tis

A simple demo of using trition-inference-serving

Platform

• ubuntu 18.04
• cmake-3.22.0
• 8 Tesla T4 gpu

Serving Model

1. prepare model repository

We need to export our model to onnx and copy it to model repository:

$ cd BiSeNet
$ python tools/export_onnx.py --config configs/bisenetv1_city.py --weight-path /path/to/your/model.pth --outpath ./model.onnx 
$ cp -riv ./model.onnx tis/models/bisenetv1/1

$ python tools/export_onnx.py --config configs/bisenetv2_city.py --weight-path /path/to/your/model.pth --outpath ./model.onnx 
$ cp -riv ./model.onnx tis/models/bisenetv2/1

2. start service

We start serving with docker:

$ docker pull nvcr.io/nvidia/tritonserver:21.10-py3
$ docker run --gpus all --rm -p8000:8000 -p8001:8001 -p8002:8002 -v /path/to/BiSeNet/tis/models:/models nvcr.io/nvidia/tritonserver:21.10-py3 tritonserver --model-repository=/models

In general, the service would start now. You can check whether service has started by:

$ curl -v localhost:8000/v2/health/ready

By default, we use gpu 0 and gpu 1, you can change configurations in the config.pbtxt file.

Client

We call the model service with both python and c++ method.

1. python method

Firstly, we need to install dependency package:

$ python -m pip install tritonclient[all]==2.15.0

Then we can run the script:

$ cd BiSeNet/tis
$ python client.py

This would generate a result file named res.jpg in BiSeNet/tis directory.

2. c++ method

We need to compile c++ client library from source:

$ apt install rapidjson-dev
$ mkdir -p /data/ $$ cd /data/
$ git clone https://github.com/triton-inference-server/client.git
$ cd client && git reset --hard da04158bc094925a56b
$ mkdir -p build && cd build
$ cmake -DCMAKE_INSTALL_PREFIX=/opt/triton_client -DTRITON_ENABLE_CC_HTTP=ON -DTRITON_ENABLE_CC_GRPC=ON -DTRITON_ENABLE_PERF_ANALYZER=OFF -DTRITON_ENABLE_PYTHON_HTTP=OFF -DTRITON_ENABLE_PYTHON_GRPC=OFF -DTRITON_ENABLE_JAVA_HTTP=OFF -DTRITON_ENABLE_GPU=ON -DTRITON_ENABLE_EXAMPLES=OFF -DTRITON_ENABLE_TESTS=ON ..
$ make cc-clients

The above commands are exactly what I used to compile the library. I learned these commands from the official document.

Also, We need to install cmake with version 3.22.

Optionally, I compiled opencv from source and install it to /opt/opencv. You can first skip this and see whether you meet problems. If you have problems about opencv in the following steps, you can compile opencv as what I do.

After installing the dependencies, we can compile our c++ client:

$ cd BiSeNet/tis/cpp_client
$ mkdir -p build && cd build
$ cmake .. && make

Finally, we run the client and see a result file named res.jpg generated:

    ./client

In the end

This is a simple demo with only basic function. There are many other features that is useful, such as shared memory and model pipeline. If you have interest on this, you can learn more in the official document.