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Caffe2 Quick Start Guide

Caffe2 Quick Start Guide

By : Ashwin Nanjappa
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Caffe2 Quick Start Guide

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.
Table of Contents (9 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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1
Introduction and Installation
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Introduction and Installation
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Introduction to deep learning
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Introduction to Caffe2
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Building and installing Caffe2
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Testing the Caffe2 Python API
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Testing the Caffe2 C++ API
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Summary
2
Composing Networks
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Composing Networks
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Operators
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Difference between layers and operators
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Building a computation graph
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Building a multilayer perceptron neural network
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Summary
3
Training Networks
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Training Networks
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Introduction to training
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LeNet network
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Training data
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Building LeNet
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Training layers
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Training and monitoring
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Summary
4
Working with Caffe
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Working with Caffe
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The relationship between Caffe and Caffe2
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Introduction to AlexNet
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Building and installing Caffe
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Caffe model file formats
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Caffe2 model file formats
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Converting a Caffe model to Caffe2
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Converting a Caffe2 model to Caffe
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Summary
5
Working with Other Frameworks
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Working with Other Frameworks
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Open Neural Network Exchange
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ONNX in Caffe2
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Exporting the Caffe2 model to ONNX
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Using the ONNX model in Caffe2
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Visualizing the ONNX model
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Summary
6
Deploying Models to Accelerators for Inference
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Deploying Models to Accelerators for Inference
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Inference engines
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NVIDIA TensorRT
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Intel OpenVINO
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Summary
7
Caffe2 at the Edge and in the cloud
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Caffe2 at the Edge and in the cloud
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Caffe2 at the edge on Raspberry Pi
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Caffe2 in the cloud using containers
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Caffe2 model visualization
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Summary
8
Other Books You May Enjoy
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Other Books You May Enjoy
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Visualizing the ONNX model

When working with ONNX models, it can be useful to have a tool that can help in visualizing the network structure. ONNX ships with such a script called net_drawer.py. You can find this tool in the onnx/onnx/tools directory in the ONNX source repository. If you installed ONNX from its Python package, then you can find this script at /usr/local/lib/python2.7/dist-packages/onnx/tools/net_drawer.py.

This script can be applied to convert an ONNX file to a directed acyclic graph representation of the network in the GraphViz DOT format. For example, consider the ONNX file alexnet.onnx that we obtained in the earlier section on converting from the Caffe2 model to the ONNX model.

We can convert this AlexNet ONNX file to a DOT file using the following command:

$ python /usr/local/lib/python2.7/dist-packages/onnx/tools/net_drawer.py --input alexnet.onnx --output...

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