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@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)
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.. 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
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.. list-table::
* - .. raw:: html
<iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen height="315" width="560"
src="https://www.youtube.com/embed/on8xSSTKCt8">
</iframe>
* - **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. <br>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)