Build Your Own Deep Learning Inference Engine
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Build Your Own Deep Learning Inference Engine

A Hands-On Guide to Implementing Operators, Graphs, and Optimization from Scratch

About the Book

KuiperInfer: Build Your Own Deep Learning Inference Framework from Scratch

KuiperInfer is an open source project that teaches you how to build a high performance deep learning inference framework in C++. Written by expert developer zjhellofss, this book guides you through implementing key components like computational graphs, common operators, and optimizations.

Follow along with the video tutorials on Bilibili to gain first-hand engineering experience. Understand techniques like im2col and kernel fusion to accelerate neural network inference. The well-structured C++ codebase features extensive unit testing.

This hands-on guide covers:

  • Essential deep learning framework architecture
  • Designing and implementing computational graphs
  • Supporting operators like convolution, pooling, and fully connected layers
  • Optimizing performance critical parts
  • Deploying models like ResNet and YOLOv5

Whether you want to level up your skills or ace technical interviews, building your own inference framework from the ground up will give you a huge boost. Get started with KuiperInfer!

About the Author

Shenshen Fu
Shenshen Fu

Zjhellofss is an experienced C++ engineer with a passion for deep learning and high performance computing. He contributes extensively to open source projects like KuiperInfer, focusing on topics like neural network inference optimization and engineering best practices.

A seasoned developer, he provides tutorials on implementing real world AI applications and delivering robust production-grade code. His teaching emphasizes practical engineering over theory.

When not coding, he enjoys sports and street dance. As an instructor, he aims to inspire engineers to build and create. Follow him on GitHub and YouTube for more machine learning content.

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