PyCUDA gives you easy, Pythonic access to Nvidia’s CUDA parallel computation API. Several wrappers of theCUDA API already exist–so why the need for PyCUDA?
- Object cleanup tied to lifetime of objects. This idiom,often calledRAIIin C++, makes it much easier to write correct, leak- andcrash-free code. PyCUDA knows about dependencies, too, so (for example)it won’t detach from a context before all memory allocated in it is alsofreed.
- Convenience. Abstractions like
pycuda.compiler.SourceModuleandpycuda.gpuarray.GPUArraymake CUDA programming even more convenientthan with Nvidia’s C-based runtime. - Completeness. PyCUDA puts the full power of CUDA’s driver API at yourdisposal, if you wish.
- Automatic Error Checking. All CUDA errors are automatically translatedinto Python exceptions.
- Speed. PyCUDA’s base layer is written in C++, so all the niceties aboveare virtually free.
- Helpful Documentation. You’re looking at it. ;)
Here’s an example, to give you an impression:
- CUDA® Python is a preview software release providing Cython/Python wrappers for CUDA driver and runtime APIs. Python developers will be able to leverage massively parallel GPU computing to achieve faster results and accuracy. Python is an important programming language that plays a critical role within the science, engineering, data analytics, and deep learning application ecosystem. However, these applications will tremendously benefit from NVIDIA’s CUDA Python software initiatives.
- GPU-Accelerated Computing with Python. NVIDIA’s CUDA Python provides a driver and runtime API for existing toolkits and libraries to simplify GPU-based accelerated processing. Python is one of the most popular programming languages for science, engineering, data analytics, and deep learning applications. However, as an interpreted language, it’s been considered too slow for high-performance computing.
Cuda Python Version
How To Install Nvidia Drivers and CUDA-10.0 for RTX 2080 Ti GPU on Ubuntu-16.04/18.04 Achintha Ihalage in Better Programming Setting up Tensorflow-GPU with Cuda and Anaconda onWindows.
(This example is examples/hello_gpu.py in the PyCUDAsource distribution.)
On the surface, this program will print a screenful of zeros. Behindthe scenes, a lot more interesting stuff is going on:
- PyCUDA has compiled the CUDA source code and uploaded it to the card.
- Note
- This code doesn’t have to be a constant–you can easily have Pythongenerate the code you want to compile. See Metaprogramming.
- PyCUDA’s numpy interaction code has automatically allocatedspace on the device, copied the numpy arrays a and b over,launched a 400x1x1 single-block grid, and copied dest back.
- Note that you can just as well keep your data on the card betweenkernel invocations–no need to copy data all the time.
- See how there’s no cleanup code in the example? That’s not because wewere lazy and just skipped it. It simply isn’t needed. PyCUDA willautomatically infer what cleanup is necessary and do it for you.
Curious? Let’s get started.
Cuda Python Install
This list is by definition incomplete! If you know of other software youfeel should be listed here, please submit a PR!
- Tutorial
- Device Interface
- Built-in Utilities
- OpenGL
- GPU Arrays
- Metaprogramming
- Changes
Note that this guide will not explain CUDA programming and technology. Pleaserefer to Nvidia’s programming documentation for that.
PyCUDA also has its own web site,where you can find updates, new versions, documentation, and support.
