([简体中文](./README_CN.md)|English)




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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](https://github.com/PaddlePaddle/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](https://github.com/PaddlePaddle/Serving/blob/develop/doc/RUN_IN_DOCKER.md) ``` # 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-gpu nvidia-docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:latest-gpu nvidia-docker exec -it test bash ``` ```shell 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. If you need install modules compiled with develop branch, please download packages from [latest packages list](./doc/LATEST_PACKAGES.md) and install with `pip install` command. Client package support Centos 7 and Ubuntu 18, or you can use HTTP service without install client.

Pre-built services with Paddle Serving

Chinese Word Segmentation

``` shell > python -m paddle_serving_app.package --get_model lac > tar -xzf lac.tar.gz > python lac_web_service.py 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



``` shell > 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](https://github.com/PaddlePaddle/Serving/blob/develop/doc/TRAIN_TO_SERVICE.md) ### 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 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` ``` shell 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` | - | - | Enable 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](https://requests.readthedocs.io/en/master/).
``` shell 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/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. ``` 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) ``` 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?](doc/SAVE.md) - [An End-to-end tutorial from training to inference service deployment](doc/TRAIN_TO_SERVICE.md) - [Write Bert-as-Service in 10 minutes](doc/BERT_10_MINS.md) ### Developers - [How to config Serving native operators on server side?](doc/SERVER_DAG.md) - [How to develop a new Serving operator?](doc/NEW_OPERATOR.md) - [How to develop a new Web Service?](doc/NEW_WEB_SERVICE.md) - [Golang client](doc/IMDB_GO_CLIENT.md) - [Compile from source code](doc/COMPILE.md) - [Deploy Web Service with uWSGI](doc/UWSGI_DEPLOY.md) - [Hot loading for model file](doc/HOT_LOADING_IN_SERVING.md) ### About Efficiency - [How to profile Paddle Serving latency?](python/examples/util) - [How to optimize performance?(Chinese)](doc/PERFORMANCE_OPTIM_CN.md) - [Deploy multi-services on one GPU(Chinese)](doc/MULTI_SERVICE_ON_ONE_GPU_CN.md) - [CPU Benchmarks(Chinese)](doc/BENCHMARKING.md) - [GPU Benchmarks(Chinese)](doc/GPU_BENCHMARKING.md) ### FAQ - [FAQ(Chinese)](doc/deprecated/FAQ.md) ### Design - [Design Doc](doc/DESIGN_DOC.md)

Community

### Slack 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)