diff --git a/docs/Documentation/deployment_guide_introduction.md b/docs/Documentation/deployment_guide_introduction.md new file mode 100644 index 00000000000..6767bb28f95 --- /dev/null +++ b/docs/Documentation/deployment_guide_introduction.md @@ -0,0 +1,8 @@ +# Introduction to OpenVINO™ Deployment {#openvino_docs_deployment_guide_introduction} + + +Once you have a model that meets both OpenVINO™ and your requirements, you can choose among several ways of deploying it with your application: +* [Run inference and develop your app with OpenVINO™ Runtime](../OV_Runtime_UG/openvino_intro.md). +* [Deploy your application locally](../OV_Runtime_UG/deployment/deployment_intro.md). +* [Deploy your model online with the OpenVINO Model Server](@ref ovms_what_is_openvino_model_server). + diff --git a/docs/Documentation/dl_workbench_overview.md b/docs/Documentation/dl_workbench_overview.md new file mode 100644 index 00000000000..24dfd805b62 --- /dev/null +++ b/docs/Documentation/dl_workbench_overview.md @@ -0,0 +1,111 @@ +# OpenVINO™ Deep Learning Workbench Overview {#workbench_docs_Workbench_DG_Introduction} + +@sphinxdirective +.. toctree:: + :maxdepth: 1 + :hidden: + + workbench_docs_Workbench_DG_Install + workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets + Tutorials + User Guide + workbench_docs_Workbench_DG_Troubleshooting + +@endsphinxdirective + + + + +Deep Learning Workbench (DL Workbench) is an official OpenVINO™ graphical interface designed to make the production of pretrained deep learning Computer Vision and Natural Language Processing models significantly easier. + +Minimize the inference-to-deployment workflow timing for neural models right in your browser: import a model, analyze its performance and accuracy, visualize the outputs, optimize and make the final model deployment-ready in a matter of minutes. DL Workbench takes you through the full OpenVINO™ workflow, providing the opportunity to learn about various toolkit components. + +![](../img/openvino_dl_wb.png) + + +@sphinxdirective + +.. link-button:: workbench_docs_Workbench_DG_Start_DL_Workbench_in_DevCloud + :type: ref + :text: Run DL Workbench in Intel® DevCloud + :classes: btn-primary btn-block + +@endsphinxdirective + +DL Workbench enables you to get a detailed performance assessment, explore inference configurations, and obtain an optimized model ready to be deployed on various Intel® configurations, such as client and server CPU, Intel® Processor Graphics (GPU), Intel® Movidius™ Neural Compute Stick 2 (NCS 2), and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs. + +DL Workbench also provides the [JupyterLab environment](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Jupyter_Notebooks.html#doxid-workbench-docs-workbench-d-g-jupyter-notebooks) that helps you quick start with OpenVINO™ API and command-line interface (CLI). Follow the full OpenVINO workflow created for your model and learn about different toolkit components. + + +## Video + +@sphinxdirective + +.. list-table:: + + * - .. raw:: html + + + * - **DL Workbench Introduction**. Duration: 1:31 + +@endsphinxdirective + + +## User Goals + +DL Workbench helps achieve your goals depending on the stage of your deep learning journey. + +If you are a beginner in the deep learning field, the DL Workbench provides you with +learning opportunities: +* Learn what neural networks are, how they work, and how to examine their architectures. +* Learn the basics of neural network analysis and optimization before production. +* Get familiar with the OpenVINO™ ecosystem and its main components without installing it on your system. + +If you have enough experience with neural networks, DL Workbench provides you with a +convenient web interface to optimize your model and prepare it for production: +* Measure and interpret model performance. +* Tune the model for enhanced performance. +* Analyze the quality of your model and visualize output. + +## General Workflow + +The diagram below illustrates the typical DL Workbench workflow. Click to see the full-size image: + +![](../img/openvino_dl_wb_diagram_overview.svg) + +Get a quick overview of the workflow in the DL Workbench User Interface: + +![](../img/openvino_dl_wb_workflow.gif) + +## OpenVINO™ Toolkit Components + +The intuitive web-based interface of the DL Workbench enables you to easily use various +OpenVINO™ toolkit components: + +Component | Description +|------------------|------------------| +| [Open Model Zoo](https://docs.openvinotoolkit.org/latest/omz_tools_downloader.html)| Get access to the collection of high-quality pre-trained deep learning [public](https://docs.openvinotoolkit.org/latest/omz_models_group_public.html) and [Intel-trained](https://docs.openvinotoolkit.org/latest/omz_models_group_intel.html) models trained to resolve a variety of different tasks. +| [Model Optimizer](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) |Optimize and transform models trained in supported frameworks to the IR format.
Supported frameworks include TensorFlow\*, Caffe\*, Kaldi\*, MXNet\*, and ONNX\* format. +| [Benchmark Tool](https://docs.openvinotoolkit.org/latest/openvino_inference_engine_tools_benchmark_tool_README.html)| Estimate deep learning model inference performance on supported devices. +| [Accuracy Checker](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker.html)| Evaluate the accuracy of a model by collecting one or several metric values. +| [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.html)| Optimize pretrained models with lowering the precision of a model from floating-point precision(FP32 or FP16) to integer precision (INT8), without the need to retrain or fine-tune models. | + + +@sphinxdirective + +.. link-button:: workbench_docs_Workbench_DG_Start_DL_Workbench_in_DevCloud + :type: ref + :text: Run DL Workbench in Intel® DevCloud + :classes: btn-outline-primary + +@endsphinxdirective + +## Contact Us + +* [DL Workbench GitHub Repository](https://github.com/openvinotoolkit/workbench) + +* [DL Workbench on Intel Community Forum](https://community.intel.com/t5/Intel-Distribution-of-OpenVINO/bd-p/distribution-openvino-toolkit) + +* [DL Workbench Gitter Chat](https://gitter.im/dl-workbench/general?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&content=body) \ No newline at end of file diff --git a/docs/Documentation/model_introduction.md b/docs/Documentation/model_introduction.md new file mode 100644 index 00000000000..1e819f2e155 --- /dev/null +++ b/docs/Documentation/model_introduction.md @@ -0,0 +1,12 @@ +# Introduction to Model Processing {#openvino_docs_model_processing_introduction} + +Every deep learning workflow begins with obtaining a model. You can choose to prepare a custom one, use a ready-made solution and adjust it to your needs, or even download and run a pre-trained network from an online database, such as OpenVINO's [Open Model Zoo](../model_zoo.md). + +This section describes how to obtain and prepare your model for work with OpenVINO to get the best inference results: +* [Browse a database of models for use in your projects](../model_zoo.md). +* [Convert different model formats to the OpenVINO IR format](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). +* [Automate model-related tasks with Model Downloader and additional OMZ Tools](https://docs.openvino.ai/latest/omz_tools_downloader.html). + + + + diff --git a/docs/Documentation/openvino_ecosystem.md b/docs/Documentation/openvino_ecosystem.md new file mode 100644 index 00000000000..dcafd7bf007 --- /dev/null +++ b/docs/Documentation/openvino_ecosystem.md @@ -0,0 +1,76 @@ +# OpenVINO™ Ecosystem Overview {#openvino_ecosystem} + + +OpenVINO™ is not just one tool. It is an expansive ecosystem of utilities, providing a comprehensive workflow for deep learning solution development. Learn more about each of them to reach the full potential of OpenVINO™ Toolkit. + +### OpenVINO™ Model Server (OVMS) +OpenVINO Model Server is a scalable, high-performance solution for serving deep learning models optimized for Intel® architectures. The server uses Inference Engine libraries as a backend and exposes gRPC and HTTP/REST interfaces for inference that are fully compatible with TensorFlow Serving. + +More resources: +* [OpenVINO documentation](https://docs.openvino.ai/latest/openvino_docs_ovms.html) +* [Docker Hub](https://hub.docker.com/r/openvino/model_server) +* [GitHub](https://github.com/openvinotoolkit/model_server) +* [Red