Motivation

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:

Some Key Features

• 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.
• Extensible framework design which can support model serving beyond Paddle.

Installation

We highly recommend you to run Paddle Serving in Docker, please visit Run in Docker

# Run CPU Docker
docker pull hub.baidubce.com/paddlepaddle/serving:0.2.0
docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:0.2.0
docker exec -it test bash

# Run GPU Docker
nvidia-docker pull hub.baidubce.com/paddlepaddle/serving:0.2.0-gpu
nvidia-docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:0.2.0-gpu
nvidia-docker exec -it test bash

pip install paddle-serving-client 
pip install paddle-serving-server # CPU
pip install paddle-serving-server-gpu # GPU

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.

Client package support Centos 7 and Ubuntu 18, or you can use HTTP service without install client.

Quick Start Example

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	bool	False	Enable memory optimization

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 '{"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"]}' http://127.0.0.1:9292/uci/prediction

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()
client.load_client_config("uci_housing_client/serving_client_conf.prototxt")
client.connect(["127.0.0.1:9292"])
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"])
print(fetch_map)

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.

Pre-built services with Paddle Serving

Chinese Word Segmentation

• Description:

Chinese word segmentation HTTP service that can be deployed with one line command.

• Download Servable Package:

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

• Host web service:

tar -xzf lac_model_jieba_web.tar.gz
python lac_web_service.py jieba_server_model/ lac_workdir 9292

• Request sample:

curl -H "Content-Type:application/json" -X POST -d '{"words": "我爱北京天安门", "fetch":["word_seg"]}' http://127.0.0.1:9292/lac/prediction

• Request result:

{"word_seg":"我|爱|北京|天安门"}

Image Classification

• Description:

Image classification trained with Imagenet dataset. A label and corresponding probability will be returned.
Note: This demo needs paddle-serving-server-gpu. 

• Download Servable Package:

wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/imagenet_demo.tar.gz

• Host web service:

tar -xzf imagenet_demo.tar.gz
python image_classification_service_demo.py resnet50_serving_model

• Request sample:

curl -H "Content-Type:application/json" -X POST -d '{"url": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg", "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction

• Request result:

{"label":"daisy","prob":0.9341403245925903}

More Demos

Key	Value
Model Name	Bert-Base-Baike
URL	https://paddle-serving.bj.bcebos.com/bert_example/bert_seq128.tar.gz
Client/Server Code	https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/bert
Description	Get semantic representation from a Chinese Sentence

Key	Value
Model Name	Resnet50-Imagenet
URL	https://paddle-serving.bj.bcebos.com/imagenet-example/ResNet50_vd.tar.gz
Client/Server Code	https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imagenet
Description	Get image semantic representation from an image

Key	Value
Model Name	Resnet101-Imagenet
URL	https://paddle-serving.bj.bcebos.com/imagenet-example/ResNet101_vd.tar.gz
Client/Server Code	https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imagenet
Description	Get image semantic representation from an image

Key	Value
Model Name	CNN-IMDB
URL	https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_model.tar.gz
Client/Server Code	https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imdb
Description	Get category probability from an English Sentence

Key	Value
Model Name	LSTM-IMDB
URL	https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_model.tar.gz
Client/Server Code	https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imdb
Description	Get category probability from an English Sentence

Key	Value
Model Name	BOW-IMDB
URL	https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_model.tar.gz
Client/Server Code	https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imdb
Description	Get category probability from an English Sentence

Key	Value
Model Name	Jieba-LAC
URL	https://paddle-serving.bj.bcebos.com/lac/lac_model.tar.gz
Client/Server Code	https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/lac
Description	Get word segmentation from a Chinese Sentence

Key	Value
Model Name	DNN-CTR
URL	https://paddle-serving.bj.bcebos.com/criteo_ctr_example/criteo_ctr_demo_model.tar.gz
Client/Server Code	https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/criteo_ctr
Description	Get click probability from a feature vector of item

Document

New to Paddle Serving

• How to save a servable model?
• An End-to-end tutorial from training to inference service deployment
• Write Bert-as-Service in 10 minutes

Developers

• How to config Serving native operators on server side?
• How to develop a new Serving operator?
• Golang client
• Compile from source code

About Efficiency

• How to profile Paddle Serving latency?
• How to optimize performance?(Chinese)
• Deploy multi-services on one GPU(Chinese)
• CPU Benchmarks(Chinese)
• GPU Benchmarks(Chinese)

FAQ

• FAQ(Chinese)

Design

• Design Doc

Community

Slack

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

Contribution

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

Feedback

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

License

Apache 2.0 License