optimize-for-gpu
CommunityAccelerate Python workloads with GPU acceleration.
Authormkurman
Version1.0.0
Installs0
System Documentation
What problem does it solve?
GPU-accelerate Python code across a broad set of libraries to unlock performance for data processing, ML, simulations, and scientific workflows.
Core Features & Use Cases
- GPU-accelerated computation with CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT.
- Broad applicability across NumPy/pandas/SciPy pipelines, machine learning workflows, large-scale data processing, and geospatial or medical imaging tasks.
- Seamless integration with CUDA-enabled hardware and RAPIDS ecosystem to speed up CPU-bound Python code and build scalable data pipelines.
Quick Start
Install the required CUDA-enabled libraries and run your Python script to automatically activate GPU acceleration across supported workflows.
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: optimize-for-gpu Download link: https://github.com/mkurman/tamux/archive/main.zip#optimize-for-gpu Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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