113 lines
3.3 KiB
Markdown
113 lines
3.3 KiB
Markdown
# Install CUDA drivers
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Following instructions assume you have a CUDA compatible GPU with at least 8GB VRAM (GTX1070 or better) as part of hardware.
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## Ubuntu 20.04
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The installation of PyTorch GPU in Ubuntu 20.04 can be summarized in the following points,
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• Install CUDA by installing nvidia-cuda-toolkit.
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• Install the cuDNN version compatible with CUDA.
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• Export CUDA environment variables.
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### Installing CUDA
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First open a terminal and run
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```bash
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$ sudo apt install nvidia-cuda-toolkit
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```
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which directly installs the latest version of CUDA in Ubuntu. After installing CUDA, run
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```bash
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$ nvcc -V
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```
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You will get an output similar to the following to verify if you had a successful installation,
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```
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nvcc: NVIDIA (R) Cuda compiler driver
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Copyright (c) 2005-2019 NVIDIA Corporation
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Built on Sun_Jul_28_19:07:16_PDT_2019
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Cuda compilation tools, release 10.1, V10.1.243
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```
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Note the CUDA version above `release 10.1`
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### Installing CUDNN
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After above step, visit - https://developer.nvidia.com/rdp/cudnn-download - and download the CUDNN package that matches your CUDA version which is highlighted above. Once downloaded run
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```bash
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$ tar -xvzf cudnn-10.1-linux-x64-v7.6.5.32.tgz
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```
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Note that the package name might vary in your case. Now move the extracted packages like so
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```bash
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$ sudo cp cuda/include/cudnn.h /usr/lib/cuda/include/
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$ sudo cp cuda/lib64/libcudnn* /usr/lib/cuda/lib64/
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```
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Set the file permissions of cuDNN,
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```bash
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$ sudo chmod a+r /usr/lib/cuda/include/cudnn.h
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$ sudo chmod a+r /usr/lib/cuda/lib64/libcudnn*
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```
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### Export CUDA environment variables
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The CUDA environment variables are needed by PyTorch for GPU support. To set them, we need to append them to `~/.bashrc` file by running,
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```bash
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$ echo 'export LD_LIBRARY_PATH=/usr/lib/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
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$ echo 'export LD_LIBRARY_PATH=/usr/lib/cuda/include:$LD_LIBRARY_PATH' >> ~/.bashrc
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```
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Load the exported environment variables by running,
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```bash
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$ source ~/.bashrc
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```
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Finally we can check if everything went fine by running
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```bash
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$ nvidia-smi
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```
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## Windows
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The installation of PyTorch GPU in Windows 10 can be summarized in the following points,
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• Install CUDA
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• Install the cuDNN version compatible with CUDA.
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• Export CUDA environment variables.
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### Install CUDA Toolkit
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Visit https://developer.nvidia.com/cuda-downloads and download the cuda-toolkit from here
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### Installing CUDNN
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After above step, visit - https://developer.nvidia.com/rdp/cudnn-download - and download the CUDNN package that matches your CUDA version. Once downloaded unzip the cuDNN package.
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`cudnn-x.x-windows-x64-vx.x.x.x.zip`
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Copy the following files into the CUDA Toolkit directory.
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1. Copy
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```
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<installpath>\cuda\bin\cudnn*.dll
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to
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C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\bin
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```
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2. Copy
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```
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<installpath>\cuda\include\cudnn*.h
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to
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C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\include.
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```
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3. Copy
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```
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<installpath>\cuda\lib\x64\cudnn*.lib
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to
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C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\lib\x64.
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```
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### Adding CUDA_PATH to environment variables
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Variable Name: `CUDA_PATH`
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Variable Value: `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x=`
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Finally we can check if everything went fine by running from a new command prompt.
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```
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$ nvidia-smi
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```
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