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 learned about Caffe2 operators and how they differ from layers used in older deep learning frameworks. We built a simple computation graph by composing several operators. We then tackled the MNIST machine learning problem and built an MLP network using Brew helper functions. We loaded pretrained weights into this network and used it for inference on a batch of input images. We also introduced several common layers, such as matrix multiplication, fully connected, Sigmoid, SoftMax, and ReLU.

We learned about performing inference on our networks in this chapter. In the next chapter, we will learn about training and how to train a network to solve the MNIST problem.