godweiyang/NN-CUDA-Example resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1538 stars · 206 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of simple example projects showing how to write custom CUDA operators and call them from PyTorch and TensorFlow. It demonstrates three compilation approaches (JIT, setuptools, CMake) along with kernel timing and model training code.

Use cases

• learn how to write a custom CUDA kernel for PyTorch
• compile and call custom CUDA operators from TensorFlow
• compare performance of custom CUDA kernels against built-in torch ops
• set up CMake builds for CUDA extensions
• benchmark CUDA kernel execution time
• implement custom backward propagation in PyTorch

When to choose

• you are learning to integrate custom CUDA kernels with PyTorch or TensorFlow
• you want minimal, readable reference code for CUDA extension compilation
• you need examples of kernel timing and training with custom ops

When to avoid

• you need production-ready, maintained CUDA extension tooling
• you use CUDA versions or frameworks other than the pinned environment (CUDA 11.0, PyTorch 1.7, TF 2.4)
• you want a library or API rather than example code

Facets

learning-resource · maturity maintenance

machine-learning gpu-computing developer-tools benchmarking deep-learning gpu-computing tutorials developer-tools python cpp cuda-kernels pytorch tensorflow custom-operators example-code jit-compilation cmake setuptools linux gpu

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