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.
Table of Contents (9 chapters)

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...