inference_perf_hillclimb
inference_perf_hillclimb is an MLOps & model serving skill for AI agents, published by pytorch in the torchtitan repository on GitHub. Skillspace rates inference_perf_hillclimb D (62/100) and it passed the safety scan; the rating is based on static analysis only.
Profiler-driven hill-climbing to close the inference throughput gap between TorchTitan's unified model (running inside vLLM) and vLLM's native model. Benchmark with generate.py --benchmark, climb optimization rungs (compile / cudagraph / fused kernels), profile torchtitan vs the native target, then patch the single biggest gap at a time and re-measure. Use when the user wants to benchmark or optimize RL inference generation speed, reproduce previous hill climbing study, or invokes /inference_perf_hillclimb.
- Grade
- D
- Quality
- 62 / 100
- Safety
- Passed
- GitHub stars
- 5,533
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 97 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- BSD-3-Clause
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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