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.

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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
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ONNX in Caffe2

Caffe2 has built-in support for ONNX. This includes support for exporting Caffe2 models to ONNX format and importing ONNX models directly for inference in Caffe2. C++ source files related to Caffe2's support of ONNX can be found in the onnx directory in the Caffe2 source code. Python source files that provide the frontend and backend support for ONNX can be found in the python/onnx directory in the Caffe2 source code.

The onnx/onnx_exporter.h and onnx/onnx_exporter.cc contain the definitions necessary to export a Caffe2 model to ONNX format. Support for exporting from Caffe2 to ONNX includes details such as the mapping from Caffe2 to ONNX for operators, data types, and transformations of data.

For example, in onnx/onnx_exporter.cc we find the following mapping of some Caffe2 operators to ONNX operators:

const std::unordered_map<std::string, std::string&gt...
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