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Dockerfiles for Ubuntu* 18.04 and Ubuntu* 20.04

We provide Dockerfiles for Ubuntu* 18.04 and Ubuntu* 20.04 which can be used to build runtime Docker* images for OpenVINO™ integration with TensorFlow on CPU, GPU, VPU, and VAD-M. They contain all required runtime python packages, and shared libraries to support execution of a TensorFlow Python app with the OpenVINO™ backend. By default, it hosts a Jupyter server with an Image Classification and an Object Detection sample that demonstrate the performance benefits of using OpenVINO™ integration with TensorFlow.

Build the docker image

docker build -t openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0 - < ubuntu20/openvino_tensorflow_cgvh_runtime_2.0.0.dockerfile

Launch the Jupyter server with CPU access:

docker run -it --rm \
	   -p 8888:8888 \
	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0

Launch the Jupyter server with iGPU access:

docker run -it --rm \
	   -p 8888:8888 \
	   --device-cgroup-rule='c 189:* rmw' \
	   --device /dev/dri:/dev/dri \
	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0

Launch the Jupyter server with MYRIAD access:

docker run -it --rm \
	   -p 8888:8888 \
	   --device-cgroup-rule='c 189:* rmw' \
	   -v /dev/bus/usb:/dev/bus/usb \
	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0

Launch the Jupyter server with VAD-M access:

docker run -itu root:root --rm \
	   -p 8888:8888 \
	   --device-cgroup-rule='c 189:* rmw' \
	   --mount type=bind,source=/var/tmp,destination=/var/tmp \
	   --device /dev/ion:/dev/ion \
	   -v /dev/bus/usb:/dev/bus/usb \
	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0

Run image with runtime target /bin/bash for container shell with CPU, iGPU, and MYRIAD device access

docker run -itu root:root --rm \
	   -p 8888:8888 \
	   --device-cgroup-rule='c 189:* rmw' \
	   --device /dev/dri:/dev/dri \
	   --mount type=bind,source=/var/tmp,destination=/var/tmp \
	   -v /dev/bus/usb:/dev/bus/usb \
	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0 /bin/bash

If execution fails on iGPU for 10th and 11th Generation Intel devices, provide docker build arg INTEL_OPENCL as 20.35.17767

docker build -t openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0 --build-arg INTEL_OPENCL=20.35.17767 - < ubuntu20/openvino_tensorflow_cgvh_runtime_2.0.0.dockerfile

Dockerfiles for TF-Serving with OpenVINOTM integration with Tensorflow

Build serving docker images:

  1. Build the runtime docker image. This dockerfile builds, and installs tensorflow model server binary onto the OpenVINOTM integration with Tensorflow runtime image.

     docker build -t openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0-serving -f ubuntu20/openvino_tensorflow_cgvh_runtime_2.0.0-serving.dockerfile .

Here is an example to serve Resnet50 model using OpenVINO™ Integration with Tensorflow and a client script that performs inference on the model using the REST API.

  1. Download Resnet50 model from TF Hub and untar its contents into the folder resnet_v2_50_classifiation/5

  2. Start serving container for the resnet50 model:

    To run on CPU backend:

     docker run -it --rm \
 	   -p 8501:8501 \
 	   -v <path to resnet_v2_50_classifiation>:/models/resnet \
 	   -e MODEL_NAME=resnet \
 	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0-serving

    To run on iGPU:

     docker run -it --rm \
 	   -p 8501:8501 \
 	   --device-cgroup-rule='c 189:* rmw' \
 	   --device /dev/dri:/dev/dri \
 	   -v <path to resnet_v2_50_classifiation>:/models/resnet \
 	   -e MODEL_NAME=resnet \
 	   -e OPENVINO_TF_BACKEND=GPU \
 	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0-serving

    To run on MYRIAD:

     docker run -it --rm \
 	   -p 8501:8501 \
 	   --device-cgroup-rule='c 189:* rmw' \
 	   -v /dev/bus/usb:/dev/bus/usb \
 	   -v <path to resnet_v2_50_classifiation>:/models/resnet \
 	   -e MODEL_NAME=resnet \
 	   -e OPENVINO_TF_BACKEND=MYRIAD \
 	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0-serving

    To run on VAD-M:

     docker run -itu root:root --rm \
 	   -p 8501:8501 \
 	   --device-cgroup-rule='c 189:* rmw' \
 	   -v /dev/bus/usb:/dev/bus/usb \
 	   --mount type=bind,source=/var/tmp,destination=/var/tmp \
 	   --device /dev/ion:/dev/ion \
 	   -v <path to resnet_v2_50_classifiation>:/models/resnet \
 	   -e OPENVINO_TF_BACKEND=VAD-M \
 	   -e MODEL_NAME=resnet \
 	   openvino/openvino_tensorflow_ubuntu20_runtime:2.0.0-serving

  3. Run the script to send inference request from client and get predictions from server.

     wget https://raw.githubusercontent.com/tensorflow/serving/master/tensorflow_serving/example/resnet_client.py
 python resnet_client.py

Prebuilt Images

Ubuntu 18 runtime image on Docker* Hub

Ubuntu 20 runtime image on Docker* Hub

Azure* Marketplace

* Other names and brands may be claimed as the property of others.