Agent skill · nvidia
tao-train-dinov3
DINOv3 continual self-supervised pre-training. Domain-adapts public DINOv3 ViT backbones
What it needs
About 4k tokens when loaded.
What this skill does
DINOv3 Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first. Use this skill to continue self-supervised training of a DINOv3 backbone on unlabeled images, then convert, export, or run inference with a trained teacher checkpoint. Quick Start (docker run) Docker-native launch — no TAO SDK and no Python on the host. Use the local Docker/platform skill instead when it gives a stricter environment-specific command (non-root UID mapping, cache redirects, remote daemons). Train: Inference: Export: Convert: Every action takes its spec with -e; resultsdir is set in the spec or overridden on the command line. Mount any pretrained-weights directory the spec references, and keep every in-container path consistent across actions. Configuration Use schemas/<action>.schema.json for supported fields, defaults, ranges, and options. Start from the matching references/spectemplate<action>.yaml. Training is AutoML-enabled. Read references/skillinfo.yaml and resolve an explicit automlpolicy or user request before training. Default to automlpolicy: on; requests such as "disable AutoML", "no HPO", or "plain training" mean off for that run. When it is on and the train schema and template are packaged, route training through tao-skill-bank:tao-run-automl. Use direct training only when the policy is off or those files are missing, and report the missing-file limitation. Non-train actions remain in this skill. Treat trainloss as an optimization signal; use downstream evaluation when selecting a checkpoint for deployment. For method background or tuning ideas, optionally read: references/dinov3-method.md references/dinov3-recipes.md Core workflow 1. Obtain a compatible DINOv3 checkpoint in timm or TAO format. 2. Point dataset.traindataset.imagesdir to a directory of unlabeled images. 3. Configure the backbone, training scale, and output location. 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-train-dinov3