Computer_Vision/Install-CUDA-Drivers.md

# Install CUDA drivers
Following instructions assume you have a CUDA compatible GPU with at least 8GB VRAM (GTX1070 or better) as part of hardware. 

## Ubuntu 20.04
The installation of PyTorch GPU in Ubuntu 20.04 can be summarized in the following points,
•   Install CUDA by installing nvidia-cuda-toolkit.  
•   Install the cuDNN version compatible with CUDA.  
•   Export CUDA environment variables.  

### Installing CUDA
First open a terminal and run 
```bash
$ sudo apt install nvidia-cuda-toolkit
```

which directly installs the latest version of CUDA in Ubuntu. After installing CUDA, run 
```bash
$ nvcc -V
```
You will get an output similar to the following to verify if you had a successful installation,
```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
```
Note the CUDA version above `release 10.1`

### Installing CUDNN
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
```bash
$ tar -xvzf cudnn-10.1-linux-x64-v7.6.5.32.tgz
```
Note that the package name might vary in your case. Now move the extracted packages like so
```bash
$ sudo cp cuda/include/cudnn.h /usr/lib/cuda/include/
$ sudo cp cuda/lib64/libcudnn* /usr/lib/cuda/lib64/
```

Set the file permissions of cuDNN,
```bash
$ sudo chmod a+r /usr/lib/cuda/include/cudnn.h 
$ sudo chmod a+r /usr/lib/cuda/lib64/libcudnn*
```

### Export CUDA environment variables

The CUDA environment variables are needed by PyTorch for GPU support. To set them, we need to append them to `~/.bashrc` file by running,
```bash
$ echo 'export LD_LIBRARY_PATH=/usr/lib/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
$ echo 'export LD_LIBRARY_PATH=/usr/lib/cuda/include:$LD_LIBRARY_PATH' >> ~/.bashrc
```

Load the exported environment variables by running,
```bash
$ source ~/.bashrc
```

Finally we can check if everything went fine by running 
```bash
$ nvidia-smi
```
![](https://i.imgur.com/3LwvM62.png)

## Windows
The installation of PyTorch GPU in Windows 10 can be summarized in the following points,
•   Install CUDA  
•   Install the cuDNN version compatible with CUDA.  
•   Export CUDA environment variables.  

### Install CUDA Toolkit

Visit https://developer.nvidia.com/cuda-downloads and download the cuda-toolkit from here

### Installing CUDNN
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.  

`cudnn-x.x-windows-x64-vx.x.x.x.zip`

Copy the following files into the CUDA Toolkit directory.
1. Copy 
```
<installpath>\cuda\bin\cudnn*.dll 
to 
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\bin
```

2. Copy 
```
<installpath>\cuda\include\cudnn*.h 
to 
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\include.
```

3. Copy 
```
<installpath>\cuda\lib\x64\cudnn*.lib
to 
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\lib\x64.
```

### Adding CUDA_PATH to environment variables
Variable Name: `CUDA_PATH`  
Variable Value: `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x=`

Finally we can check if everything went fine by running from a new command prompt.
```
$ nvidia-smi
```
![](https://i.imgur.com/3LwvM62.png)