For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and maintain bindings back to Python, which most people never did; or move up the stack and let someone else’s library do it, namely PyTorch, CuPy, or RAPIDS.
The second option is why the Python GPU ecosystem thrives. But it has limits. The moment you need something the library above you doesn’t expose, you’re back to the first choice.
Because each library reached CUDA in its own way, getting two of them to cooperate on the same data took care. If CuPy allocated a block of GPU memory, what did it take for cuDF to work on that block, on the same stream, without copying it? The answer ran through interchange protocols and close attention to who owned what.
With CUDA 13.3, we released CUDA Python 1.0, the libraries and tools that give you the full CUDA platform from Python. Python is now a supported way to use the CUDA platform.
Here is what lands together:
