I've been trying to improve the performance of my python scripts and would like to run some using my computer's built-in GPU. However, my computer is Windows 10 and its GPU is not CUDA compatible. From what I've seen, it seems that the GPU must be CUDA compatible in order for it to run python scripts. Is there any way to utilize my GPU for said purposes? If not, are there other programming languages in which I can do this?
The GPU is a proccessing unit for graphics. It most likely won't help except for drawing polygons, transfering data, or massive data sets. The closest you can get is importing a module (depending on your needs), that uses C++ to interact with the GPU (such as OpenCL), or coding interactions yourself (much more complicated).
To answer your 2nd question, C++ or C# should work with your GPU.
Please specify what script you are trying to run for more detail
Good luck!
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I'm currently working on building a graphics library with Python. (I know opengl exists, but this is just for fun) Essentially, I want to be able to implement many of the common features available with opengl.
Right now, I'm using pyopencl which uses opencl under the hood to send code to the GPU for processing.
Here's the kicker though, I'm developing on a 2015 MacBook Pro that has an AMD GPU.
I've written all of the code to make this work and now I'm working on finding the bottlenecks in the code to try to optimize.
I've found that the biggest bottle neck is in my fragment shader implemenetation when I'm converting numpy arrays to cl.Buffer objects prior to GPU processing and then back to numpy arrays after processing.
Doing some research has led me to think that using SVMs would help to minimize this cost, but.... SVMs were introduced in opencl 2.0. Apparently, Apple has stopped supporting opencl in favor of their own in house GPU library Metal and so I'm stuck with opencl 1.2
So I guess my question is, has anyone else hit this roadblock and if so, what is the common way of handling it? If I transition to Metal, then my code is no longer as universal, but if I stay on opencl, I have performance problems. I could autodetect the platform that the code is being run on and use different implementations specific to the platform, but one of my problems is I don't know if there is a trusted standard implementation for Metal in Python.
Hello I know that the key to analyzing data and working with artificial intelligence is to use the gpu and not the cpu. The problem is that I don't know how to use it with Python in the visual studio code, I use Ubuntu, I already have nvidia installed
You have to use with the libraries that are designed to work with the GPUs.
You can use Numba to compile Python code directly to binary with CUDA/ROC support, but I don't really know how limiting it is.
Another way is to call APIs that are designed for parallel computing such as OpenCL, there is PyOpenCL binding for this.
A bit limiting, but sometimes valid way - OpenGL/DirectX compute shaders, they are extremely easy, but not so fast if you need to transfer data back and forth in small batches.
According to knowledge with tf.device('/GPU') can be used for implementing tensor-flow in GPU. Is there any similar is there any way for implementing any python code on GPU(Cuda) ? or should I use pycuda?
For parallel processing in python some intermideate libraries or packages needed to be there that sit between the code and the gpu/cpu for parallel executions. Some popular packages are pycuda, numba etc. If you want to do gpu programming using simple python syntax without using other frameworks like tensorflow, then take a look at this.
Today i was asking to me if is possible to do matrix calculation using gpu instead cpu because i know that a gpu is designed to do them faster then a cpu.
I searched on the net and i found notices about the matrix calculation using gpu with different python's libraries but my question is exists a documentation that descibes how should we write code to comunicate with a gpu.
I'm asking that because i want to develop my own one to better understand how gpu work and to try something different.
Thanks to all.
I solved that problem with OpenCL
OpenCL is a standard library that the vendor of the GPU's implement by their own. Like NVIDIA support openCL and other features thanks to CUDA library.
Here a good guide to get start
I want to write an algorithm that would benefit from the GPU's superior hashing capability over the CPU.
Is PyOpenGL the answer? I don't want to use drawing tools, but simply run a "vanilla" python script ported to the GPU.
I have an ATI/AMD GPU if that means anything.
Is PyOpenGL the answer?
No. At least not in the way you expect it. If your GPU does support OpenGL-4.3 you could use Compute Shaders in OpenGL, but those are not written in Python
but simply run a "vanilla" python script ported to the GPU.
That's not how GPU computing works. You have to write the shaders of computation kernels in a special language. Either OpenCL or OpenGL Compute Shaders or, specific to NVIDIA, in CUDA.
Python would then just deliver the framework for getting the GPU computation running.