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To benchmark model performance metrics related to latency and throughput on the Deep Learning Workbench, you have to upload a dataset.
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1 What is the OpenVINO™ toolkit DL Workbench
2 Introduction to Deep Learning Workbench and identifying the benefits
3 User Goals
4 Getting started with Deep Learning Workbench and identify the difference between running the Deep Learning Workbench on your local system and on the Intel® DevCloud for the Edge
5 How to Install the OpenVINO™ toolkit DL Workbench on Local System
6 Run the DL Workbench on Intel® DevCloud for the Edge
7 Explore key concepts and features of Deep Learning Workbench
8 Getting familiar with Deep Learning Workbench workflow
9 Getting Started with DL Workbench
10 Single and Group Inference in DL Workbench
11 Visualize the model and model output, and compare Inference Results
12 Further optimize and finetune the model for improved performance using Post-Training Optimization Tool (POT)
13 Accuracy Measurement in DL Workbench
14 Package your application with deployment manager
15 Lab - DL Workbench
16 Quiz Question
17 Quiz Question
18 Quiz Question
