> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gpuhub.com/llms.txt
> Use this file to discover all available pages before exploring further.

# CUDA / cuDNN

> Install CUDA/cuDNN

<Note>
  If you don't need to recompile code, you typically don't need to install CUDA/cuDNN separately. Frameworks come with precompiled CUDA support, and the framework version corresponds to a specific CUDA version. Therefore, you only need to focus on the framework version and not the CUDA version independently.
</Note>

## Check Default CUDA/cuDNN Version

<Note>
  The CUDA version displayed by the  `nvidia-smi` command only indicates the highest CUDA version supported by the driver, not the actual version of CUDA installed on the instance.
</Note>

To check the default CUDA version installed in the platform's built-in image (installation directory is `/usr/local/`):

```bash bash theme={null}
# Query the CUDA version in the platform's built-in image
ldconfig -p | grep cuda
    libnvrtc.so.11.0 (libc6,x86-64) => /usr/local/cuda-11.0/targets/x86_64-linux/lib/libnvrtc.so.11.0
    libnvrtc.so (libc6,x86-64) => /usr/local/cuda-11.0/targets/x86_64-linux/lib/libnvrtc.so
    libnvrtc-builtins.so.11.0 (libc6,x86-64) => /usr/local/cuda-11.0/targets/x86_64-linux/lib/libnvrtc-builtins.so.11.0
```

To check the cuDNN version installed in the platform's built-in image:

```bash bash theme={null}
# Query the cuDNN version in the platform's built-in image
ldconfig -p | grep cudnn
    libcudnn_ops_train.so.8 (libc6,x86-64) => /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8
    libcudnn_ops_train.so (libc6,x86-64) => /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so
    libcudnn_ops_infer.so.8 (libc6,x86-64) => /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8
    libcudnn_ops_infer.so (libc6,x86-64) => /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so
```

The version number is indicated by the number after `.so` in the output logs. If you have installed CUDA via `conda`, you can check it using the following commands:

```bash bash theme={null}
conda list | grep cudatoolkit
cudatoolkit               10.1.243             h6bb024c_0    defaults

conda list | grep cudnn
cudnn                     7.6.5                cuda10.1_0    defaults


```

## Install Other Versions of CUDA/cuDNN

### Method 1: Install using Conda

**Advantages:** Simple and easy to use. **Disadvantages:** Typically, header files are not included. If you need to compile code, you will need to install using Method 2.

```bash bash theme={null}
conda install cudatoolkit==xx.xx
conda install cudnn==xx.xx
```

<Accordion title="Not sure what the version number is? Search for it.">
  ```bash bash theme={null}
    conda search cudatoolkit
    Loading channels: done
    # Name                       Version           Build  Channel             
    cudatoolkit                      9.0      h13b8566_0  anaconda/pkgs/main  
    cudatoolkit                      9.2               0  anaconda/pkgs/main  
    cudatoolkit                 10.0.130               0  anaconda/pkgs/main  
    cudatoolkit                 10.1.168               0  anaconda/pkgs/main  
    cudatoolkit                 10.1.243      h6bb024c_0  anaconda/pkgs/main  
    cudatoolkit                  10.2.89      hfd86e86_0  anaconda/pkgs/main  
    cudatoolkit                  10.2.89      hfd86e86_1  anaconda/pkgs/main  
    cudatoolkit                 11.0.221      h6bb024c_0  anaconda/pkgs/main  
    cudatoolkit                   11.3.1      h2bc3f7f_2  anaconda/pkgs/main    
  ```
</Accordion>

### Method 2: Install by Downloading and Installing the Package

<Note>
  CUDA Download Address: [CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive)
</Note>

**Installation Method:**

After downloading the `.run` installation package:

```bash bash theme={null}
chmod +x xxx.run   # Add execution permission
./xxx.run          # Run the installation package
```

<Note>
  cuDNN Download Address: [CUDA Deep Neural Network](https://developer.nvidia.com/cudnn)
</Note>

**Installation Method:**

1. Unzip the downloaded file.
2. Move the dynamic libraries and header files to the corresponding directories:

```bash bash theme={null}
mv cuda/include/* /usr/local/cuda/include/
chmod +x cuda/lib64/* && mv cuda/lib64/* /usr/local/cuda/lib64/
```

3. After installation, add the environment variable:

```bash bash theme={null}
echo "export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:${LD_LIBRARY_PATH} \n" >> ~/.bashrc
source ~/.bashrc && ldconfig
```

<Note>
  The default image includes the most basic version of CUDA and cuDNN. If you have installed `cudatoolkit` via Conda, it will generally be used preferentially over the default installation.
</Note>