The goal of Paddle Serving is to provide high-performance, flexible and easy-to-use industrial-grade online inference services for machine learning developers and enterprises.Paddle Serving supports multiple protocols such as RESTful, gRPC, bRPC, and provides inference solutions under a variety of hardware and multiple operating system environments, and many famous pre-trained model examples.The core features are as follows:
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:
- Integrate high-performance server-side inference engine paddle Inference and mobile-side engine paddle Lite. Models of other machine learning platforms (Caffe/TensorFlow/ONNX/PyTorch) can be migrated to paddle through [x2paddle](https://github.com/PaddlePaddle/X2Paddle).
- There are two frameworks, namely high-performance C++ Serving and high-easy-to-use Python pipeline.The C++ Serving is based on the bRPC network framework to create a high-throughput, low-latency inference service, and its performance indicators are ahead of competing products. The Python pipeline is based on the gRPC/gRPC-Gateway network framework and the Python language to build a highly easy-to-use and high-throughput inference service. How to choose which one please see [Techinical Selection]()
- Support multiple [protocols]() such as HTTP, gRPC, bRPC, and provide C++, Python, Java language SDK.
- Design and implement a high-performance inference service framework for asynchronous pipelines based on directed acyclic graph (DAG), with features such as multi-model combination, asynchronous scheduling, concurrent inference, dynamic batch, multi-card multi-stream inference, etc.- Adapt to a variety of commonly used computing hardwares, such as x86 (Intel) CPU, ARM CPU, Nvidia GPU, Kunlun XPU, etc.; Integrate acceleration libraries of Intel MKLDNN and Nvidia TensorRT, and low-precision and quantitative inference.
- Provide a model security deployment solution, including encryption model deployment, and authentication mechanism, HTTPs security gateway, which is used in practice.
- Support cloud deployment, provide a deployment case of Baidu Cloud Intelligent Cloud kubernetes cluster.
- Provide more than 40 classic pre-model deployment examples, such as PaddleOCR, PaddleClas, PaddleDetection, PaddleSeg, PaddleNLP, PaddleRec and other suites, and more models continue to expand.
- Supports distributed deployment of large-scale sparse parameter index models, with features such as multiple tables, multiple shards, multiple copies, local high-frequency cache, etc., and can be deployed on a single machine or clouds.
<h3align="center">Some Key Features of Paddle Serving</h3>
- Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed **with one line command**.
<h2align="center">Tutorial</h2>
-**Industrial serving features** supported, such as models management, online loading, online A/B testing etc.
-**Highly concurrent and efficient communication** between clients and servers supported.
-**Multiple programming languages** supported on client side, such as C++, python and Java.
***
***
- Any model trained by [PaddlePaddle](https://github.com/paddlepaddle/paddle) can be directly used or [Model Conversion Interface](./doc/SAVE.md) for online deployment of Paddle Serving.
- Support [Multi-model Pipeline Deployment](./doc/PIPELINE_SERVING.md), and provide the requirements of the REST interface and RPC interface itself, [Pipeline example](./python/examples/pipeline).
- Support the model zoos from the Paddle ecosystem, such as [PaddleDetection](./python/examples/detection), [PaddleOCR](./python/examples/ocr), [PaddleRec](https://github.com/PaddlePaddle/PaddleRec/tree/master/recserving/movie_recommender).
- Provide a variety of pre-processing and post-processing to facilitate users in training, deployment and other stages of related code, bridging the gap between AI developers and application developers, please refer to
[Serving Examples](./python/examples/).
- Video tutorial(Chinese) : [深度学习服务化部署-以互联网应用为例](https://aistudio.baidu.com/aistudio/course/introduce/19084)
<palign="center">
<palign="center">
<imgsrc="doc/images/demo.gif"width="700">
<imgsrc="doc/images/demo.gif"width="700">
</p>
</p>
<h2align="center">Documentation</h2>
***
<h2align="center">AIStudio Turorial</h2>
> Set up
Here we provide tutorial on AIStudio(Chinese Version) [AIStudio教程-Paddle Serving服务化部署框架](https://www.paddlepaddle.org.cn/tutorials/projectdetail/1555945)
This chapter guides you through the installation and deployment steps. It is strongly recommended to use Docker to deploy Paddle Serving. If you do not use docker, ignore the docker-related steps. Paddle Serving can be deployed on cloud servers using Kubernetes, running on many commonly hardwares such as ARM CPU, Intel CPU, Nvidia GPU, Kunlun XPU. The latest development kit of the develop branch is compiled and generated every day for developers to use.
