提交 3aeda852 编写于 作者: H HexToString

fix JAVA_SDK

上级 56bf8827
......@@ -20,6 +20,7 @@ The following table shows compatibilities between Paddle Serving Server and Java
| :---------------------------: | :--------------: |
| 0.3.2 | 0.0.1 |
1、Directly use the provided Java SDK as the client for prediction
### Install Java SDK
You can download jar and install it to the local Maven repository:
......@@ -29,14 +30,6 @@ wget https://paddle-serving.bj.bcebos.com/jar/paddle-serving-sdk-java-0.0.1.jar
mvn install:install-file -Dfile=$PWD/paddle-serving-sdk-java-0.0.1.jar -DgroupId=io.paddle.serving.client -DartifactId=paddle-serving-sdk-java -Dversion=0.0.1 -Dpackaging=jar
```
Or compile from the source code and install it to the local Maven repository:
```shell
cd Serving/java
mvn compile
mvn install
```
### Maven configure
```text
......@@ -47,63 +40,9 @@ mvn install
</dependency>
```
2、Use it after compiling from the source code. See the file:
https://github.com/PaddlePaddle/Serving/blob/develop/java/README_CN.md
3、examples for using the java client, see the file:
https://github.com/PaddlePaddle/Serving/blob/develop/java/README_CN.md
## Example
Here we will show how to use Java SDK for Boston house price prediction. Please refer to [examples](../java/examples) folder for more examples.
### Get model
```shell
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
tar -xzf uci_housing.tar.gz
```
### Start Python Server
```shell
python -m paddle_serving_server.serve --model uci_housing_model --port 9393 --use_multilang
```
#### Client side code example
```java
import io.paddle.serving.client.*;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import java.util.*;
public class PaddleServingClientExample {
public static void main( String[] args ) {
float[] data = {0.0137f, -0.1136f, 0.2553f, -0.0692f,
0.0582f, -0.0727f, -0.1583f, -0.0584f,
0.6283f, 0.4919f, 0.1856f, 0.0795f, -0.0332f};
INDArray npdata = Nd4j.createFromArray(data);
HashMap<String, INDArray> feed_data
= new HashMap<String, INDArray>() {{
put("x", npdata);
}};
List<String> fetch = Arrays.asList("price");
Client client = new Client();
String target = "localhost:9393";
boolean succ = client.connect(target);
if (succ != true) {
System.out.println("connect failed.");
return ;
}
Map<String, INDArray> fetch_map = client.predict(feed_data, fetch);
if (fetch_map == null) {
System.out.println("predict failed.");
return ;
}
for (Map.Entry<String, INDArray> e : fetch_map.entrySet()) {
System.out.println("Key = " + e.getKey() + ", Value = " + e.getValue());
}
return ;
}
}
```
......@@ -19,6 +19,7 @@ Paddle Serving 提供了 Java SDK,支持 Client 端用 Java 语言进行预测
| :---------------------------: | :--------------: |
| 0.3.2 | 0.0.1 |
1、直接使用提供的Java SDK作为Client进行预测
### 安装
您可以直接下载 jar,安装到本地 Maven 库:
......@@ -28,14 +29,6 @@ wget https://paddle-serving.bj.bcebos.com/jar/paddle-serving-sdk-java-0.0.1.jar
mvn install:install-file -Dfile=$PWD/paddle-serving-sdk-java-0.0.1.jar -DgroupId=io.paddle.serving.client -DartifactId=paddle-serving-sdk-java -Dversion=0.0.1 -Dpackaging=jar
```
或者从源码进行编译,安装到本地 Maven 库:
```shell
cd Serving/java
mvn compile
mvn install
```
### Maven 配置
```text
......@@ -46,64 +39,9 @@ mvn install
</dependency>
```
2、从源码进行编译后使用,详细步骤见:
https://github.com/PaddlePaddle/Serving/blob/develop/java/README_CN.md
3、相关使用示例见:
https://github.com/PaddlePaddle/Serving/blob/develop/java/README_CN.md
## 使用样例
这里将展示如何使用 Java SDK 进行房价预测,更多例子详见 [examples](../java/examples) 文件夹。
### 获取房价预测模型
```shell
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
tar -xzf uci_housing.tar.gz
```
### 启动 Python 端 Server
```shell
python -m paddle_serving_server.serve --model uci_housing_model --port 9393 --use_multilang
```
### Client 端代码示例
```java
import io.paddle.serving.client.*;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import java.util.*;
public class PaddleServingClientExample {
public static void main( String[] args ) {
float[] data = {0.0137f, -0.1136f, 0.2553f, -0.0692f,
0.0582f, -0.0727f, -0.1583f, -0.0584f,
0.6283f, 0.4919f, 0.1856f, 0.0795f, -0.0332f};
INDArray npdata = Nd4j.createFromArray(data);
HashMap<String, INDArray> feed_data
= new HashMap<String, INDArray>() {{
put("x", npdata);
}};
List<String> fetch = Arrays.asList("price");
Client client = new Client();
String target = "localhost:9393";
boolean succ = client.connect(target);
if (succ != true) {
System.out.println("connect failed.");
return ;
}
Map<String, INDArray> fetch_map = client.predict(feed_data, fetch);
if (fetch_map == null) {
System.out.println("predict failed.");
return ;
}
for (Map.Entry<String, INDArray> e : fetch_map.entrySet()) {
System.out.println("Key = " + e.getKey() + ", Value = " + e.getValue());
}
return ;
}
}
```
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