[中文](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)
## Motivation
Paddle Serving helps deep learning developers deploy an online inference service without much effort. **The goal of this project**: once you have trained a deep neural nets with [Paddle](https://github.com/PaddlePaddle/Paddle), you already have a model inference service. A demo of serving is as follows:
## Key Features
- Integrate with Paddle training pipeline seemlessly, 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 that is especially useful for large scale sparse features as model inputs.
- **Highly concurrent and efficient communication** between clients and servers.
- **Multiple programming languages** supported on client side, such as Golang, C++ and python
- **Extensible framework design** that can support model serving beyond Paddle.
## Installation
```shell
pip install paddle-serving-client
pip install paddle-serving-server
```
## Quick Start Example
### Boston House Price Prediction model
``` shell
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
``` shell
python -m paddle_serving_server.web_serve --model uci_housing_model --thread 10 --port 9292 --name uci
```
``` shell
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
``` shell
python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292
```
``` python
# 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)
```
## Models waiting for you to deploy
| Model Name | Resnet50 |
|:--------------------: |:----------------------------------: |
| Package URL | To be released |
| Description | Get the representation of an image |
| Training Data Source | Imagenet |
## Document
[How to save a servable model?](doc/SAVE.md)
[How to config Serving native operators on server side?](doc/SERVER_DAG.md)
[How to develop a new Serving operator](doc/NEW_OPERATOR.md)
[Golang client](doc/IMDB_GO_CLIENT.md)
[Compile from source code(Chinese)](doc/COMPILE.md)
[How profile serving efficiency?(Chinese)](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/util)
[FAQ(Chinese)](doc/FAQ.md)
[Design Doc(Chinese)](doc/DESIGN.md)
## Join Community
To connect with other users and contributors, welcome to join our [Slack channel](https://paddleserving.slack.com/archives/CUBPKHKMJ)
## Contribution
If you want to contribute code to Paddle Serving, please reference [Contribution Guidelines](doc/CONTRIBUTE.md)
### Feedback
For any feedback or to report a bug, please propose a [GitHub Issue](https://github.com/PaddlePaddle/Serving/issues).
## License
[Apache 2.0 License](https://github.com/PaddlePaddle/Serving/blob/develop/LICENSE)