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.

Related Content you might be interested in

Current Title:

Small book image
Caffe2 Quick Start Guide
Ashwin Nanjappa
May, 2019 | 136 pages
Small book image
Deep Learning with TensorFlow
Ahmed Menshawy | Md Rezaul Karim | Giancarlo Zaccone
Apr, 2017 | 320 pages
Small book image
Mastering Computer Vision with TensorFlow 2.x
Krishnendu Kar
May, 2020 | 430 pages
Small book image
Practical Convolutional Neural Networks
Mohit Sewak | Md Rezaul Karim | Pradeep Pujari
Feb, 2018 | 218 pages
Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction and Installation
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
2
Composing Networks
Composing Networks
Operators
Difference between layers and operators
Building a computation graph
Building a multilayer perceptron neural network
Summary
3
Training Networks
Training Networks
Introduction to training
LeNet network
Training data
Building LeNet
Training layers
Training and monitoring
Summary
4
Working with Caffe
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
5
Working with Other Frameworks
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
6
Deploying Models to Accelerators for Inference
Deploying Models to Accelerators for Inference
Inference engines
NVIDIA TensorRT
Intel OpenVINO
Summary
7
Caffe2 at the Edge and in the cloud
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
8
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Summary

In this chapter, we introduced the Caffe deep learning framework and examined the relationship between Caffe and Caffe2. We examined the Caffe and Caffe2 model file formats. Using AlexNet as an example network, we looked at how to convert a Caffe model to Caffe2 format. Finally, we looked at the difficulties in converting a Caffe2 model to Caffe.

Caffe is a DL framework that has reached its end of life and no new features are being added to it. In the next chapter, we will look at contemporary DL frameworks, such as TensorFlow and PyTorch, and see how we can exchange models to and from Caffe2 and these other frameworks.

Previous Section
End of Chapter 4
Next Chapter