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* DOCS-structure_workflow workflow diagram files and formatting added overview articles on models and deployment added the ecosystem page and changed the header from addons * DOCS-structure_dlworkbench * DOCS-structure_ovtf
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OpenVINO™ Deep Learning Workbench Overview
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workbench_docs_Workbench_DG_Install workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets Tutorials <workbench_docs_Workbench_DG_Tutorials> User Guide <workbench_docs_Workbench_DG_User_Guide> workbench_docs_Workbench_DG_Troubleshooting
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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.
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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
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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 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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- DL Workbench Introduction. Duration: 1:31
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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:
Get a quick overview of the workflow in the DL Workbench User Interface:
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 | Get access to the collection of high-quality pre-trained deep learning public and Intel-trained models trained to resolve a variety of different tasks. |
| Model Optimizer | Optimize and transform models trained in supported frameworks to the IR format. Supported frameworks include TensorFlow*, Caffe*, Kaldi*, MXNet*, and ONNX* format. |
| Benchmark Tool | Estimate deep learning model inference performance on supported devices. |
| Accuracy Checker | Evaluate the accuracy of a model by collecting one or several metric values. |
| Post-Training Optimization Tool | 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. |
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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-outline-primary
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