The Windows ML container is intended for use by developers who are already familiar with the basics of IoT and Windows ML development. To learn more about using Windows ML, see the Windows ML docs.
Prerequisites and environment setup
To install the Insider version of Windows, you will need to join the Windows Insider Program.
Once you have installed the Insider release of Windows host, you can run
winver in a command prompt to find the host version.
In the example above, the host version is
Host OS and container version matching requirements
Windows ML container runs on top of the Windows Enterprise host. Your version of the Windows ML container must be an exact match to the version of the fast ring Windows 10 Insider Preview Build (20H1).
Windows ML container on Docker hub
Once you have identified the version of the host, find the matching tag on Docker Hub page for Window ML container Insider. Locate a corresponding version tag and Docker pull URL.
10.0.18999.1 (20H1) docker pull mcr.microsoft.com/windows/ml/insider:10.0.18999.1
Visual Studio 2019
We recommend you use Visual Studio 2019 when developing for the Windows ML container. The latest Community edition is available free at the Visual Studio site. If you have not worked with Visual Studio before, follow the instructions and guidance on the site for more information.
When configuring your Visual Studio installation, please ensure you have installed the following packages:
- Universal Windows Platform development
- .NET Core cross-platform development.NET Core 2.2 development tools
Windows SDK Insider Preview
Ensure that the version of your Windows ML container and your Windows SDK Insider Preview are matched as closely as possible. An exact version match is not required, but the larger the mismatch, the greater the chance of errors or other issues.
The latest Insider SDK is available here.
Configure Visual Studio 2019 NuGet package source
While Windows ML container is in Preview, the Windows Headless WinRT contract is provided via NuGet package from a separate package source.
Make sure the following sources are in the package source:
To add the new source, follow these steps:
- Open Visual Studio 2019
- Select Tools -> NuGet Package Manager -> Package Manager Settings -> Package Sources and add the package SDK flight source URL.
For GPU acceleration to be supported by the container, the host must be able to use a GPU and graphics driver that meets these requirements:
|Vendor||Architecture||Typical customer-facing GPU names|
|AMD||GCN 4 or later||Radeon RX 400 series or later, or Radeon Pro WX series|
|Intel||Kaby Lake or later||Intel HD Graphics 600 series or later|
|NVIDIA||Kepler or later||GeForce 600 series or later, or Quadro K-series or later|
Graphics driver requirements
|Vendor||Min driver version|
If the GPU or driver does not meet the requirements above, or if the container host OS is running in a VM, then only CPU-based inferencing is supported.
Graphics driver installation
Download the most recent graphics drivers for your system by navigating to Settings > Update & Security > Windows Update > Check for updates.
Troubleshooting graphics driver
Check that your driver version meets the minimum requirements by navigating to Device Manager > Display Adapters, right-clicking on your graphics adapter and selecting Properties, and looking under the Driver tab:
If you are unable to download a graphics driver through Windows Update that meets the minimum version requirements, please email email@example.com with your system’s DxDiag information attached. For instructions on running and saving DxDiag information, see this support page.
Set up and test a basic environment
Ensure your host OS and Windows ML container image share the same version number.
Enable the Containers functionality on the host OS.
Run the following command in an elevated command window. You may be prompted to restart the system.
dism /online /Enable-Feature /FeatureName:Containers
- Download the nightly build versions of docker.exe and dockerd.exe.
curl.exe -o %windir%\system32\dockerd.exe https://master.dockerproject.org/windows/x86_64/dockerd.exe
curl.exe -o %windir%\system32\docker.exe https://master.dockerproject.org/windows/x86_64/docker.exe
Register and start the Docker service.
dockerd.exe --register-service net start docker
You should see the following output message.
The Docker Engine service is starting. The Docker Engine service was started successfully.
- You can confirm that Docker is running correct correctly with the following command.
This should produce the following output message.
Client: Version: master-dockerproject-2019-08-03 API version: 1.40 Go version: go1.12.7 Git commit: e505a7c2 Built: Sun Aug 4 00:02:51 2019 OS/Arch: windows/amd64 Experimental: false Server: Engine: Version: master-dockerproject-2019-08-03 API version: 1.41 (minimum version 1.24) Go version: go1.12.7 Git commit: 7449ca3 Built: Sun Aug 4 00:12:27 2019 OS/Arch: windows/amd64 Experimental: false
- Once Docker is started, import the container image file into Docker using the following command.
docker pull mcr.microsoft.com/windows/ml/insider:10.0.18999.1 docker tag mcr.microsoft.com/windows/ml/insider:10.0.18999.1 windowsml:latest
- After the image is installed, you can run
docker imagesto enumerate all available container images. By default, this image will not have a repository name or tag. You may specify one, or simply use the image ID from the hash provided to reference the image in future steps.
The output may look similar to this.
C:\Windows\system32>docker images REPOSITORY TAG IMAGE ID CREATED SIZE <none> <none> a9d5d08d079f 10 seconds ago 319MB C:\Windows\system32>docker tag a9d5d08d079f windowsml:latest C:\Windows\system32>docker images REPOSITORY TAG IMAGE ID CREATED SIZE windowsml latest a9d5d08d079f 25 seconds ago 319MB
- Download WinMLRunner v188.8.131.52 from https://github.com/microsoft/Windows-Machine-Learning/releases/tag/184.108.40.206 with the following command.
curl -o WinMLRunner.zip -L https://github.com/microsoft/Windows-Machine-Learning/releases/download/220.127.116.11/WinMLRunner.v18.104.22.168.zip
Then, unzip the .zip into the current folder.
