OpenVINO™ Deep Learning Workbench Overview¶
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.
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.
DL Workbench Introduction. Duration: 1:31
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.
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:
Optimize and transform models trained in supported frameworks to the IR format. Supported frameworks include TensorFlow*, Caffe*, Kaldi*, MXNet*, and ONNX* format.
Estimate deep learning model inference performance on supported devices.
Evaluate the accuracy of a model by collecting one or several metric values.
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.