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Caffe2 Quick Start Guide

By : Ashwin Nanjappa

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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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Operators

In Caffe2, a neural network can be thought of as a directed graph, where the nodes are operators and the edges represent the flow of data between operators. Operators are the basic units of computation in a Caffe2 network. Every operator is defined with a certain number of inputs and a certain number of outputs. When the operator is executed, it reads its inputs, performs the computation it is associated with, and writes the results to its outputs.

To obtain the best possible performance, Caffe2 operators are typically implemented in C++ for execution on CPUs and implemented in CUDA for execution on GPUs. All operators in Caffe2 are derived from a common interface. You can see this common interface defined in the caffe2/proto/caffe2.proto file in the Caffe2 source code.

The following is the Caffe2 operator interface found in my caffe2.proto file: