reseed-performance-baseline

Re-seed rolling performance baselines in the FastVideo HF tracking dataset from reviewed benchmark JSONs.

4.3k|431|Updated Oct 24, 2024
One-click install
npx skills add https://github.com/hao-ai-lab/FastVideo --skill reseed-performance-baseline
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: reseed-performance-baseline
Source: https://github.com/hao-ai-lab/FastVideo/tree/main/.agents/skills/reseed-performance-baseline
Command: npx skills add https://github.com/hao-ai-lab/FastVideo --skill reseed-performance-baseline

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about reseed-performance-baseline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I re-seed a performance baseline after an intentional regression in CI?

Download the normalized_perf_*.json artifacts from the failed Buildkite performance jobs, review them, and pass them as source_results with the model_id, gpu_type, and an intent rationale. The workflow validates batch consistency, backs up existing history, and uploads one success=true record per accepted source after explicit confirmation.

How many source records are needed to move the rolling performance baseline?

The baseline uses the median of the last 5 successful baseline-eligible records. One or two seed records may not move an existing median immediately, three consistent records usually move it, and five effectively reset the window. A single record is enough for a first v2 calibration seed.

What is the difference between legacy and v2 performance baseline targets?

Legacy targets are scoped by (model_id, gpu_type), while v2 targets use an exact comparable identity of workload_id, variant_id, benchmark_version, hardware_profile_id, software_profile_id, and recipe_fingerprint. V2 records never fall back to legacy matching, and first v2 baselines require successful scheduled-main CALIBRATION_NEEDED artifacts.

Why does the reseed workflow reject my source JSON batch?

Batches are rejected when any source record regresses against the batch median by more than max_intra_batch_regression (default 5%), when records target different identities, or when raw perf_*.json files are supplied instead of normalized artifacts. Provide cleaner reviewed sources or investigate the variance first.

When should I not re-seed the performance baseline?

Do not reseed when the failure might be a real code regression, when fixed thresholds in .buildkite/performance-benchmarks/tests/*.json are the actual blocker, or when there is no clear source run, commit, and rationale. Baseline history is a production signal requiring provenance.

What happens to temporary files after a performance baseline reseed?

The workflow keeps the /tmp tracking mirror, prepared staging records, source downloads, and backup with PROVENANCE.txt until you reply exactly 'cleanup reseed temp'. Nothing is deleted automatically, preserving audit artifacts in case a bad seed needs correction.