cuda-c-examples-torch

What problem does it solve?

This Skill demonstrates how to integrate CUDA C kernels into PyTorch projects by using Torch's load_inline to compile and bind custom kernels at runtime, enabling high-performance GPU ops alongside Python code.

Core Features & Use Cases

• Inline CUDA/C++ kernel integration with PyTorch via load_inline.
• Ready-to-run examples: vector_add, relu, matmul, softmax, layernorm, and a fused kernel workflow.
• Use cases include accelerating custom ops in research experiments or production-grade training loops requiring tailored GPU kernels.

Quick Start

Run a sample PyTorch script that loads an inline CUDA C kernel using load_inline and executes a simple operation.