Added guide for GPU on WSL2 (#19)

CODEOWNERS

@@ -7,3 +7,4 @@
 /manylinux_2014_docker_images @sub-mod
 /ppc64le @wdirons
 /tekton @perfinion
+/wsl2_gpu_guide @Avditvs

README.md

@@ -47,10 +47,15 @@ Want to add your own project to this list? It's easy: check out
 
 * [**ppc64le Builds**](ppc64le_builds): Dockerfiles and wheel build scripts for
   building TF on ppc64le.
+
+* [**WSL2 GPU Guide**](wsl2_gpu_guide): Instructions for enabling GPU with Tensorflow
+  on a WSL2 virtual machine.
+
 * [**Raspberry Pi Builds**](raspberry_pi_builds): TensorFlow's old official docs
   for building on Raspberry Pi. Needs an owner.
 
 
+
 ### WIP / Other
 
 * [**Tekton CI**](tekton): perfinion's experimental directory for using Tekton 

wsl2_gpu_guide/README.md

+# WSL2 GPU Guide
+
+Instructions for enabling GPU with Tensorflow on a WSL2 virtual machine.
+
+Maintainer: @Avditvs (Independent)
+
+***
+
+## Install the latest Windows build
+You first need to install the latest build of Windows (version 20145 or higher). To do so, you'll have to subscribe to the [Microsoft Windows Insider Program](https://insider.windows.com/en-us/getting-started#register)
+
+Then, install the latest build from the Fast Ring by following the provided instructions.
+
+## Ubuntu 18.04 running is WSL2
+First, install WSL2 by following the [instructions](https://docs.microsoft.com/en-us/windows/wsl/install-win10) provided by the Microsoft documentation.  
+After that you can download the Ubuntu 18.04 distribution from the Windows Store.
+
+You should now be able to launch it from the Windows terminal or the Windows start menu.  
+
+Finally check that the kernel version is superior than 4.19.121 with the following command `uname -r`. If not, that means that the latest windows build is not installed.
+
+## Install the NVIDIA driver for WSL2
+Using CUDA with WSL2 requires specific drivers. Install them by following [these](https://developer.nvidia.com/cuda/wsl) instructions.  
+
+Once installed, you can make sure that the driver is working and check the version by running the command in a Windows PowerShell terminal : `nvidia-smi`
+
+Note : You may encounter issues the 465.12, if this version is not working, revert to a previous WSL2 enabled one (460.20 for instance).
+
+## Install the libraries
+The last step is to install the libraries inside the Ubuntu 18.04 virtual machine. This can be done by running the commands below in the Ubuntu terminal.
+
+<pre class="prettyprint lang-bsh">
+# Add NVIDIA package repositories
+<code class="devsite-terminal">wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.1.243-1_amd64.deb</code>
+<code class="devsite-terminal">sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub</code>
+<code class="devsite-terminal">sudo dpkg -i cuda-repo-ubuntu1804_10.1.243-1_amd64.deb</code>
+<code class="devsite-terminal">sudo apt-get update</code>
+<code class="devsite-terminal">wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb</code>
+<code class="devsite-terminal">sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb</code>
+<code class="devsite-terminal">sudo apt-get update</code>
+
+# Install development and runtime libraries (~4GB)
+<code class="devsite-terminal">sudo apt-get install --no-install-recommends \
+    cuda-10-1 \
+    libcudnn7=7.6.5.32-1+cuda10.1  \
+    libcudnn7-dev=7.6.5.32-1+cuda10.1
+</code>
+
+# Install TensorRT. Requires that libcudnn7 is installed above.
+<code class="devsite-terminal">sudo apt-get install -y --no-install-recommends libnvinfer6=6.0.1-1+cuda10.1 \
+    libnvinfer-dev=6.0.1-1+cuda10.1 \
+    libnvinfer-plugin6=6.0.1-1+cuda10.1
+</code>
+
+#Add installation directories of libraries to this environment variable below.
+<code class="devsite-terminal">export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/include:/usr/local/cuda-10.2/targets/x86_64-linux/lib"
+</code>
+</pre>
+
+## Check if everything is working
+You can check if the GPU is available by running the following command inside a Python terminal (in Ubuntu):  
+
+```
+import tensorflow as tf
+gpus = tf.config.list_logical_devices(
+    device_type='GPU'
+)
+print(gpus)
+```
+