Merge pull request #1063 from wangjiawei04/v0.5.0

V0.5.0 pick 2 pr

README.md

@@ -31,6 +31,15 @@
 
 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:
 
+<h3 align="center">Some Key Features of Paddle Serving</h3>
+
+- 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.
+- **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_CN.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/tools/recserving/movie_recommender).
@@ -197,14 +206,6 @@ the response is
 {"result":{"price":[[18.901151657104492]]}}
 ```
 
-<h2 align="center">Some Key Features of Paddle Serving</h2>
-
-- 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.
-
 <h2 align="center">Document</h2>
 
 ### New to Paddle Serving

README_CN.md

@@ -33,6 +33,15 @@
 
 Paddle Serving 旨在帮助深度学习开发者轻易部署在线预测服务。 **本项目目标**: 当用户使用 [Paddle](https://github.com/PaddlePaddle/Paddle) 训练了一个深度神经网络，就同时拥有了该模型的预测服务。
 
+<h3 align="center">Paddle Serving的核心功能</h3>
+
+- 与Paddle训练紧密连接，绝大部分Paddle模型可以 **一键部署**.
+- 支持 **工业级的服务能力** 例如模型管理，在线加载，在线A/B测试等.
+- 支持客户端和服务端之间 **高并发和高效通信**.
+- 支持 **多种编程语言** 开发客户端，例如C++, Python和Java.
+
+***
+
 - 任何经过[PaddlePaddle](https://github.com/paddlepaddle/paddle)训练的模型，都可以经过直接保存或是[模型转换接口](./doc/SAVE_CN.md)，用于Paddle Serving在线部署。
 - 支持[多模型串联服务部署](./doc/PIPELINE_SERVING_CN.md), 同时提供Rest接口和RPC接口以满足您的需求，[Pipeline示例](./python/examples/pipeline)。
 - 支持Paddle生态的各大模型库, 例如[PaddleDetection](./python/examples/detection)，[PaddleOCR](./python/examples/ocr)，[PaddleRec](https://github.com/PaddlePaddle/PaddleRec/tree/master/tools/recserving/movie_recommender)。
@@ -198,14 +207,6 @@ curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [0.0137, -0.1
 {"result":{"price":[[18.901151657104492]]}}
 ```
 
-<h2 align="center">Paddle Serving的核心功能</h2>
-
-- 与Paddle训练紧密连接，绝大部分Paddle模型可以 **一键部署**.
-- 支持 **工业级的服务能力** 例如模型管理，在线加载，在线A/B测试等.
-- 支持 **分布式键值对索引** 助力于大规模稀疏特征作为模型输入.
-- 支持客户端和服务端之间 **高并发和高效通信**.
-- 支持 **多种编程语言** 开发客户端，例如Golang，C++和Python.
-
 <h2 align="center">文档</h2>
 
 ### 新手教程

doc/GRPC_IMPL_CN.md

@@ -115,24 +115,12 @@ python test_asyn_client.py
 python test_batch_client.py
 ```
 
-#### 通用 pb 预测
-
-``` shell
-python test_general_pb_client.py
-```
-
 #### 预测超时
 
 ``` shell
 python test_timeout_client.py
 ```
 
-#### List 输入
-
-``` shell
-python test_list_input_client.py
-```
-
 ## 3.更多示例
 
 详见[`python/examples/grpc_impl_example`](../python/examples/grpc_impl_example)下的示例文件。

doc/JAVA_SDK.md

@@ -18,7 +18,7 @@ The following table shows compatibilities between Paddle Serving Server and Java
 
 | Paddle Serving Server version | Java SDK version |
 | :---------------------------: | :--------------: |
-|             0.3.2             |      0.0.1       |
+|             0.5.0             |      0.0.1       |
 
 1.    Directly use the provided Java SDK as the client for prediction
 ### Install Java SDK

doc/JAVA_SDK_CN.md

@@ -17,7 +17,7 @@ Paddle Serving 提供了 Java SDK，支持 Client 端用 Java 语言进行预测
 
 | Paddle Serving Server version | Java SDK version |
 | :---------------------------: | :--------------: |
-|             0.3.2             |      0.0.1       |
+|             0.5.0             |      0.0.1       |
 
 1.    直接使用提供的Java SDK作为Client进行预测
 ### 安装

python/examples/fit_a_line/test_numpy_input_client.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# pylint: disable=doc-string-missing
-
-from paddle_serving_client import Client
-import numpy as np
-import sys
-
-client = Client()
-client.load_client_config(sys.argv[1])
-client.connect(["127.0.0.1:9393"])
-
-import paddle
-test_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.uci_housing.test(), buf_size=500),
-    batch_size=1)
-
-for data in test_reader():
-    fetch_map = client.predict(
-        feed={"x": np.array(data[0][0])}, fetch=["price"])
-    print("{} {}".format(fetch_map["price"][0][0], data[0][1][0]))