Enable TensorFlow with DirectML in WSL
Windows 11 and Windows 10, version 21H2 provide students, beginners, and professionals a way to run machine learning (ML) training on their existing hardware by using the TensorFlow with DirectML package. Once set up, you can use your exisiting model scripts or check out a few samples on the DirectML repo.
Install Windows 11 or Windows 10, version 21H2
Install the latest GPU driver
Before installing the TensorFlow with DirectML package inside WSL, you need to install the latest drivers from your GPU hardware vendor. These drivers enable the Windows GPU to work with WSL.
Either select Check for updates in the Windows Update section of the Settings app or check your GPU hardware vendors website.
Download and install AMD's driver from their website. This functionality is supported on the following hardware:
- AMD Radeon™ RX series and Radeon™ VII graphics.
- AMD Radeon™ Pro series graphics.
- AMD Ryzen™ and Ryzen™ PRO Processors with Radeon™ Vega graphics.
- AMD Ryzen™ and Ryzen™ PRO Mobile Processors with Radeon™ Vega graphics.
For a complete list of compatible AMD products, please refer to the AMD Release Notes.
Download and install Intel's driver to use with DirectML from their website.
Set up TensorFlow with DirectML
Once you've installed the above driver, ensure you enable WSL and install a glibc-based distribution (like Ubuntu or Debian). For our testing, we used Ubuntu. Ensure you have the latest kernel by selecting Check for updates in the Windows Update section of the Settings app.
Ensure you have the Receive updates for other Microsoft products when you update Windows enabled. You can find it in Advanced options within the Windows Update section of the Settings app.
For these features, you need a kernel version of 220.127.116.11 or higher. You can check the version number by running the following command in PowerShell.
wsl cat /proc/version
Set up a Python environment
We recommend setting up a virtual Python environment inside your WSL instance. There are many tools you can use to setup a virtual Python environment—for these instructions, we'll use Anaconda's Miniconda. The rest of this setup assumes that you use a miniconda environment.
Install Miniconda by following the 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 in WSL, create an environment using Python named directml, and activate it through the following commands.
In the commands below, we use Python 3.6. However, the tensorflow-directml package works in a Python 3.5, 3.6 or 3.7 environment.
conda create --name directml python=3.6 conda activate directml
Install the TensorFlow with DirectML package
Install the TensorFlow with DirectML package through pip by running the following command.
The tensorflow-directml package only supports TensorFlow 1.15.
pip install tensorflow-directml
Once you've installed the tensorflow-directml package, you can verify that it runs correctly by adding two tensors. Copy the following lines into an interactive Python session.
import tensorflow.compat.v1 as tf tf.enable_eager_execution(tf.ConfigProto(log_device_placement=True)) print(tf.add([1.0, 2.0], [3.0, 4.0]))
You should see output similar to the following, with the add operator placed on the DML device.
2020-06-15 11:27:18.235973: I tensorflow/core/common_runtime/dml/dml_device_factory.cc:45] DirectML device enumeration: found 1 compatible adapters. 2020-06-15 11:27:18.240065: I tensorflow/core/common_runtime/dml/dml_device_factory.cc:32] DirectML: creating device on adapter 0 (AMD Radeon VII) 2020-06-15 11:27:18.323949: I tensorflow/stream_executor/platform/default/dso_loader.cc:60] Successfully opened dynamic library libdirectml.so.ba106a7c621ea741d21598708ee581c11918380 2020-06-15 11:27:18.337830: I tensorflow/core/common_runtime/eager/execute.cc:571] Executing op Add in device /job:localhost/replica:0/task:0/device:DML:0 tf.Tensor([4. 6.], shape=(2,), dtype=float32)
TensorFlow with DirectML samples and feedback
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