ml-execution-preflight

name	ml-execution-preflight
description	Checklist before long execution_run or execution_run_background for ML/training scripts.
always	true

ML execution preflight

Run these checks before a long or GPU-heavy execution_run / execution_run_background:

1. execution_env_info — note max_wall_secs, default timeout, provider id, and artifact caps.
2. Interpreter / kernel — for Jupyter or SSH, run a one-line execution_run that prints Python version and (if relevant) torch.cuda.is_available() or equivalent.
3. Timeouts — set timeout_secs explicitly from model size and hardware; training almost always needs a large value within max_wall_secs.
4. Pilot — run the smallest slice that still exercises the code path (few steps / tiny data), then scale.
5. Logging — prefer plain-text step logs the model can read from captured stdout (avoid hiding loss inside unreadable progress noise).
6. Outputs — decide where checkpoints and metrics will live (execution_artifact_list, sandbox paths, or copy-out plan). Ephemeral sandboxes lose data when torn down.
7. Single path first — do not launch many parallel heavy jobs until one configuration completes successfully.

If any step fails, fix the root cause before scaling.

**Before launching a fine-tuning or dataset generation script, also load long-running-execution with load_skill_instructions.