Package: openvino-tensorflow
OpenVINO™ integration with TensorFlowOpenVINO™ integration with TensorFlowis a product designed for TensorFlow* developers who want to get started withOpenVINO™in their inferencing applications. This product deliversOpenVINO™inline optimizations which enhance inferencing performance with minimal code modifications.OpenVINO™ integration with TensorFlowaccelerates inference across manyAI modelson a variety of Intel®silicon such as:Intel®CPUsIntel®integrated and discrete GPUsNote: Support for Intel Movidius™ MyriadX VPUs is no longer maintained. Consider previous releases for running on Myriad VPUs.[Note: For maximum performance, efficiency, tooling customization, and hardware control, we recommend the developers to adopt native OpenVINO™ APIs and its runtime.]InstallationRequirementsUbuntu 18.04, macOS 11.2.3 or Windows110 - 64 bitPython* 3.7, 3.8 or 3.9TensorFlow* v2.9.31Windows release supports only Python3.9ThisOpenVINO™ integration with TensorFlowpackage comes with pre-built libraries of OpenVINO™ version 2022.3.0 meaning you do not have to install OpenVINO™ separately.
This package supports:Intel®CPUsIntel®integrated GPUspip3 install -U pip
  pip3 install tensorflow==2.9.3
  pip3 install openvino-tensorflow==2.3.0For installation instructions on Windows please refer toOpenVINO™ integration with TensorFlowfor WindowsFor more details on installation please refer toINSTALL.md, and for build from source options please refer toBUILD.mdVerify InstallationOnce you have installedOpenVINO™ integration with TensorFlow, you can use TensorFlow to run inference using a trained model.To check ifOpenVINO™ integration with TensorFlowis properly installed, runpython3 -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:  2.9.3
    OpenVINO integration with TensorFlow version: b'2.3.0'
    OpenVINO version used for this build: b'2022.3.0'
    TensorFlow version used for this build: v2.9.3
    CXX11_ABI flag used for this build: 1UsageBy default, Intel®CPU is used to run inference. However, you can change the default option to Intel®integrated or discrete GPUs (GPU, GPU.0, GPU.1 etc). 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'.To determine what processing units are available on your system for inference, use the following function:openvino_tensorflow.list_backends()For further performance improvements, it is advised to set the environment variable OPENVINO_TF_CONVERT_VARIABLES_TO_CONSTANTS=1. For more API calls and environment variables, seeUSAGE.md.[Note: If a CUDA capable device is present in the system then set the environment variable CUDA_VISIBLE_DEVICES to -1]ExamplesTo see what you can do withOpenVINO™ integration with TensorFlow, explore the demos located in theexamplesrepository.Docker SupportDockerfiles for Ubuntu* 18.04, Ubuntu* 20.04, and TensorFlow* Serving are provided which can be used to build runtime Docker* images forOpenVINO™ integration with TensorFlowon CPU, GPU.
For more details seedocker readme.Prebuilt ImagesUbuntu18 runtime image on Docker* HubUbuntu20 runtime image on Docker* HubAzure* MarketplaceTry it on Intel®DevCloudSample tutorials are also hosted onIntel®DevCloud. The demo applications are implemented using Jupyter Notebooks. You can interactively execute them on Intel®DevCloud nodes, compare the results ofOpenVINO™ integration with TensorFlow, native TensorFlow and OpenVINO™.LicenseOpenVINO™ integration with TensorFlowis licensed underApache 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.SupportPlease submit your questions, feature requests and bug reports viaGitHub issues.How to ContributeWe welcome community contributions toOpenVINO™ integration with TensorFlow. If you have an idea for improvement:Share your proposal viaGitHub issues.Submit apull 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 buildOpenVINO™ integration with TensorFlowand run all the examples with your fix/patch. If you want to introduce a large feature, create test cases for your feature. Upon the 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.* Other names and brands may be claimed as the property of others.