- Download SqueezeNet.onnx sample from https://github.com/microsoft/Windows-Machine-Learning/tree/22.214.171.124/SharedContent/models with the following command.
curl -o SqueezeNet.onnx -L https://github.com/microsoft/Windows-Machine-Learning/raw/126.96.36.199/SharedContent/models/SqueezeNet.onnx
- Create a Dockerfile to copy the necessary files into the imported Windows ML container image.
You can copy the commands below to directly create a Dockerfile:
echo FROM windowsml:latest > Dockerfile echo WORKDIR C:/App >> Dockerfile echo COPY ./x64/WinMLRunner.exe C:/App/ >> Dockerfile echo COPY ./SqueezeNet.onnx C:/App/ >> Dockerfile
C:\tgz>type Dockerfile FROM windowsml:latest WORKDIR C:/App COPY ./x64/WinMLRunner.exe C:/App/ COPY ./SqueezeNet.onnx C:/App/
- Build a new container based on the Dockerfile, with the
docker build command.
C:\tgz>docker build -t winmlrunner:latest .
Results will be similar to the following.
Sending build context to Docker daemon 5.525MB Step 1/4 : FROM windowsml:latest ---> a9d5d08d079f Step 2/4 : WORKDIR C:/App ---> Running in 37e9d759365f Removing intermediate container 37e9d759365f ---> 8ab270ff4deb Step 3/4 : COPY ./x64/WinMLRunner.exe C:/App/ ---> 16e2b23a5de0 Step 4/4 : COPY ./SqueezeNet.onnx C:/App/ ---> 05269bb7c5f8 Successfully built 05269bb7c5f8 Successfully tagged winmlrunner:latest C:\tgz>docker images REPOSITORY TAG IMAGE ID CREATED SIZE winmlrunner latest ed74203b2655 About a minute ago 325MB windowsml latest a9d5d08d079f 23 minutes ago 319MB
- Launch a GPU-enabled process-isolated container based on the Windows ML container and WinMLRunner image.
docker run -it --isolation process --device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599 winmlrunner:latest cmd /k
There are a few important arguments that you must specify for running Windows ML containers using Docker.
Used to allow interactive shell components to forward
tty. If you omit this argument, the command line interface will not function properly.
--isolation processSpecifies the type of container as a process-isolated container.
--device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599Specifies the class GUID for the device that should be exposed into the container.
GUID_DEVINTERFACE_DISPLAY_ADAPTER, which enables DirectX GPUs.
winmlrunner:latestSpecifies the image to run, that was created in previous steps. This can either be the repository name and tag you provided during a
docker tagcommand, or the image ID/hash of the image. If you used latest as the tag name, it can be omitted.
This is the command that docker will execute inside the container.
- From the container command line, run WinMLRunner using CPU.
WinMLRunner.exe -model C:/App/SqueezeNet.onnx -cpu
Output should appear similar to the following.
DXGI module not found. Loading model (path = C:/App/SqueezeNet.onnx)... ================================================================= Name: squeezenet_old Author: onnx-caffe2 Version: 9223372036854775807 Domain: Description: Path: C:/App/SqueezeNet.onnx Support FP16: false Input Feature Info: Name: data_0 Feature Kind: Float Output Feature Info: Name: softmaxout_1 Feature Kind: Float ================================================================= Creating Session with CPU device Binding (device = CPU, iteration = 1, inputBinding = CPU, inputDataType = Tensor, deviceCreationLocation = WinML)...[SUCCESS] Evaluating (device = CPU, iteration = 1, inputBinding = CPU, inputDataType = Tensor, deviceCreationLocation = WinML)...[SUCCESS]
Congratulations, your environment is now set up correctly to use Windows ML container.
- You can also run WinMLRunner using the GPU. Specify one of AMD Radeon, Nvidia or Intel through the
-GPUAdapterNamecommand line argument.
WinMLRunner.exe -model C:/App/SqueezeNet.onnx -GPUAdapterName [radeon/nvidia/intel]
Build Apps the for Windows ML container
Samples for Windows ML container
To get started, make sure your Visual Studio 2019 is set up and configured according to the above instructions. Then, try the following samples:
- CustomVision. This sample uses the model trained by Azure Custom Vision Service. The trained model is exported as an ONNX file, and included as part of the sample app that runs inside the container.
- SqueezeNetObjectDetection. This app (cpp and c# only) uses the SqueezeNet model to detect the predominant object in an image.
Create a Visual Studio 2019 C# project from scratch
Windows ML container only supports a subset of Windows APIs due to its small size. When you create a Visual Studio project, you can specify this smaller API surface, to detect unsupported APIs before runtime.
To use the Headless WinRT API contract:
- In Visual Studio 2019, create a new C# Console App (.NET Core) project.
Select Tools->Nuget->Package Manager Console
In the Package Manager Console, run:
Install-Package Microsoft.Windows.SDK.Headless.Contracts -Prerelease
Create a Visual Studio 2019 C++ project from scratch
- In Visual Studio 2019, create a new C++ Console App project.
Select Tools->Nuget->Package Manager Console
In the Package Manager Console, run:
Install-Package Microsoft.Windows.SDK.Headless.Contracts -Prerelease Install-Package Microsoft.Windows.CppWinRT -Version 2.0.190730.2
- Update project to use
- Right click on your project.
- Choose properties
- In the dialog choose Linker->Input
- Update Additional Dependencies to include
windowscoreheadless.lib. For example:
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