nv-generate-vae-finetune

NV-Generate-VAE-Finetune Purpose Used for finetuning the NV-Generate-CTMR MAISI VAE/autoencoder from user-supplied CT or MRI NIfTI training volumes. Not for clinical interpretation, regulatory use, or approving synthetic data for production training. Upstream currently documents VAE training in train_vae_tutorial.ipynb and provides configs/helpers, but not a scripts.train_vae CLI. This skill does not execute the notebook; it stages the required config/datalist glue locally and uses upstream helper APIs. Manifest I/O: inputs are datalist and data_base_dir ; outputs are autoencoder_checkpoint , discriminator_checkpoint , and result_json . The underlying training contract is the upstream config/env JSON ( config_maisi_vae_train.json + environment_maisi_vae_train.json , as used in train_vae_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_vae_finetune.py from the Medical AI Skills repo root. If a host agent exposes run_script , use run_script("scripts/run_vae_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 534 Repository nvidia/skills GitHub Stars 1.3K First Seen May 31, 2026 Security Audits Gen Agent Trust Hub Pass Socket Pass Snyk Pass