Build and benchmark CUDA kernels for NVIDIA GPUs in HuggingFace workflows.
npx skills add https://github.com/dotruru/claudemd --skill cuda-kernels-dotruru
Please help me install this Agent Skill. Skill: cuda-kernels Source: https://github.com/dotruru/claudemd/tree/main/skills/cuda-kernels Command: npx skills add https://github.com/dotruru/claudemd --skill cuda-kernels-dotruru
This Skill provides patterns and guidance for developing optimized CUDA kernels for NVIDIA GPUs, enabling benchmarking, integration with HuggingFace Kernels Hub, and diffusers/transformers workflows.
Run the quick demo by executing scripts/benchmark_example.py to benchmark optimized versus baseline kernels.
High-intent search queries and answers about installing and using this skill.
You build and optimize CUDA kernels for NVIDIA GPUs by applying provided patterns for RMSNorm, RoPE, and GEGLU, then integrating them directly into HuggingFace diffusers and transformers workflows using included scripts and references.
Yes, you can benchmark custom CUDA kernels against baselines by executing scripts such as benchmark_example.py, which measures performance improvements for optimized kernels targeting BF16, FP16, and FP32 workloads.
Yes, the CUDA kernel optimization requires multi-arch builds supporting NVIDIA H100, A100, and T4 GPUs, ensuring reproducible workflows across different hardware architectures via Nix or pip environments.
Available CUDA kernel patterns include RMSNorm, RoPE, GEGLU, and AdaLN, specifically aimed at accelerating diffusion models and large language models within your HuggingFace transformers and diffusers pipelines.
You integrate optimized CUDA kernels with the HuggingFace Kernels Hub by following included guides and integration references, enabling seamless deployment of your custom kernels into diffusers video and image generation pipelines.
You need diffusers, transformers, accelerate, and torch dependencies installed to run optimized CUDA kernels with HuggingFace transformers, ensuring proper BF16, FP16, and FP32 tensor operations across supported NVIDIA GPUs.
huggingfaceOptimizes NVIDIA GPU AI inference and training with custom CUDA kernels for HuggingFace libraries.
m0atDevelop and benchmark optimized CUDA kernels for HuggingFace diffusers and transformers.
jacardlGenerate images from text prompts using Stable Diffusion via HuggingFace Diffusers.
LancerLabGenerate CUDA kernel build scripts and profile performance with NVIDIA Nsight Compute.
