(简体中文|English)
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:
Installation
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 bashpip install paddle-serving-client
pip install paddle-serving-server # CPU
pip install paddle-serving-server-gpu # GPUYou 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"]}' http://127.0.0.1:9393/lac/prediction
{"result":[{"word_seg":"我|爱|北京|天安门"}]}
Image Classification
> 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"]}' http://127.0.0.1:9393/lac/prediction
{"result":[{"word_seg":"我|爱|北京|天安门"}]}
> 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"]}' http://127.0.0.1:9292/image/prediction
{"result":{"label":["daisy"],"prob":[0.9341403245925903]}}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.gzPaddle 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"]}' http://127.0.0.1:9292/uci/predictionRPC 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.
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.
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?
- How to develop a new Web Service?
- Golang client
- Compile from source code
- Deploy Web Service with uWSGI
- Hot loading for model file
About Efficiency
- How to profile Paddle Serving latency?
- How to optimize performance?
- Deploy multi-services on one GPU(Chinese)
- CPU Benchmarks(Chinese)
- GPU Benchmarks(Chinese)
FAQ
Design
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.