Hat Ecosystem Catalog](https://catalog.redhat.com/software/container-stacks/detail/60649e41ccfb383fe395a167) + +### Neural Network Compression Framework (NNCF) +A suite of advanced algorithms for Neural Network inference optimization with minimal accuracy drop. NNCF applies quantization, filter pruning, binarization and sparsity algorithms to PyTorch and TensorFlow models during training. + +More resources: +* [Documentation](@ref docs_nncf_introduction) +* [GitHub](https://github.com/openvinotoolkit/nncf) +* [PyPI](https://pypi.org/project/nncf/) + +### OpenVINO™ Security Add-on +A solution for Model Developers and Independent Software Vendors to use secure packaging and secure model execution. + +More resources: +* [documentation](https://docs.openvino.ai/latest/ovsa_get_started.html) +* [GitHub]https://github.com/openvinotoolkit/security_addon) + + +### OpenVINO™ integration with TensorFlow (OVTF) +A solution empowering TensorFlow developers with OpenVINO's optimization capabilities. With just two lines of code in your application, you can offload inference to OpenVINO, while keeping the TensorFlow API. + +More resources: +* [documentation](https://github.com/openvinotoolkit/openvino_tensorflow) +* [PyPI](https://pypi.org/project/openvino-tensorflow/) +* [GitHub](https://github.com/openvinotoolkit/openvino_tensorflow) + +### DL Streamer +A streaming media analytics framework, based on the GStreamer multimedia framework, for creating complex media analytics pipelines. + +More resources: +* [documentation on GitHub](https://openvinotoolkit.github.io/dlstreamer_gst/) +* [installation Guide on GitHub](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Install-Guide) + +### DL Workbench +A web-based tool for deploying deep learning models. Built on the core of OpenVINO and equipped with a graphics user interface, DL Workbench is a great way to explore the possibilities of the OpenVINO workflow, import, analyse, optimize, and build your pre-trained models. You can do all that by visiting [Intel® DevCloud for the Edge](https://software.intel.com/content/www/us/en/develop/tools/devcloud.html) and launching DL Workbench on-line. + +More resources: +* [documentation](dl_workbench_overview.md) +* [Docker Hub](https://hub.docker.com/r/openvino/workbench) +* [PyPI](https://pypi.org/project/openvino-workbench/) + +### OpenVINO™ Training Extensions (OTE) +A convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. + +More resources: +* [GitHub](https://github.com/openvinotoolkit/training_extensions) + +### Computer Vision Annotation Tool (CVAT) +An online, interactive video and image annotation tool for computer vision purposes. + +More resources: +* [documentation on GitHub](https://openvinotoolkit.github.io/cvat/docs/) +* [web application](https://cvat.org/) +* [Docker Hub](https://hub.docker.com/r/openvino/cvat_server) +* [GitHub](https://github.com/openvinotoolkit/cvat) + +### Dataset Management Framework (Datumaro) +A framework and CLI tool to build, transform, and analyze datasets. + +More resources: +* [documentation on GitHub](https://openvinotoolkit.github.io/datumaro/docs/) +* [PyPI](https://pypi.org/project/datumaro/) +* [GitHub](https://github.com/openvinotoolkit/datumaro) + diff --git a/docs/Documentation/openvino_ecosystem_ovtf.md b/docs/Documentation/openvino_ecosystem_ovtf.md new file mode 100644 index 00000000000..da03d51bd05 --- /dev/null +++ b/docs/Documentation/openvino_ecosystem_ovtf.md @@ -0,0 +1,44 @@ +# OpenVINO™ integration with TensorFlow {#ovtf_integration} + +**OpenVINO™ integration with TensorFlow** is a solution for TensorFlow developers who want to get started with OpenVINO™ in their inferencing applications. By adding just two lines of code you can now take advantage of OpenVINO™ toolkit optimizations with TensorFlow inference applications across a range of Intel® computation devices. + +This is all you need: +```bash +import openvino_tensorflow +openvino_tensorflow.set_backend('') +``` + +**OpenVINO™ integration with TensorFlow** accelerates inference across many AI models on a variety of Intel® technologies, 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 