The tutorial provides
-[Install Paddle Serving using docker](doc/Install.md)
<ul>
-[Build Paddle Serving from Source with Docker](doc/COMPILE.md)
<li>Paddle Serving Environment Setup</li>
-[Deploy Paddle Serving on Kubernetes](doc/PADDLE_SERVING_ON_KUBERNETES.md)
<ul>
-[Deploy Paddle Serving with Security gateway](doc/SERVING_AUTH_DOCKER.md)
<li>Running in docker images
-[Deploy Paddle Serving on more hardwares](doc/BAIDU_KUNLUN_XPU_SERVING.md)
<li>pip install Paddle Serving
-[Latest Wheel packages](doc/LATEST_PACKAGES.md)(Update everyday on branch develop)
</ul>
<li>Quick Experience of Paddle Serving</li>
<li>Advanced Tutorial of Model Deployment</li>
<ul>
<li>Save/Convert Models for Paddle Serving</li>
<li>Setup Online Inference Service</li>
</ul>
<li>Paddle Serving Examples</li>
<ul>
<li>Paddle Serving for Detections</li>
<li>Paddle Serving for OCR</li>
</ul>
</ul>
> Use
<h2align="center">Installation</h2>
The first step is to call the model save interface to generate a model parameter configuration file (.prototxt), which will be used on the client and server. The second step, read the configuration and startup parameters and start the service. According to API documents and your case, the third step is to write client requests based on the SDK, and test the inference service.
We **highly recommend** you to **run Paddle Serving in Docker**, please visit [Run in Docker](doc/RUN_IN_DOCKER.md). See the [document](doc/DOCKER_IMAGES.md) for more docker images.
-[Quick Start](doc/QuickStart.md)
-[Save a servable model](doc/SAVE.md)
-[Description of configuration and startup parameters](doc/SERVING_CONFIGURE.md)
-[Guide for RESTful/gRPC/bRPC APIs](doc/HTTP_SERVICE_CN.md)
-[Infer on quantizative models](doc/LOW_PRECISION_DEPLOYMENT_CN.md)
-[Data format of classic models](doc/PROCESS_DATA.md)
**Attention:**: Currently, the default GPU environment of paddlepaddle 2.1 is Cuda 10.2, so the sample code of GPU Docker is based on Cuda 10.2. We also provides docker images and whl packages for other GPU environments. If users use other environments, they need to carefully check and select the appropriate version.
> Developers
For Paddle Serving developers, we provide extended documents such as custom OP, level of detail(LOD) processing and performance indicators.
-[Custom Operators](doc/NEW_OPERATOR.md)
-[Processing LOD Data](doc/LOD_CN.md)
-[Benchmarks(Chinese)](doc/BENCHMARKING_GPU.md)
<h2align="center">Model Zoo</h2>
***
**Attention:** the following so-called 'python' or 'pip' stands for one of Python 3.6/3.7/3.8.
Paddle Serving works closely with the Paddle model suite, and implements a large number of service deployment examples, including image classification, object detection, language and text recognition, Chinese part of speech, sentiment analysis, content recommendation and other types of examples, for a total of 42 models.
pip3 install paddle-serving-server-gpu==0.6.2.post102 #GPU with CUDA10.2 + TensorRT7
# DO NOT RUN ALL COMMANDS! check your GPU env and select the right one
pip3 install paddle-serving-server-gpu==0.6.2.post101 # GPU with CUDA10.1 + TensorRT6
pip3 install paddle-serving-server-gpu==0.6.2.post11 # GPU with CUDA10.1 + TensorRT7
```
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. If you want to compile by yourself, please refer to [How to compile Paddle Serving?](./doc/COMPILE.md)
Packages of paddle-serving-server and paddle-serving-server-gpu support Centos 6/7, Ubuntu 16/18, Windows 10.
Packages of paddle-serving-client and paddle-serving-app support Linux and Windows, but paddle-serving-client only support python3.6/3.7/3.8.
**For latest version, Cuda 9.0 or Cuda 10.0 are no longer supported, Python2.7/3.5 is no longer supported.**
Recommended to install paddle >= 2.1.0
```
# CPU users, please run
pip3 install paddlepaddle==2.1.0
# GPU Cuda10.2 please run
pip3 install paddlepaddle-gpu==2.1.0
```
**Note**: If your Cuda version is not 10.2, please do not execute the above commands directly, you need to refer to [Paddle official documentation-multi-version whl package list
the default `paddlepaddle-gpu==2.1.0` is Cuda 10.2 with no TensorRT. If you want to install PaddlePaddle with TensorRT. please also check the documentation-multi-version whl package list and find key word `cuda10.2-cudnn8.0-trt7.1.3`. More info please check [Paddle Serving uses TensorRT](./doc/TENSOR_RT.md)
If it is other environment and Python version, please find the corresponding link in the table and install it with pip.
For **Windows Users**, please read the document [Paddle Serving for Windows Users](./doc/WINDOWS_TUTORIAL.md)
<h2align="center">Quick Start Example</h2>
This quick start example is mainly for those users who already have a model to deploy, and we also provide a model that can be used for deployment. in case if you want to know how to complete the process from offline training to online service, please refer to the AiStudio tutorial above.
