Skip to content

cuda_bindings

Directory actions

More options

Directory actions

More options

Latest commit

 

History

History
 
 

cuda_bindings

README.md

cuda.bindings: Low-level CUDA interfaces

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 for the workflow and performance results.

cuda.bindings is a subpackage of cuda-python.

Installing

cuda.bindings can be installed from:

  • PyPI
  • Conda (conda-forge/nvidia channels)
  • Source builds

Differences between these options are described in Installation documentation. Each package guarantees minor version compatibility.

Runtime Dependencies

cuda.bindings is supported on all the same platforms as CUDA. Specific dependencies are as follows:

  • Driver: Linux (450.80.02 or later) Windows (456.38 or later)
  • CUDA Toolkit 12.x

Only the NVRTC and nvJitLink redistributable components are required from the CUDA Toolkit, which can be obtained via PyPI, Conda, or local installers (as described in the CUDA Toolkit Windows and Linux Installation Guides).

Developing

We use pre-commit to manage various tools to help development and ensure consistency.

pip install pre-commit

Code linting

Run this command before checking in the code changes

pre-commit run -a --show-diff-on-failure

to ensure the code formatting is in line of the requirements (as listed in pyproject.toml).

Code signing

This repository implements a security check to prevent the CI system from running untrusted code. A part of the security check consists of checking if the git commits are signed. See here and here for more details, including how to sign your commits.

Testing

Latest dependencies can be found in requirements.txt.

Multiple testing options are available:

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

Python Unit Tests

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 Unit Tests

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_HOME with the path to the CUDA Toolkit installation.
  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

Samples

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.
  • examples/extra/numba_emm_plugin.py: Implements a Numba External Memory Management plugin, showing that this CUDA Python Driver API can coexist with other wrappers of the driver API.

To run these samples:

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

Benchmark (WIP)

Benchmarks were used for performance analysis during initial release of CUDA Python. Today they need to be updated the 12.x toolkit and are work in progress.

The intended way to run these benchmarks was:

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