developers to adopt native OpenVINO™ solutions. +To find out more about the product itself, as well as learn how to use it in your project, check its dedicated [GitHub repository](https://github.com/openvinotoolkit/openvino_tensorflow/tree/master/docs). + + +To see what you can do with **OpenVINO™ integration with TensorFlow**, explore the demos located in the [examples folder](https://github.com/openvinotoolkit/openvino_tensorflow/tree/master/examples) in our GitHub repository. + +Sample tutorials are also hosted on [Intel® DevCloud](https://www.intel.com/content/www/us/en/developer/tools/devcloud/edge/build/ovtfoverview.html). 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](https://github.com/openvinotoolkit/openvino_tensorflow/blob/master/LICENSE). +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](https://github.com/openvinotoolkit/openvino_tensorflow/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](https://github.com/openvinotoolkit/openvino_tensorflow/issues). +* Submit a [pull request](https://github.com/openvinotoolkit/openvino_tensorflow/pulls). + +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. + +--- +\* Other names and brands may be claimed as the property of others. \ No newline at end of file diff --git a/docs/_static/css/homepage_style.css b/docs/_static/css/homepage_style.css index b1b02dd0424..207e687cd81 100644 --- a/docs/_static/css/homepage_style.css +++ b/docs/_static/css/homepage_style.css @@ -1,5 +1,5 @@ /* overrides */ -.switcher-set {display: none;} +.switcher-set, .prev-next-bottom {display: none;} #openvino-documentation > h1 { display: none; @@ -9,6 +9,7 @@ img { cursor: default; } + /* === SEPARATOR HEADERS ================================================= */ .HP_separator-header { font-weight: bold; @@ -40,7 +41,7 @@ img { } /* === HEADER BANNER ===================================================== */ -#HP_head_banner { +/*#HP_head_banner { width: 100%; height: auto; background: left / cover no-repeat url("../images/head_banner.jpg"); @@ -107,11 +108,61 @@ float: right; #HP_head_banner ~ div ~ p { text-align: left; } +*/ +/* === OV workflow chart ===================================================== */ + +#HP_flow-container { + margin: 0 auto; + width: 650px; +} + +#HP_flow-a, #HP_flow-b, #HP_flow-c, .HP_flow-arrow { + float: left; + margin: 0; +} + +#HP_flow-a div { + background-image: url("../images/OV_flow_model.svg"); +} +#HP_flow-b div { + background: url("../images/OV_flow_optimization.svg"); +} +#HP_flow-c div { + background: url("../images/OV_flow_deployment.svg"); +} + + +#HP_flow-a div, #HP_flow-b div, #HP_flow-c div { + width: 170px; + height: 330px; + background-repeat: no-repeat; + background-size: 340px auto; + background-position: 0 0; +} + + + +#HP_flow-a:hover div, #HP_flow-b:hover div, #HP_flow-c:hover div { + background-position: -170px 0; +} + +.HP_flow-arrow { + width: 60px; + height: 330px; + background: no-repeat url("../images/OV_flow_arrow.svg"); + background-position: center 55px; + background-size: 28px; +} + + + + .HP_img_chart { display: block; margin: 40px auto 60px auto; width:80%; + cursor: default !important; } img.HP_img_chart ~ p { margin-top: -40px; @@ -119,6 +170,9 @@ img.HP_img_chart ~ p { } + + + /* === INFORMATION BOXES ================================================= */ .HP_infoboxes { width: 100%; @@ -201,6 +255,14 @@ img.HP_img_chart ~ p { margin-left: 2%; margin-right: 0; } + + #HP_flow-container { + width: 580px; + } + .HP_flow-arrow { + width: 20px; + background-position: -10px 55px; + } } diff --git a/docs/_static/images/HP_ov_flow.svg b/docs/_static/images/HP_ov_flow.svg deleted file mode 100644 index 902a0de840d..00000000000 --- a/docs/_static/images/HP_ov_flow.svg +++ /dev/null @@ -1,62 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - Train your model - - - - - - Convert to the - OpenVINO format* - - - - - - Run Inference - - - - - - Deploy your application - - - - - - Tune and Optimize - - - OR - - - - - - Pick a - pre-trained model - - - - diff --git a/docs/_static/images/OV_flow_arrow.svg b/docs/_static/images/OV_flow_arrow.svg new file mode 100644 index 00000000000..b2b298601af --- /dev/null +++ b/docs/_static/images/OV_flow_arrow.svg @@ -0,0 +1,11 @@ + + + + + + + + + + + diff --git a/docs/_static/images/OV_flow_deployment.svg b/docs/_static/images/OV_flow_deployment.svg new file mode 100644 index 00000000000..4991b5a2bc2 --- /dev/null +++ b/docs/_static/images/OV_flow_deployment.svg @@ -0,0 +1,562 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/_static/images/OV_flow_model.svg b/docs/_static/images/OV_flow_model.svg new file mode 100644 index 00000000000..1f20c27f474 --- /dev/null +++ b/docs/_static/images/OV_flow_model.svg @@ -0,0 +1,556 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/_static/images/OV_flow_optimization.svg b/docs/_static/images/OV_flow_optimization.svg new file mode 100644 index 00000000000..972dc188a7d --- /dev/null +++ b/docs/_static/images/OV_flow_optimization.svg @@ -0,0 +1,520 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/documentation.md b/docs/documentation.md index 1895b0adfd3..77ee70f3fac 100644 --- a/docs/documentation.md +++ b/docs/documentation.md @@ -2,29 +2,27 @@ @sphinxdirective +.. toctree:: + :maxdepth: 1 + :caption: API 2.0 + :hidden: + + openvino_2_0_transition_guide + + .. toctree:: :maxdepth: 1 :caption: Converting and Preparing Models :hidden: + openvino_docs_model_processing_introduction openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide omz_tools_downloader .. toctree:: :maxdepth: 1 - :caption: Deploying Inference - :hidden: - - openvino_docs_OV_UG_OV_Runtime_User_Guide - openvino_2_0_transition_guide - openvino_deployment_guide - openvino_inference_engine_tools_compile_tool_README - - -.. toctree:: - :maxdepth: 1 - :caption: Tuning for Performance + :caption: Optimization and Performance :hidden: openvino_docs_optimization_guide_dldt_optimization_guide @@ -37,15 +35,27 @@ .. toctree:: :maxdepth: 1 - :caption: Graphical Web Interface for OpenVINO™ toolkit + :caption: Deploying Inference :hidden: + openvino_docs_deployment_guide_introduction + openvino_docs_OV_UG_OV_Runtime_User_Guide + openvino_deployment_guide + openvino_inference_engine_tools_compile_tool_README + + +.. toctree:: + :maxdepth: 1 + :hidden: + :caption: THE Ecosystem + + openvino_ecosystem + ovms_what_is_openvino_model_server + ovsa_get_started + ovtf_integration + ote_documentation workbench_docs_Workbench_DG_Introduction - workbench_docs_Workbench_DG_Install - workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets - Tutorials - User Guide - workbench_docs_Workbench_DG_Troubleshooting + .. toctree:: :maxdepth: 1 @@ -57,14 +67,6 @@ OpenCV* Developer Guide OpenCL™ Developer Guide -.. toctree:: - :maxdepth: 1 - :caption: Add-Ons - :hidden: - - ovms_what_is_openvino_model_server - ote_documentation - ovsa_get_started .. toctree:: :maxdepth: 1 @@ -84,22 +86,22 @@ openvino_docs_security_guide_workbench openvino_docs_OV_UG_protecting_model_guide ovsa_get_started - @endsphinxdirective -This section provides reference documents that guide you through developing your own deep learning applications with the OpenVINO™ toolkit. These documents will most helpful if you have first gone through the [Get Started](get_started.md) guide. +This section provides reference documents that guide you through the OpenVINO toolkit workflow, from obtaining models, optimizing them, to deploying them in your own deep learning applications. ## Converting and Preparing Models -With the [Model Downloader](@ref omz_tools_downloader) and [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) guides, you will learn to download pre-trained models and convert them for use with the OpenVINO™ toolkit. You can provide your own model or choose a public or Intel model from a broad selection provided in the [Open Model Zoo](model_zoo.md). +With [Model Downloader](@ref omz_tools_downloader) and [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) guides, you will learn to download pre-trained models and convert them for use with OpenVINO™. You can use your own models or choose some from a broad selection provided in the [Open Model Zoo](./model_zoo.md). + +## Optimization and Performance +In this section you will find resources on [how to test inference performance](MO_DG/prepare_model/Getting_performance_numbers.md) and [how to increase it](optimization_guide/dldt_optimization_guide.md). It can be achieved by [optimizing the model](optimization_guide/model_optimization_guide.md) or [optimizing inference at runtime](optimization_guide/dldt_deployment_optimization_guide.md). ## Deploying Inference -The [OpenVINO™ Runtime User Guide](./OV_Runtime_UG/openvino_intro.md) explains the process of creating your own application that runs inference with the OpenVINO™ toolkit. The [API Reference](./api_references.html) defines the OpenVINO Runtime API for Python, C++, and C. The OpenVINO Runtime API is what you'll use to create an OpenVINO™ inference application, use enhanced operations sets and other features. After writing your application, you can use the [Deployment with OpenVINO](./OV_Runtime_UG/deployment/deployment_intro.md) for deploying to target devices. +This section explains the process of creating your own inference application using [OpenVINO™ Runtime](./OV_Runtime_UG/openvino_intro.md) and documents the [OpenVINO Runtime API](./api_references.html) for both Python and C++. +It also provides a [guide on deploying applications with OpenVINO](./OV_Runtime_UG/deployment/deployment_intro.md) and directs you to other sources on this topic. -## Tuning for Performance -The toolkit provides a [Performance Optimization Guide](optimization_guide/dldt_optimization_guide.md) and utilities for squeezing the best performance out of your application, including [Accuracy Checker](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool](@ref pot_introduction), and other tools for measuring accuracy, benchmarking performance, and tuning your application. - -## Graphical Web Interface for OpenVINO™ Toolkit -You can choose to use the [OpenVINO™ Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), a web-based tool that guides you through the process of converting, measuring, optimizing, and deploying models. This tool also serves as a low-effort introduction to the toolkit and provides a variety of useful interactive charts for understanding performance. +## OpenVINO Ecosystem +Apart from the core components, OpenVINO offers tools, plugins, and expansions revolving around it, even if not constituting necessary parts of its workflow. This section will give you an overview of [what makes up OpenVINO Toolkit](./Documentation/openvino_ecosystem.md). ## Media Processing and Computer Vision Libraries diff --git a/docs/img/openvino_dl_wb.png b/docs/img/openvino_dl_wb.png new file mode 100644 index 00000000000..bd86f059507 --- /dev/null +++ b/docs/img/openvino_dl_wb.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:90e5ff4285c9d1069647097157eccf7d8a3f545f4ba8b93930b55d8b62c17a1a +size 100677 diff --git a/docs/img/openvino_dl_wb_diagram_overview.svg b/docs/img/openvino_dl_wb_diagram_overview.svg new file mode 100644 index 00000000000..b0bb1433013 --- /dev/null +++ b/docs/img/openvino_dl_wb_diagram_overview.svg @@ -0,0 +1,179 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Import Model + + + + + + Select an Open + Model Zoo model + + + + + + Select + inference + environment + + + + + + Create a not + annotated dataset + + + + + + Import an + annotated dataset + + + + + + Apply INT8 + calibration with + Default method + + + + + + + Apply INT8 + calibration with + AccuracyAware + method + + + + + + Analyze + throughput and + latency values + + + + + + + Vizualize + model output + + + + + + + Analyze + per-layer + performance + + + + + + + Measure + accuracy + + + + + + + Analyze + execution graph + + + + + + + Analyze + execution time + chart + + + + + + + Find an optimal + batch and stream + combination + + + + + + Create + a deployment + package + + + + diff --git a/docs/img/openvino_dl_wb_workflow.gif b/docs/img/openvino_dl_wb_workflow.gif new file mode 100644 index 00000000000..0bdc9ffb57e --- /dev/null +++ b/docs/img/openvino_dl_wb_workflow.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9d3757d6b894f06c559045b83c23d23d0f8bc772d50252e960d82bcbe34cf598 +size 2106121 diff --git a/docs/index.rst b/docs/index.rst index 58e17073924..6821cacd0bc 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -12,78 +12,92 @@ OpenVINO™ Documentation .. raw:: html
- +
- +

