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)

Open Neural Network Exchange

Open Neural Network Exchange (ONNX), typically pronounced as on-niks, is a format to represent a computation graph, with support for a wide variety of operators and data types. This format is general enough to support both neural networks and traditional ML models. Started by Facebook and Microsoft, this format has quickly gained a reputation as a popular format for the export and import of deep neural networks among most DL frameworks.

Installing ONNX

The ONNX source code can be found online at: https://github.com/onnx/onnx This includes definitions of the format and scripts to operate on ONNX files. Libraries and tools to convert from and to specific DL framework formats are usually provided...