PaddleSeg Python Simple serving is an example of serving deployment built by FastDeploy based on the Flask framework that can quickly verify the feasibility of online model deployment. It completes AI inference tasks based on http requests, and is suitable for simple scenarios without concurrent inference task. For high concurrency and high throughput scenarios, please refer to fastdeploy_serving

Environment

1. Prepare environment and install FastDeploy Python whl, refer to download_prebuilt_libraries

Server:

# Download demo code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving

# Download PP_LiteSeg model
wget  https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
tar -xvf PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz

# Launch server, change the configurations in server.py to select hardware, backend, etc.
# and use --host, --port to specify IP and port
fastdeploy simple_serving --app server:app

Client:

# Download demo code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving

# Download test image
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png

# Send request and get inference result (Please adapt the IP and port if necessary)
python client.py