maisi_diff_model_rflow-mr-brain.json. Edit the upstream copy, or pass your own with --model-config FILE (and --env-config / --model-def for the other two). The wrapper only ever rewrites the fields below.

Environment JSON (environment_maisi_diff_model_rflow-mr-brain.json) — fields the wrapper rewrites per run:

Model config (config_maisi_diff_model_rflow-mr-brain.json) — the only fields the wrapper touches:

Everything else in that file (lr, batch_size, cache_rate, the rest of diffusion_unet_inference) is left exactly as written — edit the JSON to change it.

Runtime flags (not config fields): --num-gpus N (>1 launches torch.distributed.run), --no-amp (disable mixed precision, passed through to diff_model_train).

--modality selects the integer code from configs/modality_mapping.json. Supported brain values: mri (8), mri_t1 (9, default), mri_t2 (10), mri_flair (11), mri_swi (20), and their *_skull_stripped variants (29/30/31/32). Per-case training[].modality overrides --modality. The modality also feeds the step-3 embedding sidecars.

For an end-to-end reference including example data download and checkpoint loading, see the upstream tutorial train_diff_unet_tutorial.ipynb.

2. Usage (one-line training)

Preflight only:

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-.workbench_data/upstreams/NV-Generate-CTMR}" && \
python skills/nv-generate-mr-brain-finetune/scripts/run_mr_brain_finetune.py \
  PATH_TO_DATALIST.json \
  --data-base-dir PATH_TO_DATA_ROOT \
  --output-dir runs/nv_generate_mr_brain_finetune_preflight \
  --preflight

Preflight bundle input:

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-.workbench_data/upstreams/NV-Generate-CTMR}" && \
python skills/nv-generate-mr-brain-finetune/scripts/run_mr_brain_finetune.py \
  PATH_TO_INPUT_BUNDLE/preflight_datalist.json \
  --data-base-dir PATH_TO_INPUT_BUNDLE/preflight_dataset \
  --output-dir runs/nv_generate_mr_brain_finetune_preflight \
  --preflight

GPU finetuning:

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-.workbench_data/upstreams/NV-Generate-CTMR}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-mr-brain-finetune/scripts/run_mr_brain_finetune.py \
  PATH_TO_DATALIST.json \
  --data-base-dir PATH_TO_DATA_ROOT \
  --output-dir runs/nv_generate_mr_brain_finetune \
  --epochs 2 \
  --modality mri_t1 \
  --run-inference

Replace PATH_TO_DATALIST.json and PATH_TO_DATA_ROOT with the user's actual paths. Do not use the fixture datalist for real training; it is a preflight-only placeholder.

3. Monitor training (TensorBoard)

scripts.diff_model_train writes TensorBoard event files under the staged model_dir (OUT_DIR/artifacts/models). Launch TensorBoard against the output directory and watch the loss curve:

python -m pip install tensorboard && \
tensorboard --logdir runs/nv_generate_mr_brain_finetune/artifacts

The run summary is written to OUT_DIR/artifacts/workflow_summary.json (checkpoint path, embedding sidecars, inference outputs); the JSON the wrapper prints to stdout mirrors the same paths plus exit_code and a stderr_tail for quick triage.

4. Hyperparameter tuning and common pitfalls

• Loss not decreasing / unstable — lower diffusion_unet_train.lr (default 1e-5) in the model-config JSON, or keep AMP on (default); --no-amp is slower but more numerically stable on older GPUs.
• Out-of-memory — keep diffusion_unet_train.batch_size at 1 and cache_rate at 0 in the config JSON, and confirm the autoencoder/UNet fit your GPU before scaling. Multi-GPU (--num-gpus N) shards the batch via torch.distributed.run.
• Few cases / quick check — keep --epochs small (the wrapper default 2 is for verification, not convergence; the upstream config ships 1000).
• Wrong modality conditioning — set --modality or per-case training[].modality to a value present in configs/modality_mapping.json; a mismatch produces a clear error rather than silently mislabeling latents.
• Slow startup on first run — diff_model_create_training_data precomputes latent embeddings once; reuse the same --output-dir to avoid recomputing them.

5. Evaluate the finetuned model

Use the staged checkpoint (OUT_DIR/artifacts/models/<model_filename>) as the diffusion UNet for generation, then inspect the synthesized volumes:

• Pass --run-inference here for a quick built-in sanity render, or
• Point the nv-generate-mr-brain inference skill at the finetuned checkpoint to generate fresh brain MRI volumes for qualitative review.

This skill gates file accounting and command provenance only — anatomical realism and downstream utility must be judged by a domain expert on the generated images.

Limitations

• Requires a current upstream NV-Generate-CTMR checkout with the existing diffusion training scripts. The skill itself stages the required config and datalist glue locally and does not depend on the notebook or PR #33.
• Full training can be expensive and is not deterministic across hardware, CUDA, and package versions.
• The wrapper gates file accounting and command provenance, not anatomical realism or downstream model utility.
• Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission, or production training-data approval.

Troubleshooting