PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab (FAIR).
We are able to dramatically speed up computing applications by harnessing the power of GPUs.
NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools, and the CUDA runtime.
So let’s start and install PyTorch and CUDA locally !
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– We can install PyTorch from here
– Here we choose the preferences we want:
Stable: Stable represents the most currently tested and supported version of PyTorch.
Preview (Nightly): available if you want the latest, not fully tested and supported.
If you want to work in CPU only or GPU not available choose None.
Now after PyTorch installation, we will install CUDA (If you did not choose None above).
– Download and install CUDA Toolkit, the same version you chose above, from here.
– Then download cuDNN compatible with the version of your CUDA from here. Note: it requires signing up.
– Unzip the cuDNN package.
– Copy the following files into the CUDA Toolkit directory:
1- Copy cuda\bin\cudnn*.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\bin.
2- Copy cuda\include\cudnn*.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\include.
3- Copy cuda\lib\x64\cudnn*.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\lib\x64.
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Run this Command in you PyTorch project.
import torch torch.cuda.is_available()
If the output is Ture. then congratulations it works :D.
else we will need to fix something. feel free to ask.
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