using-gpus
OfficialOnly use GPUs when your code truly needs them.
Authoryale-som-hpc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
GPU resources on the Yale SOM HPC cluster are scarce and expensive to waste. Idle GPU allocations block other users; this skill ensures GPUs are requested only when the code actively uses them, reducing wasted compute time and avoiding unnecessary resource contention.
Core Features & Use Cases
- Enforces on-demand GPU provisioning by aligning GPU requests with active CUDA workloads (e.g., PyTorch, TensorFlow, JAX, RAPIDS).
- Guides CPU-GPU separation, testing, and monitoring to quickly identify idle allocations and cancel them.
- Provides practical sbatch guidance and best practices for incremental GPU usage in ML pipelines and research jobs.
Quick Start
Tell Claude Code to apply the GPU-on-demand policy to your project and validate with a small GPU workload.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: using-gpus Download link: https://github.com/yale-som-hpc/claude-code-marketplace/archive/main.zip#using-gpus Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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