# NVIDIA @ Windows

:::{warning}
**TensorFlow with GPU support for WSL2 on Windows**

Install NVIDIA Driver **only** on Windows (Host) not on WSL2 (Guest)!

:::

This documentation is designed to serve as a helpful resource for users navigating the installation, maintenance, and operation of a TensorFlow environment. While it may not cover every aspect comprehensively, its goal is to provide assistance where needed.

Specifically, this documentation aims to address important details concerning the integration of NVIDIA and CUDA drivers with TensorFlow. These are challenges that I have personally encountered and observed others facing, hence their inclusion in this guide.

## NVIDIA Installer


![image](images/nvidia_installer.png)

:::{tip}
The NVIDIA installer offers a `clean installation` option, which can be both beneficial and potentially risky, if manually modifications are made to the driver configuration. 
To illustrate, consider the following example: I removed files generated by the NVIDIA Installer and substituted them with symbolic links.

```{code-block}
:caption: Windows Command Prompt as admin

    > cd \Windows\System32\lxss\lib
    > del libcuda.so
    > del libcuda.so.1
    > mklink libcuda.so libcuda.so.1.1
    > mklink libcuda.so.1 libcuda.so.1.1

```

:::

<br>

## Verify NVIDIA Driver
 ```{code-block} bash
   :caption: Terminal    
    
   nvidia-smi    
```

![image](images/nvidia-smi.png)

| No |             |                |
|----|------------------|--------------------------|
| 1  | cmd              | `nvidia-smi`             |
| 2  | installed        | NIVIA driver version     |
| 3  | highest possible | CUDA version             |

:::{danger}
CUDA Version: This is **not** the installed CUDA version, this is the highest possible version compatible with the current NVIDIA driver.
:::

<br>

## Verify CUDA Toolkit
 ```{code-block} bash
   :caption: Terminal    
    
   nvcc --version    
```

![image](images/nvcc--version.png)

| No |             |                      |
|----|------------------|----------------------|
| 1  | cmd              | `nvcc --version`     |
| 2  | installed        | CUDA Toolkit version |

:::{important}
CUDA Version: ´nvcc --version´ returns the installed CUDA Toolkit version **on your system**, that doesn't mean ``Tensorflow`` will use it in your `python environment`. `pip install tensorflow[and-cuda]` will install it's on CUDA and CUDnn drivers. If you check the drivers in Python, you have to separte between system and environment installations.  
:::

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