### Boston House Price Prediction model
get into the Serving git directory, and change dir to `fit_a_line`
``` shell
cd Serving/python/examples/fit_a_line
sh get_data.sh
```
Paddle Serving provides HTTP and RPC based service for users to access
### 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.
| `use_calib` | bool | False | Use TRT int8 calibration |
| `gpu_multi_stream` | bool | False | EnableGpuMultiStream to get larger QPS |
#### Description of asynchronous model
Asynchronous mode is suitable for 1. When the number of requests is very large, 2. When multiple models are concatenated and you want to specify the concurrency number of each model.
Asynchronous mode helps to improve the throughput (QPS) of service, but for a single request, the delay will increase slightly.
In asynchronous mode, each model will start n threads of the number you specify, and each thread contains a model instance. In other words, each model is equivalent to a thread pool containing N threads, and the task is taken from the task queue of the thread pool to execute.
In asynchronous mode, each RPC server thread is only responsible for putting the request into the task queue of the model thread pool. After the task is executed, the completed task is removed from the task queue.
In the above table, the number of RPC server threads is specified by --thread, and the default value is 2.
--runtime_thread_num specifies the number of threads in the thread pool of each model. The default value is 0, indicating that asynchronous mode is not used.
--batch_infer_size specifies the number of batches for each model. The default value is 32. It takes effect when --runtime_thread_num is not 0.
#### When you want a model to use multiple GPU cards.
#### When a service contains two models, and each model needs to specify multiple GPU cards, and needs asynchronous mode, each model specifies different concurrency number.
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.
### WEB service
Users can also put the data format processing logic on the server side, so that they can directly use curl to access the service, refer to the following case whose path is `python/examples/fit_a_line`
Paddle Serving provides industry-leading multi-model tandem services, which strongly supports the actual operating business scenarios of major companies, please refer to [OCR word recognition](./python/examples/pipeline/ocr).
**Method Two** ：In the path where starting the Serving/Pipeline service or the path which environment variable SERVING_HOME set (the file named ProcessInfo.json exists in this path)
```
python3 -m paddle_serving_server.serve stop
```
<h2align="center">Document</h2>
### New to Paddle Serving
-[How to save a servable model?](doc/SAVE.md)
-[Write Bert-as-Service in 10 minutes](doc/BERT_10_MINS.md)
-[Paddle Serving Examples](python/examples)
-[How to process natural data in Paddle Serving?(Chinese)](doc/PROCESS_DATA.md)
-[How to process level of detail(LOD)?](doc/LOD.md)
### Developers
-[How to deploy Paddle Serving on K8S?(Chinese)](doc/PADDLE_SERVING_ON_KUBERNETES.md)
-[How to route Paddle Serving to secure endpoint?(Chinese)](doc/SERVING_AUTH_DOCKER.md)
-[How to develop a new Web Service?](doc/NEW_WEB_SERVICE.md)
若按缺省训练参数，本模型的embedding dim为100w，size为10，也就是参数矩阵为1000000 x 10的float型矩阵，实际占用内存共1000000 x 10 x sizeof(float) = 39MB；**实际场景中，embedding参数要大的多；因此该demo仅为演示使用**。
In some scenarios, multiple models with the same input may be used to predict in parallel and integrate predicted results for better prediction effect. Paddle Serving also supports this feature.
Next, we will take the text classification task as an example to show model ensemble in Paddle Serving (This feature is still serial prediction for the time being. We will support parallel prediction as soon as possible).
## Simple example
In this example (see the figure below), the server side predict the bow and CNN models with the same input in a service in parallel, The client side fetchs the prediction results of the two models, and processes the prediction results to get the final predict results.
It should be noted that at present, only multiple models with the same format input and output in the same service are supported. In this example, the input and output formats of CNN and BOW model are the same.
The code used in the example is saved in the `python/examples/imdb` path:
```shell
.
├── get_data.sh
├── imdb_reader.py
├── test_ensemble_client.py
└── test_ensemble_server.py
```
### Prepare data
Get the pre-trained CNN and BOW models by the following command (you can also run the `get_data.sh` script):
Different from the normal prediction service, here we need to use DAG to describe the logic of the server side.
When creating an Op, you need to specify the predecessor of the current Op (in this example, the predecessor of `cnn_infer_op` and `bow_infer_op` is `read_op`, and the predecessor of `response_op` is `cnn_infer_op` and `bow_infer_op`. For the infer Op `infer_op`, you need to define the prediction engine name `engine_name` (You can also use the default value. It is recommended to set the value to facilitate the client side to obtain the order of prediction results).
At the same time, when configuring the model path, you need to create a model configuration dictionary with the infer Op as the key and the corresponding model path as value to inform Serving which model each infer OP uses.
### Start client
Start client by the following Python code (you can also run the `test_ensemble_client.py` script):
```python
frompaddle_serving_clientimportClient
fromimdb_readerimportIMDBDataset
client=Client()
# If you have more than one model, make sure that the input
Compared with the normal prediction service, the client side has not changed much. When multiple model predictions are used, the prediction service will return a dictionary with engine name `engine_name`(the value is defined on the server side) as the key, and the corresponding model prediction results as the value.
