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OpenVINO™ integration with TensorFlow

This repository contains the source code of OpenVINO™ integration with TensorFlow, designed for TensorFlow* developers who want to get started with OpenVINO™ in their inferencing applications. TensorFlow* developers can now take advantage of OpenVINO™ toolkit optimizations with TensorFlow inference applications across a wide range of Intel® compute devices by adding just two lines of code.

import openvino_tensorflow
openvino_tensorflow.set_backend('<backend_name>')

This product delivers OpenVINO™ inline optimizations which enhance inferencing performance with minimal code modifications. OpenVINO™ integration with TensorFlow accelerates inference across many AI models on a variety of Intel^® silicon such as:

• Intel^® CPUs
• Intel^® integrated GPUs
• Intel^® Movidius™ Vision Processing Units - referred to as VPU
• Intel^® Vision Accelerator Design with 8 Intel Movidius™ MyriadX VPUs - referred to as VAD-M or HDDL

[Note: For maximum performance, efficiency, tooling customization, and hardware control, we recommend the developers to adopt native OpenVINO™ APIs and its runtime.]

Installation

Prerequisites

• Ubuntu 18.04, 20.04, macOS 11.2.3 or Windows¹ 10 - 64 bit
• Python* 3.7, 3.8 or 3.9
• TensorFlow* v2.8.0

¹Windows package supports only Python3.9

Check our Interactive Installation Table for a menu of installation options. The table will help you configure the installation process.

The OpenVINO™ integration with TensorFlow package comes with pre-built libraries of OpenVINO™ version 2022.1.0. The users do not have to install OpenVINO™ separately. This package supports:

• Intel^® CPUs

• Intel^® integrated GPUs

• Intel^® Movidius™ Vision Processing Units (VPUs)

    pip3 install -U pip
    pip3 install tensorflow==2.8.0
    pip3 install openvino-tensorflow==2.0.0
  

For installation instructions on Windows please refer to OpenVINO™ integration with TensorFlow for Windows

To use Intel^® integrated GPUs for inference, make sure to install the Intel® Graphics Compute Runtime for OpenCL™ drivers

To leverage Intel® Vision Accelerator Design with Movidius™ (VAD-M) for inference, install OpenVINO™ integration with TensorFlow alongside the Intel® Distribution of OpenVINO™ Toolkit.

For more details on installation please refer to INSTALL.md, and for build from source options please refer to BUILD.md

Configuration

Once you've installed OpenVINO™ integration with TensorFlow, you can use TensorFlow* to run inference using a trained model.

For further performance improvements, it is advised to enable oneDNN Deep Neural Network Library (oneDNN) by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1.

To see if OpenVINO™ integration with TensorFlow is properly installed, run

python3 -c "import tensorflow as tf; print('TensorFlow version: ',tf.__version__);\
            import openvino_tensorflow; print(openvino_tensorflow.__version__)"

This should produce an output like:

    TensorFlow version:  2.8.0
    OpenVINO integration with TensorFlow version: b'2.0.0'
    OpenVINO version used for this build: b'2022.1.0'
    TensorFlow version used for this build: v2.8.0
    CXX11_ABI flag used for this build: 0

By default, Intel^® CPU is used to run inference. However, you can change the default option to either Intel^® integrated GPU or Intel^® VPU for AI inferencing. Invoke the following function to change the hardware on which inferencing is done.

openvino_tensorflow.set_backend('<backend_name>')

Supported backends include 'CPU', 'GPU', 'GPU_FP16', 'MYRIAD', and 'VAD-M'.

To determine what processing units are available on your system for inference, use the following function:

openvino_tensorflow.list_backends()

For more API calls and environment variables, see USAGE.md.

Examples

To see what you can do with OpenVINO™ integration with TensorFlow, explore the demos located in the examples directory.

Docker Support

Dockerfiles for Ubuntu* 18.04, Ubuntu* 20.04, and TensorFlow* Serving are provided which can be used to build runtime Docker* images for OpenVINO™ integration with TensorFlow on CPU, GPU, VPU, and VAD-M. For more details see docker readme.

Prebuilt Images

• Ubuntu 18 runtime image on Docker* Hub
• Ubuntu 20 runtime image on Docker* Hub
• Azure* Marketplace

Try it on Intel^® DevCloud

Sample tutorials are also hosted on Intel^® DevCloud. The demo applications are implemented using Jupyter Notebooks. You can interactively execute them on Intel^® DevCloud nodes, compare the results of OpenVINO™ integration with TensorFlow, native TensorFlow and OpenVINO™.

License

OpenVINO™ integration with TensorFlow is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

Support

Submit your questions, feature requests and bug reports via GitHub issues.

How to Contribute

We welcome community contributions to OpenVINO™ integration with TensorFlow. If you have an idea for improvement:

• Share your proposal via GitHub issues.
• Submit a pull request.

We will review your contribution as soon as possible. If any additional fixes or modifications are necessary, we will guide you and provide feedback. Before you make your contribution, make sure you can build OpenVINO™ integration with TensorFlow and run all the examples with your fix/patch. If you want to introduce a large feature, create test cases for your feature. Upon our verification of your pull request, we will merge it to the repository provided that the pull request has met the above mentioned requirements and proved acceptable.

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