xla-tuning
xla-tuning is an MLOps & model serving skill for AI agents, published by AMD-AGI in the maxtext-slurm repository on GitHub. Skillspace rates xla-tuning D (63/100) and it passed the safety scan; the rating is based on static analysis only.
Find the XLA flag / NCCL env-var combination that maximizes steady-state TGS for one (model × parallelism) cell. Produces an evidence-backed leaderboard, mechanistic explanation of the winning flag, and a deployment recipe. Use when the user asks to tune XLA flags, tune NCCL, find best collective-permute / all-gather threshold, optimize FSDP/PP/TP, close a parallelism-vs-parallelism throughput gap, or sweep cross-iteration prefetch / overlap-limit / async-stream-priority knobs for a specific model.
- Grade
- D
- Quality
- 63 / 100
- Safety
- Passed
- GitHub stars
- 28
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 90 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
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
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.