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)

NVIDIA TensorRT

TensorRT is the most popular inference engine for deploying trained models on NVIDIA GPUs for inference. Not surprisingly, this library and its set of tools are developed by NVIDIA and it is available free for download and use. A new version of TensorRT typically accompanies the release of every new NVIDIA GPU architecture, adding optimizations for the new GPU architecture and also support for new types of layers, operators, and DL frameworks.

Installing TensorRT

TensorRT installers can be downloaded from the web at https://developer.nvidia.com/tensorrt. Installation packages are available for x86-64 (Intel or AMD 64-bit CPU) computers, PowerPC computers, embedded hardware such as NVIDIA TX1/TX2, and NVIDIA...