# zjhellofss/KuiperInfer

校招、秋招、春招、实习好项目！带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step

Repository: https://github.com/zjhellofss/KuiperInfer
Canonical: https://ross.abutalabs.com/products/kuiperinfer
Language: C++
License: MIT
License Family: permissive
Topics: inference, inference-engine, deep-learning, deep-neural-networks, convolution, relu, sigmoid, graph-algorithms, maxpooling, caffe, pnnx, pytorch, ncnn, diy, resnet, yolo, yolov5
Last push: 2025-06-22T07:48:34+00:00

## Health v2 (maintenance only)
Score: 46/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 28, release rhythm 40, longevity 98
- inputs: {"age_days": 1381, "days_push": 437, "days_rel": 714, "gap_med": 12, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3496, forks 372 (observed 2026-08-28T04:08:07.341615+00:00)

## What it is
KuiperInfer is an open-source course that guides learners through building a high-performance deep learning inference engine from scratch in modern C++, supporting models like ResNet, YOLOv5, UNet, and Llama-family LLMs. It includes a companion paid course on building an LLM inference framework with hand-written CUDA operators, CUDA acceleration, and Int8 quantization.

## Use cases
- learn how deep learning inference engines work internally
- implement convolution and pooling operators from scratch in C++
- build a computational graph executor for neural networks
- run inference for resnet, yolov5, and unet models with a custom framework
- write CUDA kernels for llama and qwen large language model inference
- prepare for backend/ML systems job interviews with a substantial C++ project
- learn modern C++ project structure with CMake, unit tests, and benchmarks

## When to choose
- you want to deeply understand inference engine internals rather than just use one
- you need a portfolio project for campus recruiting or interviews in C++/ML systems
- you want guided, step-by-step video lessons paired with a real codebase
- you want to learn CUDA operator development for LLM inference

## When to avoid
- you need a production-ready inference engine for deployment (use ONNX Runtime, TensorRT, or llama.cpp instead)
- you want a framework to train models rather than run inference
- you prefer not to follow a course format and just need drop-in inference support
- you need broad model format support beyond PNNX/PyTorch-exported models

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-inference, machine-learning, deep-learning, gpu-computing, benchmarking, testing
- domain: deep-learning, large-language-models, machine-learning, computer-vision, education, developer-tools
- platform: cpp, windows, cross-platform
- tags: inference-engine, course, tutorial, cuda-kernels, llama, yolov5, unet, resnet, int8-quantization, computational-graph, modern-cpp, cmake, job-interview-prep, linux, macos, gpu

## Member repositories
- zjhellofss/KuiperInfer (main) score 46

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:07.341615+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:35:53.780278+00:00, confidence not recorded.
  - readme: https://github.com/zjhellofss/KuiperInfer (fetched 2026-08-28T04:08:07.341615+00:00, sha 811ec71a5954)
- Data as of 2026-08-30T08:39:29.467469+00:00.