How to build a DeepLearning environment using WSL, Docker Desktop and GPUs in Windows 11.

How to build a DeepLearning environment using WSL, Docker Desktop and GPUs in Windows 11.

In this article, I will show you how to create a Deep Learning environment for Windows 11.

We will show you how to use NVIDIA GPUs from containers. Now you can quickly get a Deep Learning environment by quickly launching a public container for DeepLearning. Yay!

Install the driver to use CUDA with WSL.

First, download and install the NVIDIA driver for using CUDA with WSL from the following URL.


You will be able to use CUDA from WSL.

Install Docker Desktop

Next, download Docker Desktop from the following URL and install it.


Now you’re all set!
Docker Desktop now supports nvidia-docker, so you can use CUDA from the container. The following URL is an introductory article.


Let’s see if CUDA is available on Ubuntu.

Let’s start NVIDIA’s CUDA container and run nvidia-smi. This container is useful if you want to install TensorFlow or PyTorch on your own.

command (you can run it from PowerShell or Command. The following specifies Ubuntu 20.04 with CUDA 11.6 installed)

docker run -it --gpus=all --rm nvidia/cuda:11.6.0-base-ubuntu20.04 nvidia-smi

Execution result

| NVIDIA-SMI 510.00       Driver Version: 510.06       CUDA Version: 11.6     |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|   0  NVIDIA GeForce ...  On   | 00000000:01:00.0  On |                  N/A |
| 30%   30C    P8    31W / 350W |   1187MiB / 24576MiB |     N/A      Default |
|                               |                      |                  N/A |

| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|  No running processes found                                                 |

Some frameworks support different versions of CUDA, so you can use the version of CUDA you want to use simply by specifying it in the container tag.
There is no need to reinstall CUDA and cuDNN anymore. You don’t need to reinstall CUDA and cuDNN.


Let’s check if CUDA can be used with TensorFlow.

TensorFlow is also just used from a container. No installation is required.

command (run it from PowerShell or Command. The following specifies the latest container)

docker run --gpus all -it --rm tensorflow/tensorflow:latest-gpu python -c "from tensorflow.python.client import device_lib; import os; os.environ['TF_CPP_MIN_LOG_LEVEL']='1'; print(device_lib.list_local_devices())"

Execution result

[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
incarnation: 7510238269992894144
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 22681550848
locality {
  bus_id: 1
  links {
incarnation: 3708138668520980037
physical_device_desc: "device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:01:00.0, compute capability: 8.6"
xla_global_id: 416903410

You can specify the version by referring to the following URL.


You can also use NGC’s TensorFlow provided by NVIDIA. tf1 is Version 1 series, and tf2 is Version 2 series.


Let’s see if we can use CUDA with PyTorch.

PyTorch is also just used from a container. No installation is required.

Command (run from PowerShell or Command. (The following specifies the NGC container)

docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:21.12-py3 python -c "import torch; print('version={}\ncuda is available={}\ncuda device count={}'.format(torch.__version__, torch.cuda.is_available(), torch.cuda.device_count()))"

Execution results (excerpt)

cuda is available=True
cuda device count=1

You can specify the version by referring to the following URL


To actually run the program

If you are familiar with containers, you probably don’t need any explanation, but it is recommended that you mount a Windows folder when starting a container. Edit the program in the mounted folder.

Using Jupyter Notebook

If you are using Jupyter Notebook, you may want to set up port forwarding as well.

In the following TensorFlow startup, we are running bash. We also set up a mount of the Windows D:\work folder to the container’s /work, and forward the container’s 8888 port to the Windows 8888 port.

docker run --gpus all -it -p 8888:8888 -v D:\work:/work nvcr.io/nvidia/tensorflow:21.12-tf2-py3 bash

After launching, you can access Jupyter Notebook in your Windows browser by referring to the following URL that appears after launching Jupyter Notebook.

jupyter notebook


    Or copy and paste this URL:

When accessing the site with a browser, where it says hostname, change it to localhost.

Using Visual Studio Code

VSCode’s Remote Developent (Remote - Contaners) plugin allows you to attach to a container from Visual Studio Code and use file editing, terminal and debugging.
If you only want to edit files, you don’t need to use this plugin. You can edit the mounted files directly.

Once the plugin is installed, click on the bottom left and select Attach to Container.


I can now run packages that were only available for Linux without difficulty.
WSL, CUDA on WSL, and Docker Desktop are the best!