sue-exp-naming

To generate consistent experiment names for W&B and Slurm, you need a deterministic process that creates identical run IDs, job names, and ledger fields across retries. This ensures reliable end-to-end tracking and prevents mismatched results across your ML workflows.

Slurm job submissions often fail due to name collisions or length limit violations. Validating experiment names for CSV-safe formatting and compliance with Slurm constraints before submission prevents these tracking errors and failed job deployments.

The best way to avoid W&B run ID collisions is to use deterministic name generation that checks for collisions and enforces length limits. This standardizes env-var exports for downstream tooling and keeps your scale-up HPC experiments fully traceable.

Yes, this naming validation applies to pre-run preparation workflows for ML experiments deployed on sandbox backends including LUMI, Snellius, NM5, Brev, RunPod, and AutoDL. It ensures identifiers remain consistent across these platforms.

You can standardize per-GPU labels and result ledger fields by generating all required identifiers before submission. This creates consistent W&B entities, Slurm job names, and ledger fields that link your experiments correctly for full traceability.