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f7377499
编写于
7月 17, 2020
作者:
B
barrierye
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Merge branch 'java-sdk' of
https://github.com/barrierye/Serving
into java-sdk
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1655467e
c68bda6c
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doc/JAVA_SDK.md
doc/JAVA_SDK.md
+94
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doc/JAVA_SDK_CN.md
doc/JAVA_SDK_CN.md
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doc/JAVA_SDK.md
0 → 100644
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f7377499
# Paddle Serving Client Java SDK
(
[
简体中文
](
JAVA_SDK_CN.md
)
|English)
Paddle Serving provides Java SDK,which supports predict on the Client side with Java language. This document shows how to use the Java SDK.
## Getting started
### Prerequisites
```
- Java 8 or higher
- Apache Maven
```
The following table shows compatibilities between Paddle Serving Server and Java SDK.
| Paddle Serving Server version | Java SDK version |
| :---------------------------: | :--------------: |
| 0.3.2 | 0.0.1 |
### Install Java SDK
You can use Apache Maven to download the SDK.
```
text
<dependency>
<groupId>io.paddle.serving.client</groupId>
<artifactId>paddle-serving-sdk-java</artifactId>
<version>0.0.1</version>
</dependency>
```
## 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
;
}
}
```
doc/JAVA_SDK_CN.md
0 → 100644
浏览文件 @
f7377499
# Paddle Serving Client Java SDK
(简体中文|
[
English
](
JAVA_SDK.md
)
)
Paddle Serving 提供了 Java SDK,支持 Client 端用 Java 语言进行预测,本文档说明了如何使用 Java SDK。
## 快速开始
### 环境要求
```
- Java 8 or higher
- Apache Maven
```
下表显示了 Paddle Serving Server 和 Java SDK 之间的兼容性
| Paddle Serving Server version | Java SDK version |
| :---------------------------: | :--------------: |
| 0.3.2 | 0.0.1 |
### 安装
您可以使用 Apache Maven 下载该 SDK。
```
text
<dependency>
<groupId>io.paddle.serving.client</groupId>
<artifactId>paddle-serving-sdk-java</artifactId>
<version>0.0.1</version>
</dependency>
```
## 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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