debug-failing-gpu
debug-failing-gpu is a System & environment troubleshooting skill for AI agents, published by facebookexperimental in the triton repository on GitHub. Skillspace rates debug-failing-gpu D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Recover from GPU-busy / GPU-unavailable failures. Use when a command (pytest, python, a TLX/Triton kernel run, a benchmark) fails with errors indicating the GPU is busy, out of memory, or unavailable — e.g. "CUDA error: out of memory", "all CUDA-capable devices are busy or unavailable", "CUDA-capable device(s) is/are busy or unavailable", "RuntimeError: No CUDA GPUs are available", "device-side assert", or a hang on the first CUDA call. Runs find_working_gpu.sh to locate a healthy GPU and re-runs the failed command pinned to it via CUDA_VISIBLE_DEVICES.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 178
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
- 84 / 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
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