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The Intel® DevCloud for the Edge allows you to actively prototype and experiment with AI workloads for computer vision on Intel hardware. This repository contains a series of Jupyter* notebook tutorials and examples hosted on Intel® DevCloud for the Edge. It comes preloaded with everything you needed to quickly get started. This includes trained models, sample data and executable code from the Intel® Distribution of OpenVINO™ Toolkit as well as other tools for deep learning. These notebooks are designed to help you quickly learn how to implement deep learning applications to enable compelling, high-performance solutions.
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What you should know before running the examples:
Your home directory on the development server is network-shared between the development server and edge compute servers.
Figure 1 illustrates the process flow involved in job submission.
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intel /
Get started using Intel® FPGA tools on the Devcloud with tutorials, workshops, advanced courses, and sample projects built specifically for students, researchers, and developers. Visit our official Intel® FPGA Devcloud website:
17/100 healthLeverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications, and run pre-trained deep learning models for computer vision on-premise. You will identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU), and utilize the Intel® DevCloud for the Edge to test model performance on the various hardware types. Finally, you will use software tools to optimize deep learning models to improve performance of Edge AI systems. - Source
26/100 healthRahul24-06 /
Build custom queuing systems for the retail, manufacturing and transportation sectors and use the Intel® DevCloud for the Edge to test your solutions performance.
46/100 healthARBUCHELI /
Requesting a CPU with a VPU (Intel® NCS2) on Intel's DevCloud, loading a model on the Intel® NCS2, and running inference on an image using both a CPU and IGPU.
27/100 healthARBUCHELI /
Requesting an edge node with a CPU and Intel® Arria 10 FPGA on Intel's DevCloud, loading a model on the Intel® Arria 10 FPGA, and running inference on an image.
27/100 healthARBUCHELI /
Demonstration showing you how to request an edge node with an Intel i5 CPU and load a model on the Intel® Arria 10 FPGA using Udacity's workspace integration with Intel's DevCloud.
27/100 health