Caffe2 Quick Start Guide

By : Ashwin Nanjappa

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Buy this Book

Caffe2 Quick Start Guide

5 (2)

By: Ashwin Nanjappa

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction and Installation

Introduction to deep learning

Introduction to Caffe2

Building and installing Caffe2

Testing the Caffe2 Python API

Testing the Caffe2 C++ API

Summary

Composing Networks

Operators

Difference between layers and operators

Building a computation graph

Building a multilayer perceptron neural network

Summary

Training Networks

Introduction to training

LeNet network

Training data

Building LeNet

Training layers

Training and monitoring

Summary

Working with Caffe

The relationship between Caffe and Caffe2

Introduction to AlexNet

Building and installing Caffe

Caffe model file formats

Caffe2 model file formats

Converting a Caffe model to Caffe2

Converting a Caffe2 model to Caffe

Summary

Working with Other Frameworks

Open Neural Network Exchange

ONNX in Caffe2

Exporting the Caffe2 model to ONNX

Using the ONNX model in Caffe2

Visualizing the ONNX model

Summary

Deploying Models to Accelerators for Inference

Inference engines

NVIDIA TensorRT

Intel OpenVINO

Summary

Caffe2 at the Edge and in the cloud

Caffe2 at the edge on Raspberry Pi

Caffe2 in the cloud using containers

Caffe2 model visualization

Summary

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Building a computation graph

In this section, we will learn how to build a network in Caffe2 using model_helper. (model_helper was introduced earlier in this chapter.) To maintain the simplicity of this example, we use mathematical operators that require no trained parameters. So, our network is a computation graph rather than a neural network because it has no trained parameters that were learned from training data. The network we will build is illustrated by the graph shown in Figure 2.5:

Figure 2.5: Our simple computation graph with three operators

As you can see, we provide two inputs to the network: a matrix, A, and a vector, B. A MatMul operator is applied to A and B and its result is fed to a Sigmoid function, designated by σ in Figure 2.5. The result of the Sigmoid function is fed to a SoftMax function. (We will learn a bit more about the Sigmoid and SoftMax operators...