cuda.bindings is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python. Checkout the Overview page for the workflow and performance results.

Please refer to the Installation page for instructions and required/optional dependencies.

This subpackage adheres to the developing practices described in the parent metapackage CONTRIBUTING.md.

Pass the pip / uv configuration option -C="debug=True" or --config-settings="debug=True" to explicitly to build debuggable binaries. Debuggable binaries are built by default for editable builds.

Debuggable builds are not supported on Windows.

Testing dependencies can be installed using the [test] optional dependency identifier. For example, pip install -v -e .[test].

Multiple testing options are available:

• Python Unit Tests
• Cython Unit Tests
• Samples
• Benchmark

Responsible for validating different binding usage patterns. Unit test test_kernelParams.py is particularly special since it demonstrates various approaches in setting up kernel launch parameters.

To run these tests:

• python -m pytest tests/ against editable installations
• pytest tests/ against installed packages

Cython tests are located in tests/cython and need to be built. These builds have the same CUDA Toolkit header requirements as Installing from Source where the major.minor version must match cuda.bindings. To build them:

1. Setup environment variable CUDA_PATH (or CUDA_HOME) with the path to the CUDA Toolkit installation. Note: If both are set, CUDA_PATH takes precedence.
2. Run build_tests script located in test/cython appropriate to your platform. This will both cythonize the tests and build them.

To run these tests:

• python -m pytest tests/cython/ against editable installations
• pytest tests/cython/ against installed packages

Various CUDA Samples that were rewritten using CUDA Python are located in examples.

In addition, extra examples are included:

• examples/extra/jit_program_test.py: Demonstrates the use of the API to compile and launch a kernel on the device. Includes device memory allocation / deallocation, transfers between host and device, creation and usage of streams, and context management.

To run these samples:

• python -m pytest tests/cython/ against editable installations
• pytest tests/cython/ against installed packages

Allows for analyzing binding performance using plugin pytest-benchmark.

To run these benchmarks:

• python -m pytest --benchmark-only benchmarks/ against editable installations
• pytest --benchmark-only benchmarks/ against installed packages