Enable PyTorch with DirectML on WSL 2

This preview provides students and beginners a way to start building your knowledge in the machine-learning (ML) space on your existing hardware by using the PyTorch with DirectML package. Once set up, you can start with our samples.

Note

Some information relates to pre-released product, which may be substantially modified before it's commercially released. Microsoft makes no warranties, express or implied, with respect to the information provided here.

Check your version of Windows

The PyTorch with DirectML package on native Windows Subsystem for Linux (WSL) works starting with Windows 11. You can check your build version number by running winver via the Run command (Windows logo key + R).

Check for GPU driver updates

Ensure you have the latest GPU driver installed. Select check for updates in the Windows Update section of the Settings app.

Set up the PyTorch with DirectML preview

Install WSL 2

To install the Windows Subsystem for Linux (WSL) 2, see the instructions in Install WSL.

Then install the WSL GUI driver by following the instructions in the README.md file in the microsoft/wslg GitHub repository.

Set up a Python environment

We recommend that you set up a virtual Python environment inside your WSL 2 instance. There are many tools that you can use to set up a virtual Python environment—in this topic we'll use Anaconda's Miniconda. The rest of this setup assumes that you use a Miniconda environment.

Install Miniconda by following the installation guidance on Anaconda's site, or by running the following commands in WSL.

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh 
bash Miniconda3-latest-Linux-x86_64.sh

Once Miniconda is installed, create a Python environment named directml, and activate it through the following commands:

conda create --name directml -y
conda activate directml

Install PyTorch and the Torch-DirectML Plugin

Note

The PyTorch-directml package supports only PyTorch 1.13.

The latest release of Torch-DirectML follows a plugin model, meaning you have two packages to install. First, install the pytorch dependencies by running the following commands:

conda install numpy pandas tensorboard matplotlib tqdm pyyaml -y
pip install opencv-python
pip install wget
pip install torchvision

Then, install PyTorch. For our purposes you only need to install the cpu version, but if you need other compute platforms then follow the installation instructions on PyTorch's website.

conda install pytorch cpuonly -c pytorch

Finally, install the PyTorch-DirectML plugin.

pip install torch-directml

Verification and Device Creation

Once you've installed the Torch-DirectML package, you can verify that it runs correctly by adding two tensors. First start an interactive Python session, and import Torch with the following lines:

import torch
import torch_directml
dml = torch_directml.device()

The current release of the Torch-DirectML plugin is mapped to the "PrivateUse1" Torch backend. The new torch_directml.device() API is a convenient wrapper for sending your tensors to the DirectML device.

With the DirectML device created, you can now define two simple tensors; one tensor containing a 1 and another containing a 2. Place the tensors on the "dml" device.

tensor1 = torch.tensor([1]).to(dml) # Note that dml is a variable, not a string!
tensor2 = torch.tensor([2]).to(dml)

Add the tensors together, and print the results.

dml_algebra = tensor1 + tensor2
dml_algebra.item()

You should see the number 3 being output, as in the example below.

>>> import torch
>>> tensor1 = torch.tensor([1]).to(dml)
>>> tensor2 = torch.tensor([2]).to(dml)
>>> dml_algebra = tensor1 + tensor2
>>> dml_algebra.item()
3

PyTorch with DirectML samples and feedback

Now you're ready to start learning more about ML training. Check out the our samples to get started. If you run into issues, or have feedback on the PyTorch with DirectML package, then please connect with our team here.