Tao Run On Virtualenv

What it does

When your agent uses it

• Docker-free local execution
• Plain-Python model scripts
• Fast HPO/AutoML trial smokes
• Hosts where containers are unavailable

Example prompts

• “run in my venv”
• “no docker”
• “virtualenv execution”
• “/tao-run-on-virtualenv”

Requirements

• Python 3
• Docker
• Compatibility (from SKILL.md): Requires a local Python virtualenv (pyvenv.cfg + bin/python) with the training script's dependencies installed. Linux is first-class (/proc); macOS works for smokes with documented caveats. No nvidia-tao-sdk, no docker.
• Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Author the spec (if the script takes one) at a local path — nested
2. 2Open the record — mints the id, binds results_dir BEFORE launch
3. 3Launch detached (the runner writes a durable wrapper that gates start,
4. 4Record RUNNING with the pid the runner printed

What it can do on your machine

Read from SKILL.md and the folder at commit dfdd080. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Bash

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python3

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • HF_TOKEN

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires a local Python virtualenv (pyvenv.cfg + bin/python) with the training script's dependencies installed. Linux is first-class (/proc); macOS works for smokes with documented caveats. No nvidia-tao-sdk, no docker.

  From compatibility in the SKILL.md frontmatter.

Context cost

Tao Run On Virtualenv loads about 1.5k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 149 tokens; SKILL.md has 492 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~149

When it runs · the whole SKILL.md, loaded when a task matches

~1.5k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~16k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:78

  set -a; source /path/to/.env; set +a   # omit if already exported

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 492 words, ~1,531 tokens.

name

tao-run-on-virtualenv

description

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".

allowed-tools

Read, Bash

compatibility

Requires a local Python virtualenv (pyvenv.cfg + bin/python) with the training script's dependencies installed. Linux is first-class (/proc); macOS works for smokes with documented caveats. No nvidia-tao-sdk, no docker.

license

Apache-2.0

metadata.author

NVIDIA Corporation

metadata.version

0.1.0

tags

platform, virtualenv, local

# 1. The venv is real and has an executable interpreter.
[ -f "$VENV/pyvenv.cfg" ] && [ -x "$VENV/bin/python" ] || echo "MISSING: $VENV is not a venv"
# 2. The script's top-level imports resolve inside it (catches wrong-venv early);
#    substitute the real modules your script imports.
"$VENV/bin/python" -c "import torch" || echo "MISSING: script dependency not in $VENV"
# 3. GPU visibility only if the script needs CUDA.
nvidia-smi >/dev/null 2>&1 || echo "note: no GPU visible (fine for CPU scripts)"

python3 "$RUNNER" status --job-dir "$RESULTS_DIR"   # {"status": "...", ...}

python3 "$RUNNER" logs --job-dir "$RESULTS_DIR" --tail 200

python3 "$RUNNER" cancel --job-dir "$RESULTS_DIR"
"$BANK/scripts/tao_job_record.py" mark "$JOB_ID" --state CANCELED --source agent

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (references) in skills/tao-run-on-virtualenv of NVIDIA/skills.

• SKILL.md
• BENCHMARK.md
• config/skillspector-baseline.yaml
• evals/evals.json
• references/skill_info.yaml
• references/tests/test_virtualenv_runner.py
• references/virtualenv_runner.py
• skill-card.md
• skill.oms.sig

Open the folder on GitHubat commit dfdd080