Agent skill · nvidia
tao-run-on-virtualenv
Run a Python training/eval script directly in an existing local virtualenv — no docker, no container. Implements the four-verb consumer contract (submit/status/logs/cancel) over a vendored process-lifecycle runner with durable on-disk state, PID-reuse-safe identity, and process-group cleanup. Use for docker-free local execution, plain-Python model scripts, fast HPO/AutoML trial smokes, or hosts where containers are unavailable. Trigger phrases include "run in my venv", "no docker", "virtualenv execution", "local python training", "run this training script directly".
What it needs
About 4k tokens when loaded.
What this skill does
Virtualenv — docker-free local Python execution Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery). The virtualenv platform runs a Python script natively in an existing venv — as an argv vector whose first element is <venv>/bin/python, never through a shell, never activating anything. The vendored runner (references/virtualenvrunner.py) is this platform's "native CLI" — the role docker/kubectl/sbatch play elsewhere — and owns only the process lifecycle. Job records stay with taojobrecord.py; specs are authored by the agent, exactly like every other platform. When to use The workload is a plain Python script (its dependencies pip-installed in a venv), not a TAO container action. No docker on the host, or container startup cost isn't worth it (fast smokes, AutoML trial loops over lightweight models). Single node only. For TAO container actions use tao-run-on-docker; for clusters use -slurm / -kubernetes. Preflight No credentials are required by the platform itself; model-specific env vars (e.g. HFTOKEN) pass through by NAME with -e (values never land on argv). Storage Tier A by definition — everything is local paths. Datasets must already be on local disk (stage with tao-data-io first if they live in S3). Outputs land in the job record's resultsdir, which IS the runner's --job-dir. Execution — the four verbs $BANK = ${TAOSKILLBANKPATH}; $RUNNER = $BANK/skills/platform/tao-run-on-virtualenv/references/virtualenvrunner.py. submit 1. Author the spec (if the script takes one) at a local path — nested dicts, never flat dotted keys — and lint the assembled command with redactsecrets.py lint. 2. Open the record — mints the id, binds resultsdir BEFORE launch: 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-run-on-virtualenv