Placeholder Image

Subtitles section Play video

  • Welcome back

  • I'd like to show you the full flow of inferencing with OpenVINO

  • Thru the flow you'll be able to see the various utilities that are included in the OpenVINO

  • package. Let's begin

  • In a very high level

  • We want to start with a DL based model that is already trained.

  • We want to prepare it for inference

  • We want to implement the inference in our application

  • We want the inference to run on multiple devices CPU, GPU, VPU or FPGA

  • And we might even want to implement custom layers per each of these devices..

  • And now in a little more details.

  • the assumption is that you already have a trained model.

  • The model is usually described by few files, .pb file for TensorFlow

  • .params for Mxnet and so on. Here I'm showing a Caffe model file,

  • Connectivity is described by the prototex.. weights and biases are in the .caffemodel

  • file In Video #6, we'll show you how to easily

  • use our model-downloader to get a public model.

  • Now we have to prepare this model for inference, we need to optimize it, change the data format

  • if needed etc. All of these tasks are performed by the Model-Optimizer

  • Check out Video 8 for an explanation on the Model Optimizer concept

  • In Video 9, we'll demonstrate the basic functions, video 10 more advance ones and so on

  • Now that we have the model ready for inference, we are using the "Inference Engine" API

  • in our application to actually do the inference Check out video 15 for a short explanation

  • of the concept, In Video 16 we'll show the API using a sample

  • code and so on..

  • On Video 19 we'll show you how easy it is to run inference on difference devices with

  • the Inference engine, You only have to code once, and you can run

  • the same model on a CPU or a GPU or many other devices which is really cool

  • And if you want to code your custom layers for CPU or GPU,

  • We'll show you how to do it on Videos 25 and 26..

  • So this is the full flow, it is simple and we will go over each stage in details.

  • In coming videos..

  • in the next video let's just try to run a demo that demonstrate the full flow..

  • Subscribe to out channel and we'll send you a new video each time we have a new feature

  • of OpenVINO..

  • Thank you.

Welcome back

Subtitles and vocabulary

Operation of videos Adjust the video here to display the subtitles

B1 inference model video number flow cpu file

(03) OpenVINO toolkit - Full Inference flow

  • 71 3
    alex posted on 2019/04/27
Video vocabulary