There is a lot of interest in using deep learning at the edge. This is the application of deep learning to compute solutions on or near the devices that capture data using sensors and cameras. An alternative solution to deep learning at the edge is to capture edge data and send it to in the cloud for processing. But, deep learning at the edge has the advantage of lower latency and higher security. Devices at the edge are typically cheap, have a small form factor and use less power, and their processors or accelerators have less compute capability. One of the key advantages of Caffe2 is that it has been designed and developed from the beginning to scale: from multi-GPU, multi-CPU servers, down to tiny edge devices. In this section, we will use the Raspberry Pi as an example of an edge device and learn how to use Caffe2 on it.
-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
Caffe2 Quick Start Guide
By :
Caffe2 Quick Start Guide
By:
Overview of this book
Caffe2 is a popular deep learning library used for fast and scalable training, and inference of deep learning models on different platforms. This book introduces you to the Caffe2 framework and demonstrates how you can leverage its power to build, train, and deploy efficient neural network models at scale.
The Caffe 2 Quick Start Guide will help you in installing Caffe2, composing networks using its operators, training models, and deploying models to different architectures. The book will also guide you on how to import models from Caffe and other frameworks using the ONNX interchange format. You will then cover deep learning accelerators such as CPU and GPU and learn how to deploy Caffe2 models for inference on accelerators using inference engines. Finally, you'll understand how to deploy Caffe2 to a diverse set of hardware, using containers on the cloud and resource-constrained hardware such as Raspberry Pi.
By the end of this book, you will not only be able to compose and train popular neural network models with Caffe2, but also deploy them on accelerators, to the cloud and on resource-constrained platforms such as mobile and embedded hardware.
Table of Contents (9 chapters)
Preface
Free Chapter
Introduction and Installation
Composing Networks
Training Networks
Working with Caffe
Working with Other Frameworks
Deploying Models to Accelerators for Inference
Caffe2 at the Edge and in the cloud
Other Books You May Enjoy
Customer Reviews
