**Published Microsoft Container Registry (MCR) Images**
Use `docker pull` with any of the images and tags below to pull an image and try for yourself. Note that the CPU, CUDA, and TensorRT images include additional dependencies like miniconda for compatibility with AzureML image deployment.
**Example**: Run `docker pull mcr.microsoft.com/azureml/onnxruntime:latest-cuda` to pull the latest released docker image with ONNX Runtime GPU, CUDA, and CUDNN support.
Starting with the OpenVINO™ toolkit 2020.2 release, all of the features previously available through nGraph have been merged into the OpenVINO™ toolkit. As a result, all the features previously available through ONNX RT Execution Provider for nGraph have been merged with ONNX RT Execution Provider for OpenVINO™ toolkit.
Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Users are recommended to migrate to the ONNX RT Execution Provider for OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.
- Choose Dockerfile.openvino as the dockerfile for building an OpenVINO 2021.1 based Docker image. Providing the docker build argument DEVICE enables the onnxruntime build for that particular device. You can also provide arguments ONNXRUNTIME_REPO and ONNXRUNTIME_BRANCH to test that particular repo and branch. Default repository is http://github.com/microsoft/onnxruntime and default branch is master.
*This is the hardware accelerator target that is enabled by **default** in the container image. After building the container image for one default target, the application may explicitly choose a different target at run time with the same container by using the [Dynamic device selction API](https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/OpenVINO-ExecutionProvider.md#dynamic-device-selection).*
2. Install the Myriad rules drivers on the host machine according to the reference in [here](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-NCS-steps)
1. Download OpenVINO **Full package** for version **2021.1** for Linux on host machine from [this link](https://software.intel.com/en-us/openvino-toolkit/choose-download) and install it with the help of instructions from [this link](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html)
2. Install the drivers on the host machine according to the reference in [here](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux_ivad_vpu.html)
3. Build the docker image from the DockerFile in this repository.
The Dockerfile used in these instructions specifically targets Raspberry Pi 3/3+ running Raspbian Stretch. The same approach should work for other ARM devices, but may require some changes to the Dockerfile such as choosing a different base image (Line 0: `FROM ...`).
- Reported at the end of the build, after the `# Build Output` line.
- It should follow the format `onnxruntime-0.3.0-cp35-cp35m-linux_armv7l.whl`, but version number may have changed. You'll use this path to extract the wheel file later.
6. Check that the build succeeded
Upon completion, you should see an image tagged `onnxruntime-arm32v7` in your list of docker images:
```bash
docker images
```
7. Extract the Python wheel file from the docker image
(Update the path/version of the `.whl` file with the one noted in step 5)
Note: You may add --use_tensorrt and --tensorrt_home options if you wish to use NVIDIA TensorRT (support is experimental), as well as any other options supported by [build.sh script](build.sh).
2. After the Python wheel is successfully built, use 'find' command for Docker to install the wheel inside new image:
Note: Resulting Docker image will have ONNX Runtime installed in /usr, and ONNX Runtime wheel copied to /onnxruntime directory.
Nothing else from ONNX Runtime source tree will be copied/installed to the image.
Note: When running the container you built in Docker, please either use 'nvidia-docker' command instead of 'docker', or use Docker command-line options to make sure NVIDIA runtime will be used and appropiate files mounted from host. Otherwise, CUDA libraries won't be found. You can also [set NVIDIA runtime as default in Docker](https://github.com/dusty-nv/jetson-containers#docker-default-runtime).
Send HTTP requests to the docker container through the binding local port. Here is the full [usage document](https://github.com/Microsoft/onnxruntime/blob/master/docs/ONNX_Runtime_Server_Usage.md).
```
curl -X POST -d "@request.json" -H "Content-Type: application/json" http://0.0.0.0:{your_local_port}/v1/models/mymodel/versions/3:predict
