cuda-python provides low-level Python bindings for the CUDA Driver and Runtime APIs. It serves as a programmatic wrapper for controlling device memory, managing hardware toolchains, and orchestrating execution graphs on NVIDIA GPUs, allowing for the compilation and launching of parallel kernels directly from Python.
The project enables the development of SIMT kernels and the execution of mathematical algorithms on device memory. It integrates pre-compiled bytecode as custom operators and interfaces with accelerated device libraries to access low-level hardware functions without leaving the language environment.
The toolkit covers hardware management through host and runtime API integration, as well as device memory management for structured data layouts and temporary buffers. It includes capabilities for system hardware inspection, GPU execution debugging, and performance optimizations such as disk-backed kernel caching and cooperative reduction execution.