autodl-hpc

name

autodl-hpc

description

Prepare, validate, and gate AutoDL/HPC research experiments with GitHub deploy-key bootstrap, offline data policy, preflight and smoke gates, FileZilla/SFTP result transfer, and formal-run approval boundaries. Defaults to command preparation over autonomous SSH execution. Use when Codex needs AutoDL, /root/autodl-tmp, remote GPU/HPC smoke tests, preflight_autodl.py, run_autodl_smoke.sh, deploy keys, formal suite gating, or safe download/audit workflows.

AutoDL HPC

Capability Routing

This is a first-layer entry skill. Keep it loaded as the user-facing route; when a request needs a specialized capability below, resolve the debuffer repo root from .debuffer_skills/installed-skills-codex.txt (repo_root) when available, read the referenced library SKILL.md, then follow that skill. Do not copy the whole library skill into this file.

• /system-profile: read ../library/experiments/system-profile/SKILL.md.
• /training-check: read ../library/experiments/training-check/SKILL.md.
• /run-experiment: read ../library/experiments/run-experiment/SKILL.md.
• /analyze-results: read ../library/experiments/analyze-results/SKILL.md.
• /hpc-formal-run-gate: read ../library/experiments/hpc-formal-run-gate/SKILL.md.

Use this skill for AutoDL or similar SSH GPU/HPC machines where a research repo is cloned to a target machine, validated with preflight/smoke commands, and only then allowed to run formal experiment suites.

Read references/autodl-hpc.md before issuing commands, writing a runbook, or changing a repo's AutoDL/HPC workflow. For any nontrivial remote environment setup or rebuild, also read ../shared-references/compute-env-contract.md and use an env spec + hash ledger + smoke witness before declaring the machine ready.

Customized Pack Defaults

• Prefer command preparation over direct SSH execution. If the user has not explicitly approved remote execution, output the exact AutoDL command block and wait for pasted results.
• Keep local work lightweight: repo edits, tests, lint, config validation, manifest updates, and result audit. Heavy training belongs on AutoDL/HPC.
• Do not implement fully autonomous SSH workflows. SSH is a manual or explicitly approved boundary.
• Keep runbooks concise. Merge repeated operational notes into docs/runbooks/AUTODL_HPC_RUNBOOK.md instead of generating new long Markdown files for every attempt.
• Read ../shared-references/autosci-lite-patterns.md for the pilot gate and negative-memory rules. Formal suites require a passed pilot or an explicit waiver recorded in the runbook.

Workflow

1. Classify the current phase: bootstrap, data staging, setup, preflight, smoke, pilot gate, formal approval, formal execution, result download, or local audit.
2. Confirm the repo has the expected contracts before remote execution:
   • scripts/autodl_setup.sh
   • scripts/hpc/preflight_autodl.py
   • scripts/hpc/run_autodl_smoke.sh
   • scripts/run_experiment_suite.py
   • scripts/analysis/audit_run_bundles.py
   • experiments/suites/autodl_smoke.yaml
   • data/DATA_MANIFEST.md when large uploaded data exists
   • docs/runbooks/AUTODL_HPC_RUNBOOK.md or equivalent
3. Treat target-machine smoke as engineering validation only. Do not present smoke metrics as paper evidence or formal results.
4. Prepare a clean local package before touching AutoDL: syntax checks, suite dry-run, manifest generation, job-count inspection, then git commit and git push. Do not use the server as a development workspace.
5. Keep code sync Git-based: create a machine-specific deploy key on AutoDL, add the public key to GitHub, clone/pull with git pull --ff-only, and never copy a local private key to the server. For an existing server clone, preserve local server changes with git stash push -u before pulling.
6. Always make the remote Python path explicit in prepared AutoDL command blocks: export PATH=/root/miniconda3/bin:... and export PYTHON=/root/miniconda3/bin/python. Use $PYTHON in gate commands when possible.
7. Treat AutoDL as offline except for GitHub access. Required data must already be tracked or uploaded explicitly into expected data/raw/ or data/processed/ paths.
8. Run setup, preflight, dry-run, smoke, formal dry-run, and formal manifest generation before any formal execution. If setup changes Python/CUDA/packages/weights, update the compute environment ledger first and rerun the smoke witness.
9. Run or review a bounded pilot gate before formal suites. Pilot output stays under experiments/runs/pilot/... or another raw run path and is not paper evidence.
10. Require explicit user approval before enabling or running any formal suite. Dry-run formal suites first.
11. Launch long formal work only in a detached screen/tmux session with logs. If auto-shutdown is requested, use set -euo pipefail and place /sbin/shutdown -h now after the final success sentinel so failed runs preserve the machine for debugging.
12. Download raw run folders back under experiments/runs/...; do not copy them into experiments/results/ until local audit and result-to-claim scripts have passed.

Key Rules

• Do not hand-edit server code for formal experiments. Change locally, commit, push, then pull fast-forward on AutoDL.
• Do not start a long formal run directly from the SSH foreground shell.
• Do not auto-shutdown on failure. The shutdown command belongs after FORMAL_SUCCESS or an equivalent all-success sentinel.
• Do not upload the whole local repository with FileZilla/SFTP, and never sync .git/.
• Do not rely on runtime downloads from UCI, Kaggle, or other external data sites on AutoDL.
• Do not write smoke output into experiments/results/.
• Do not treat pilot output as paper evidence; use it only to decide whether formal runs are safe to launch.
• Do not edit a frozen protocol after seeing smoke or formal outputs unless making an explicit protocol revision.
• Stop at a failed gate and report the failed command, artifact path, and required fix.
• If pilot or smoke fails, update experiments/NEGATIVE_RESULTS.md with the run id, artifact path, failure reason, and decision.

Coordination

If the repo needs structural changes before AutoDL work, use the research-repo architecture conventions first: reusable code in src/, thin entrypoints in scripts/, suites in experiments/suites/, raw outputs in experiments/runs/, and curated outputs only in experiments/results/.