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We consider deploying deep learning inference service online to be a user-facing application in the future. The goal of this project: When you have trained a deep neural net with Paddle, you are also capable to deploy the model online easily. A demo of Paddle Serving is as follows:


We highly recommend you to run Paddle Serving in Docker, please visit Run in Docker. See the document for more docker images.

# Run CPU Docker
docker pull hub.baidubce.com/paddlepaddle/serving:latest
docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:latest
docker exec -it test bash
# Run GPU Docker
nvidia-docker pull hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
nvidia-docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
nvidia-docker exec -it test bash
pip install paddle-serving-client==0.3.2 
pip install paddle-serving-server==0.3.2 # CPU
pip install paddle-serving-server-gpu==0.3.2.post9 # GPU with CUDA9.0
pip install paddle-serving-server-gpu==0.3.2.post10 # GPU with CUDA10.0

You may need to use a domestic mirror source (in China, you can use the Tsinghua mirror source, add -i https://pypi.tuna.tsinghua.edu.cn/simple to pip command) to speed up the download.

If you need install modules compiled with develop branch, please download packages from latest packages list and install with pip install command.

Packages of paddle-serving-server and paddle-serving-server-gpu support Centos 6/7 and Ubuntu 16/18.

Packages of paddle-serving-client and paddle-serving-app support Linux and Windows, but paddle-serving-client only support python2.7/3.6/3.7.

Recommended to install paddle >= 1.8.2.

Pre-built services with Paddle Serving

Latest release

Optical Character Recognition
Object Detection
Image Segmentation

Chinese Word Segmentation
> python -m paddle_serving_app.package --get_model lac
> tar -xzf lac.tar.gz
> python lac_web_service.py lac_model/ lac_workdir 9393 &
> curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "我爱北京天安门"}], "fetch":["word_seg"]}'

Image Classification

> python -m paddle_serving_app.package --get_model resnet_v2_50_imagenet
> tar -xzf resnet_v2_50_imagenet.tar.gz
> python resnet50_imagenet_classify.py resnet50_serving_model &
> curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"image": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}'

Quick Start Example

This quick start example is only for users who already have a model to deploy and we prepare a ready-to-deploy model here. If you want to know how to use paddle serving from offline training to online serving, please reference to Train_To_Service

Boston House Price Prediction model

wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
tar -xzf uci_housing.tar.gz

Paddle Serving provides HTTP and RPC based service for users to access

HTTP service

Paddle Serving provides a built-in python module called paddle_serving_server.serve that can start a RPC service or a http service with one-line command. If we specify the argument --name uci, it means that we will have a HTTP service with a url of $IP:$PORT/uci/prediction

python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 --name uci
Argument Type Default Description
thread int 4 Concurrency of current service
port int 9292 Exposed port of current service to users
name str "" Service name, can be used to generate HTTP request url
model str "" Path of paddle model directory to be served
mem_optim_off - - Disable memory / graphic memory optimization
ir_optim - - Enable analysis and optimization of calculation graph
use_mkl (Only for cpu version) - - Run inference with MKL

Here, we use curl to send a HTTP POST request to the service we just started. Users can use any python library to send HTTP POST as well, e.g, requests.

curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]}], "fetch":["price"]}'

RPC service

A user can also start a RPC service with paddle_serving_server.serve. RPC service is usually faster than HTTP service, although a user needs to do some coding based on Paddle Serving's python client API. Note that we do not specify --name here.

python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292
# A user can visit rpc service through paddle_serving_client API
from paddle_serving_client import Client

client = Client()
data = [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727,
        -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]
fetch_map = client.predict(feed={"x": data}, fetch=["price"])

Here, client.predict function has two arguments. feed is a python dict with model input variable alias name and values. fetch assigns the prediction variables to be returned from servers. In the example, the name of "x" and "price" are assigned when the servable model is saved during training.

Some Key Features of Paddle Serving

  • Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed with one line command.
  • Industrial serving features supported, such as models management, online loading, online A/B testing etc.
  • Distributed Key-Value indexing supported which is especially useful for large scale sparse features as model inputs.
  • Highly concurrent and efficient communication between clients and servers supported.
  • Multiple programming languages supported on client side, such as Golang, C++ and python.


New to Paddle Serving

Tutorial at AIStudio


About Efficiency





To connect with other users and contributors, welcome to join our Slack channel


If you want to contribute code to Paddle Serving, please reference Contribution Guidelines


For any feedback or to report a bug, please propose a GitHub Issue.


Apache 2.0 License

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A flexible, high-performance carrier for machine learning models(『飞桨』服务器端部署库) spread retract
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