2. Environment Setup

This chapter describes the development environment configuration. The code is compiled and run in docker.

2.1. Code Download

GitHub link: https://github.com/sophgo/tpu-mlir

After cloning this code, it needs to be compiled in docker. For specific steps, please refer to the following.

2.2. Docker Configuration

TPU-MLIR is developed in the Docker environment, and it can be compiled and run after Docker is configured.

Download the required image from DockerHub https://hub.docker.com/r/sophgo/tpuc_dev :

$ docker pull sophgo/tpuc_dev:v3.4

If the pulling fails, you can download the required image file from the official website development materials https://developer.sophgo.com/site/index/material/86/all.html, or use the following command to download and load the image:

1$ wget https://sophon-assets.sophon.cn/sophon-prod-s3/drive/25/04/15/16/tpuc_dev_v3.4.tar.gz
2$ docker load -i tpuc_dev_v3.4.tar.gz

If you are using docker for the first time, you can execute the following commands to install and configure it (only for the first time):

1$ sudo apt install docker.io
2$ sudo systemctl start docker
3$ sudo systemctl enable docker
4$ sudo groupadd docker
5$ sudo usermod -aG docker $USER
6$ newgrp docker

Make sure the installation package is in the current directory, and then create a container in the current directory as follows:

$ docker run --privileged --name myname -v $PWD:/workspace -it sophgo/tpuc_dev:v3.4
# "myname" is just an example, you can use any name you want
# use --privileged to get root permission, if you don't need root permission, please remove this parameter

Note that the path of the TPU-MLIR project in docker should be /workspace/tpu-mlir

2.3. ModelZoo (Optional)

TPU-MLIR comes with the yolov5s model. If you want to run other models, you need to download them from ModelZoo. The path is as follows:

https://github.com/sophgo/model-zoo

After downloading, put it in the same directory as tpu-mlir. The path in docker should be /workspace/model-zoo

2.4. Compilation

In the docker container, the code is compiled as follows:

$ cd tpu-mlir
$ source ./envsetup.sh
$ ./build.sh

Regression validation:

# This project contains the yolov5s.onnx model, which can be used directly for validation
$ pushd regression
$ python run_model.py yolov5s
$ popd

You can validate more networks with model-zoo, but the whole regression takes a long time:

# The running time is very long, so it is not necessary
$ pushd regression
$ ./run_all.sh
$ popd