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. -

    +

    • Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks
    • Use models trained with popular frameworks like TensorFlow, PyTorch and more
    • Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud
    -

    - - OpenVINO allows to process models built with Caffe, Keras, mxnet, TensorFlow, ONNX, and PyTorch. They can be easily optimized and deployed on devices running Windows, Linux, or MacOS. +

    + + OpenVINO allows to process models built with Caffe, Keras, mxnet, TensorFlow, ONNX, and PyTorch. They can be easily optimized and deployed on devices running Windows, Linux, or MacOS.
    -

    Check the full range of supported hardware in the - Supported Devices page and see how it stacks up in our - Performance Benchmarks page.
    - Supports deployment on Windows, Linux, and macOS. -

    +

    Check the full range of supported hardware in the + Supported Devices page and see how it stacks up in our + Performance Benchmarks page.
    + Supports deployment on Windows, Linux, and macOS. +

    + + +
    -

    Train, Optimize, Deploy

    -
    -
    - -

    * The ONNX format is also supported, but conversion to OpenVINO is recommended for better performance.

    -
    - +

    OpenVINO Workflow

    +
+
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ +

Want to know more?

- +
- -

Get Started

+ +

Get Started

Learn how to download, install, and configure OpenVINO.

- -

Open Model Zoo

-

Browse through over 200 publicly available neural networks and pick the right one for your solution.

- - -

Model Optimizer

-

Learn how to convert your model and optimize it for use with OpenVINO.

- - -

Tutorials

-

Learn how to use OpenVINO based on our training material.

- - -

Samples

-

Try OpenVINO using ready-made applications explaining various use cases.

- - -

DL Workbench

-

Learn about the alternative, web-based version of OpenVINO. DL Workbench container installation Required.

- - -

OpenVINO™ Runtime

-

Learn about OpenVINO's inference mechanism which executes the IR, ONNX, Paddle models on target devices.

- - -

Tune & Optimize

-

Model-level (e.g. quantization) and Runtime (i.e. application) -level optimizations to make your inference as fast as possible.

- - -

Performance
Benchmarks

-

View performance benchmark results for various models on Intel platforms.

- + +

Open Model Zoo

+

Browse through over 200 publicly available neural networks and pick the right one for your solution.

+ + +

Model Optimizer

+

Learn how to convert your model and optimize it for use with OpenVINO.

+ + +

Tutorials

+

Learn how to use OpenVINO based on our training material.

+ + +

Samples

+

Try OpenVINO using ready-made applications explaining various use cases.

+ + +

DL Workbench

+

Learn about the alternative, web-based version of OpenVINO. DL Workbench container installation Required.

+ + +

OpenVINO™ Runtime

+

Learn about OpenVINO's inference mechanism which executes the IR, ONNX, Paddle models on target devices.

+ + +

Tune & Optimize

+

Model-level (e.g. quantization) and Runtime (i.e. application) -level optimizations to make your inference as fast as possible.

+ + +

Performance
Benchmarks

+

View performance benchmark results for various models on Intel platforms.

+