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.
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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!
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.
I've been using the SBM algorithm, implemented in the graph-tool package. I need to process a huge amount of data and need to run it in parallel.
I know that OpenMP is activated by default in this package and used in the specific and compatible algorithms, but the documentation doesn't specify which algorithms.
I've tried openmp_enabled() or openmp_set_num_threads() and also export OMP_NUM_THREADS=16. Everything seems fine but when I check the running processes, it's not paralleled.
Do you have any experience with implementing SBM parallelized?
Although graph-tool uses OpenMP, not every algorithm is implemented in parallel, simply because this cannot be done in some cases. The SBM inference algorithm implemented in graph-tool is based on MCMC, which cannot be parallelized in general. Because of this, enabling OpenMP will have no effect.
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 would like to build my project in Scala and then use it in a script in Python for my data hacking (as a module or something like that). I have seen that there are ways to integrate python code into JVM languages with Jython (only Python 2 projects though). What I want to do is the other way around though. I found no information on the net how to do this, but it seems strange that this should not be possible.
General solution -- use some RPC/IPC (sockets, protobuf, whatever).
However, you might want to look at Spark's solution -- how they translate Python code in Scala's APIs (https://www.py4j.org/) .
Recently, scalapy was created to call Python libraries from Scala.
https://github.com/shadaj/scalapy