diff --git a/docs/Documentation/dl_workbench_overview.md b/docs/Documentation/dl_workbench_overview.md index 24dfd805b62..41acb4a1364 100644 --- a/docs/Documentation/dl_workbench_overview.md +++ b/docs/Documentation/dl_workbench_overview.md @@ -13,99 +13,3 @@ @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