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)

What this book covers

Chapter 1, Introduction and Installation, introduces Caffe2 and examines how to build and install it.

Chapter 2, Composing Networks, teaches you about Caffe2 operators and how to compose them to build a simple computation graph and a neural network to recognize handwritten digits.

Chapter 3, Training Networks, gets into how to use Caffe2 to compose a network for training and how to train a network to solve the MNIST problem.

Chapter 4, Working with Caffe, explores the relationship between Caffe and Caffe2 and how to work with models trained in Caffe.

Chapter 5, Working with Other Frameworks, looks at contemporary deep learning frameworks such as TensorFlow and PyTorch and how we can exchange models from and to Caffe2 and these other frameworks.

Chapter 6, Deploying Models to Accelerators for Inference, talks about inference engines and how they are an essential tool for the final deployment of a trained Caffe2 model on accelerators. We focus on two types of popular accelerators: NVIDIA GPUs and Intel CPUs. We look at how to install and use TensorRT for deploying our Caffe2 model on NVIDIA GPUs. We also look at the installation and use of OpenVINO for deploying our Caffe2 model on Intel CPUs and accelerators.

Chapter 7, Caffe2 at the Edge and in the cloud, covers two applications of Caffe2 to demonstrate its ability to scale. As an application of Caffe2 with edge devices, we look at how to build Caffe2 on Raspberry Pi single-board computers and how to run Caffe2 applications on them. As an application of Caffe2 with the cloud, we look at the use of Caffe2 in Docker containers.