Book Image

Caffe2 Quick Start Guide

By : Ashwin Nanjappa
Book Image

Caffe2 Quick Start Guide

By: Ashwin Nanjappa

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)

Summary

In this chapter, we introduced the Caffe deep learning framework and examined the relationship between Caffe and Caffe2. We examined the Caffe and Caffe2 model file formats. Using AlexNet as an example network, we looked at how to convert a Caffe model to Caffe2 format. Finally, we looked at the difficulties in converting a Caffe2 model to Caffe.

Caffe is a DL framework that has reached its end of life and no new features are being added to it. In the next chapter, we will look at contemporary DL frameworks, such as TensorFlow and PyTorch, and see how we can exchange models to and from Caffe2 and these other frameworks.