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PyCUDA lets you access Nvidia <https://nvidia.com>'s CUDA <https://nvidia.com/cuda/> parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about PyCUDA?
Object cleanup tied to lifetime of objects. This idiom, often
called
RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>_
in C++, makes it much easier to write correct, leak- and
crash-free code. PyCUDA knows about dependencies, too, so (for
example) it won't detach from a context before all memory
allocated in it is also freed.
Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia's C-based runtime.
Completeness. PyCUDA puts the full power of CUDA's driver API at your disposal, if you wish. It also includes code for interoperability with OpenGL.
Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions.
Speed. PyCUDA's base layer is written in C++, so all the niceties above are virtually free.
Helpful Documentation <https://documen.tician.de/pycuda>_.
Relatedly, like-minded computing goodness for OpenCL <https://www.khronos.org/registry/OpenCL/>_
is provided by PyCUDA's sister project PyOpenCL <https://pypi.org/project/pyopencl>_.
FAQs
Python wrapper for Nvidia CUDA
We found that pycuda demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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