What problem does it solve?
Performance CI in FastVideo fails when latency, throughput, or memory metrics shift for legitimate reasons such as runtime upgrades, dependency changes, or hardware image updates. This Skill safely advances the rolling median baseline stored in the Hugging Face dataset FastVideo/performance-tracking so accepted shifts stop triggering false regression failures, without hiding real code regressions.
Core Features & Use Cases
- Validated multi-source reseeding: Uploads one success=true baseline record per reviewed normalized performance JSON, rejecting internally inconsistent source batches that exceed the intra-batch regression tolerance.
- Legacy and v2 identity support: Handles legacy (model_id, gpu_type) targets and v2 exact comparable identities with workload, variant, hardware, software, and recipe fingerprints, including first-time calibration seeds.
- Auditable safety workflow: Backs up existing HF history under /tmp, writes provenance files, requires explicit confirmation phrases before upload, and offers cleanup of temporary state afterward.
- Use Case: After a torch upgrade legitimately raises peak memory and CI fails the rolling baseline comparison, download the normalized_perf_*.json artifacts from the failed Buildkite jobs, review them, and use this Skill to advance the baseline with full provenance.
Quick Start
Ask the agent to re-seed the performance baseline for a given model_id and gpu_type using the reviewed normalized performance JSON artifacts from the failed Buildkite run, providing a one-line rationale for the accepted shift.