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)

Difference between layers and operators

Older deep learning frameworks, such as Caffe, did not have operators. Instead, their basic units of computation were called layers. These older frameworks chose the name layer inspired by the layers in neural networks.

However, contemporary frameworks, such as Caffe2, TensorFlow, and PyTorch, prefer to use the term operator for their basic units of computation. There is a subtle difference between operators and layers. A layer in older frameworks, such as Caffe, was composed of both the computation function of that layer and the trained parameters of that layer. In contrast to this, an operator in Caffe2 only holds the computation function. Both the trained parameters and the inputs are external to the operator and need to be fed to it explicitly.

...