Delete README_CN.md

README_CN.md

-# Paddle Serving
-
-Paddle Serving是PaddlePaddle的在线预估服务框架，能够帮助开发者轻松实现从移动端、服务器端调用深度学习模型的远程预测服务。当前Paddle Serving以支持PaddlePaddle训练的模型为主，可以与Paddle训练框架联合使用，快速部署预估服务。Paddle Serving围绕常见的工业级深度学习模型部署场景进行设计，一些常见的功能包括多模型管理、模型热加载、基于[Baidu-rpc](https://github.com/apache/incubator-brpc)的高并发低延迟响应能力、在线模型A/B实验等。与Paddle训练框架互相配合的API可以使用户在训练与远程部署之间无缝过度，提升深度学习模型的落地效率。
-
-------------
-
-## 快速上手指南
-
-Paddle Serving当前的develop版本支持轻量级Python API进行快速预测，并且与Paddle的训练可以打通。我们以最经典的波士顿房价预测为示例，完整说明在单机进行模型训练以及使用Paddle Serving进行模型部署的过程。
-
-#### 安装
-```
-pip install paddle-serving-client
-pip install paddle-serving-server
-```
-
-#### 训练脚本
-``` python
-import sys
-import paddle
-import paddle.fluid as fluid
-
-train_reader = paddle.batch(paddle.reader.shuffle(
-    paddle.dataset.uci_housing.train(), buf_size=500), batch_size=16)
-
-test_reader = paddle.batch(paddle.reader.shuffle(
-    paddle.dataset.uci_housing.test(), buf_size=500), batch_size=16)
-
-x = fluid.data(name='x', shape=[None, 13], dtype='float32')
-y = fluid.data(name='y', shape=[None, 1], dtype='float32')
-
-y_predict = fluid.layers.fc(input=x, size=1, act=None)
-cost = fluid.layers.square_error_cost(input=y_predict, label=y)
-avg_loss = fluid.layers.mean(cost)
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.01)
-sgd_optimizer.minimize(avg_loss)
-
-place = fluid.CPUPlace()
-feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
-exe = fluid.Executor(place)
-exe.run(fluid.default_startup_program())
-
-import paddle_serving_client.io as serving_io
-
-for pass_id in range(30):
-    for data_train in train_reader():
-        avg_loss_value, = exe.run(
-            fluid.default_main_program(),
-            feed=feeder.feed(data_train),
-            fetch_list=[avg_loss])
-
-serving_io.save_model(
-    "serving_server_model", "serving_client_conf",
-    {"x": x}, {"y": y_predict}, fluid.default_main_program())
-```
-
-#### 服务器端代码
-``` python
-import sys
-from paddle_serving.serving_server import OpMaker
-from paddle_serving.serving_server import OpSeqMaker
-from paddle_serving.serving_server import Server
-
-op_maker = OpMaker()
-read_op = op_maker.create('general_reader')
-general_infer_op = op_maker.create('general_infer')
-
-op_seq_maker = OpSeqMaker()
-op_seq_maker.add_op(read_op)
-op_seq_maker.add_op(general_infer_op)
-
-server = Server()
-server.set_op_sequence(op_seq_maker.get_op_sequence())
-server.load_model_config(sys.argv[1])
-server.prepare_server(workdir="work_dir1", port=9393, device="cpu")
-server.run_server()
-```
-
-#### 服务器端启动
-``` shell
-python test_server.py serving_server_model
-```
-
-#### 客户端预测
-``` python
-from paddle_serving_client import Client
-import paddle
-import sys
-
-client = Client()
-client.load_client_config(sys.argv[1])
-client.connect(["127.0.0.1:9292"])
-
-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": data[0][0]}, fetch=["y"])
-    print("{} {}".format(fetch_map["y"][0], data[0][1][0]))
-
-```
-
-### 文档
-
-[设计文档](doc/DESIGN.md)
-
-[FAQ](doc/FAQ.md)
-
-### 资深开发者使用指南
-
-[编译指南](doc/INSTALL.md)
-
-## 贡献
-如果你想要给Paddle Serving做贡献，请参考[贡献指南](doc/CONTRIBUTE.md)