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README.md

Docker containers for ONNX Runtime

Build Instructions

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 build from source (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.

Build Flavor Base Image ONNX Runtime Docker Image tags Latest
Source (CPU) mcr.microsoft.com/azureml/onnxruntime :v0.4.0, :v0.5.0 :latest
CUDA (GPU) mcr.microsoft.com/azureml/onnxruntime :v0.4.0-cuda10.0-cudnn7, :v0.5.0-cuda10.1-cudnn7 :latest-cuda
TensorRT (x86) mcr.microsoft.com/azureml/onnxruntime :v0.4.0-tensorrt19.03, :v0.5.0-tensorrt19.06 :latest-tensorrt
OpenVino (VAD-M) mcr.microsoft.com/azureml/onnxruntime TBA TBA
OpenVino (MYRIAD) mcr.microsoft.com/azureml/onnxruntime TBA TBA
Server mcr.microsoft.com/onnxruntime/server :v0.4.0, :v0.5.0 :latest

Build from Source

Linux 16.04, CPU, Python Bindings

  1. Build the docker image from the Dockerfile in this repository.
# If you have a Linux machine, preface this command with "sudo"

docker build -t onnxruntime-source -f Dockerfile.source .
  1. Run the Docker image
# If you have a Linux machine, preface this command with "sudo"

docker run -it onnxruntime-source

CUDA

Linux 16.04, CUDA 10.0, CuDNN 7

  1. Build the docker image from the Dockerfile in this repository.
# If you have a Linux machine, preface this command with "sudo"

docker build -t onnxruntime-cuda -f Dockerfile.cuda .
  1. Run the Docker image
# If you have a Linux machine, preface this command with "sudo"

docker run -it onnxruntime-cuda

nGraph (Public Preview)

Linux 16.04, Python Bindings

  1. Build the docker image from the Dockerfile in this repository.
# If you have a Linux machine, preface this command with "sudo"

docker build -t onnxruntime-ngraph -f Dockerfile.ngraph .
  1. Run the Docker image
# If you have a Linux machine, preface this command with "sudo"

docker run -it onnxruntime-ngraph

TensorRT

Linux 16.04, TensorRT 5.0.2

  1. Build the docker image from the Dockerfile in this repository.
# If you have a Linux machine, preface this command with "sudo"

docker build -t onnxruntime-trt -f Dockerfile.tensorrt .
  1. Run the Docker image
# If you have a Linux machine, preface this command with "sudo"

docker run -it onnxruntime-trt

OpenVINO (Public Preview)

Linux 16.04, Python Bindings

  1. Build the onnxruntime image for all the accelerators supported as below

    Retrieve your docker image in one of the following ways.

    • For building the docker image, download OpenVINO online installer version 2019 R1.1 from here and copy the openvino tar file in the same directory and build the image. The online installer size is only 16MB and the components needed for the accelerators are mentioned in the dockerfile. Providing the argument device enables onnxruntime for that particular device. You can also provide arguments ONNXRUNTIME_REPO and ONNXRUNTIME_BRANCH to test that particular repo and branch. Default values are http://github.com/microsoft/onnxruntime and repo is master 
      docker build -t onnxruntime --build-arg DEVICE=$DEVICE .
    • Pull the official image from DockerHub.

  2. DEVICE: Specifies the hardware target for building OpenVINO Execution Provider. Below are the options for different Intel target devices.

    Device Option Target Device
    CPU_FP32 Intel CPUs
    GPU_FP32 Intel Integrated Graphics
    GPU_FP16 Intel Integrated Graphics
    MYRIAD_FP16 Intel MovidiusTM USB sticks
    VAD-M_FP16 Intel Vision Accelerator Design based on MovidiusTM MyriadX VPUs

CPU

  1. Retrieve your docker image in one of the following ways.

    • Build the docker image from the DockerFile in this repository.

      docker build -t onnxruntime-cpu --build-arg DEVICE=CPU_FP32 --network host .

    • Pull the official image from DockerHub.

      # Will be available with next release

  2. Run the docker image

     docker run -it onnxruntime-cpu

GPU

  1. Retrieve your docker image in one of the following ways.

    • Build the docker image from the DockerFile in this repository. 
       docker build -t onnxruntime-gpu --build-arg DEVICE=GPU_FP32 --network host .
    • Pull the official image from DockerHub. 
        # Will be available with next release

  2. Run the docker image

    docker run -it --device /dev/dri:/dev/dri onnxruntime-gpu:latest

Myriad VPU Accelerator

  1. Retrieve your docker image in one of the following ways.
    • Build the docker image from the DockerFile in this repository. 
       docker build -t onnxruntime-myriad --build-arg DEVICE=MYRIAD_FP16 --network host .
    • Pull the official image from DockerHub. 
       # Will be available with next release
  2. Install the Myriad rules drivers on the host machine according to the reference in here
  3. Run the docker image by mounting the device drivers 
    docker run -it --network host --privileged -v /dev:/dev  onnxruntime-myriad:latest

=======

VAD-M Accelerator Version

  1. Retrieve your docker image in one of the following ways.
    • Build the docker image from the DockerFile in this repository. 
       docker build -t onnxruntime-vadr --build-arg DEVICE=VAD-M_FP16 --network host .
    • Pull the official image from DockerHub. 
       # Will be available with next release
  2. Install the HDDL drivers on the host machine according to the reference in here
  3. Run the docker image by mounting the device drivers 
    docker run -it --device --mount type=bind,source=/var/tmp,destination=/var/tmp --device /dev/ion:/dev/ion  onnxruntime-hddl:latest

ONNX Runtime Server (Public Preview)

Linux 16.04

  1. Build the docker image from the Dockerfile in this repository
docker build -t {docker_image_name} -f Dockerfile.server .
  1. Run the ONNXRuntime server with the image created in step 1
docker run -v {localModelAbsoluteFolder}:{dockerModelAbsoluteFolder} -p {your_local_port}:8001 {imageName} --model_path {dockerModelAbsolutePath}
  1. Send HTTP requests to the container running ONNX Runtime Server

Send HTTP requests to the docker container through the binding local port. Here is the full usage document.

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

Nuphar (Public Preview)

Linux 16.04, Python Bindings

  1. Build the docker image from the Dockerfile in this repository.
# If you have a Linux machine, preface this command with "sudo"

docker build -t onnxruntime-nuphar -f Dockerfile.nuphar .
  1. Run the Docker image
# If you have a Linux machine, preface this command with "sudo"

docker run -it onnxruntime-nuphar