autohpc

Use immediately before moving or publishing a checkpoint, and after downloading it, to generate or verify a lightweight SHA-256 file manifest.

Use before running a checkpoint on a robot or inference rig to audit the real sensor-to-action chain using saved model assets, runner code, and live or replay samples.

Use when evaluating checkpoints to maintain a persistent log of what was evaluated, the metrics, qualitative assessment, and verdict.

Use before committing to a full training run to reconcile a base model's baked-in expectations against the training config and the actual dataset, catching semantic mismatches (normalization, feature order, units, encoding) that build and run fine but silently ruin results.

Use when submitting, monitoring, or reviewing HPC training runs to maintain a persistent log of each run's config, status, results, and next steps.

Use when running ML training workflows on Slurm-based HPC clusters, including environment setup, data and image transfer, job submission, monitoring, debugging, and storage cleanup, especially when notes are ad-hoc, usernames differ, or commands may expose secrets.

Use when preparing cluster-ready containers by validating a repo locally with Docker and promoting the result into the container artifact or runtime required by the target cluster.

Use when the user's dataset format differs from what the target repo's data loaders expect, to adapt the code (not the data) for compatibility.