dockerfiles

Docker containers for ONNX Runtime

Build Flavors: Dockerfiles

• Arm 32v7
• Build from source (CPU)
• CUDA + CUDNN
• nGraph
• TensorRT
• OpenVINO
• ONNX Runtime Server
• Nuphar

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	:v0.5.0-openvino-r1.1-vadm	:latest-openvino-vadm
OpenVino (MYRIAD)	mcr.microsoft.com/azureml/onnxruntime	:v0.5.0-openvino-r1.1-myriad	:latest-openvino-myriad
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 .

2. 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 .

2. 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 .

2. 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 .

2. 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 one of the accelerators supported below.

   Retrieve your docker image in one of the following ways.

   • To build your docker image, download the OpenVINO online installer version 2019 R1.1 for Linux from this link and copy the OpenVINO tar file to the same directory before building the Docker image. The online installer size is 16MB and the components needed for the accelerators are mentioned in the dockerfile. 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.

     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 .

2. 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}

3. 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 .

2. Run the Docker image

# If you have a Linux machine, preface this command with "sudo"

docker run -it onnxruntime-nuphar