# autotvm_tutorial

**Repository Path**: loki-2019/autotvm_tutorial

## Basic Information

- **Project Name**: autotvm_tutorial
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-02
- **Last Updated**: 2025-06-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README



# autotvm_tutorial
autoTVM神经网络推理代码优化搜索演示，基于tvm编译开源模型centerface，并使用autoTVM搜索最优推理代码，　最终部署编译为c++代码，演示平台是cuda，可以是其他平台，例如树莓派，安卓手机，苹果手机

+ 知乎介绍文章位置：　https://zhuanlan.zhihu.com/p/366913595

> Thi is a demonstration of how to use autoTVM to search and optimize a neural network inference code. the main process of this program is , firstly use tvm to compile opensource model centerface , then use autotvm to auto search the best inference code for the compiled model, finaly the model to compile and deploy to c++ inference code , the demonstration platform is cuda framework , alternatively other platform is acceptable , ie rasspery , android and apple



# NOTE:
+ add variables "PATH=$PATH:/usr/local/cuda-11.1/bin" in order to use nvcc

# HOW TO

1. python tuning_centerface.py
2. use function "case_eval_from_autotvmlog()" in tuning_centerface.py to generate the inference dynamic library which is searched by the autoTVM
3. python inference_relay.py to verify the result
4. convert to the c++ api , see cpp_deploy project

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