Call a service over a public endpoint - Platform For AI - Alibaba Cloud Documentation Center

Elastic Algorithm Service (EAS) allows you to call a service over a common public endpoint by using the official SDK for Python, official SDK for Java, or custom call logic. This topic describes these call methods in detail.

Call a service over a common public endpoint

After a model is deployed as a service in EAS, a public endpoint is automatically generated for the service. You can perform the following steps to obtain the public endpoint of a service: On the Elastic Algorithm Service (EAS) page, find the service that you want to call and click Invoke Intro in the Service Method column. In the Invoke Intro dialog box, click the Public Network Invoke tab to check the Access address value and the link to relevant documentation, as well as the Token value. Based on the call information, you can perform a call test. The following code provides an example on a call test:

$ curl http://166408185518****.cn-hangzhou.pai-eas.aliyuncs.com/api/predict/heart_predict_online -H 'Authorization: ZGI1YWYxNmQwYjMzMDM1YTNlMmFmNmEzYjIzZTVlZGQ0NDJhYTRm****' -d '[{"sex":0,"cp":0,"fbs":0,"restecg":0,"exang":0,"slop":0,"thal":0,"age":0,"trestbps":0,"chol":0,"thalach":0,"oldpeak":0,"ca":0}]'

Note

You can replace http with https for more secure encrypted transmission.

After the call test succeeds, you can call the service. To facilitate service calls, EAS allows you to use one of the following methods to call a service over a common public endpoint:

Use the official SDK for Python
EAS encapsulates the call logic and provides the official SDK for Python.
Use the official SDK for Java
EAS encapsulates the call logic and provides the official SDK for Java.
Use custom call logic
We recommend that you use the preceding two official SDKs to call services. This reduces the amount of time consumed to define call logic and improves the call stability. If you need to use SDKs in other languages or use custom call logic, you can follow the demo in the "Use custom call logic" section of this topic. To implement custom call logic, you must construct service requests based on specific frameworks. For more information, see Construct a request for a TensorFlow service.

Use the official SDK for Python

Install the SDK.
```
pip install -U eas-prediction --user
```
For more information about how to use the SDK for Python, visit GitHub.

Compile a call program.

In this example, a program that uses strings as input and output is used. For information about sample programs with other input and output formats, such as TensorFlow and PyTorch programs, visit GitHub.

#!/usr/bin/env python
from eas_prediction import PredictClient
from eas_prediction import StringRequest
if __name__ == '__main__':
    # Set the input of client = PredictClient() to the public endpoint that is used to call the service. 
    client = PredictClient('http://166408185518****.cn-hangzhou.pai-eas.aliyuncs.com','heart_predict_online')
    # Use the token that is obtained on the Public Network Invoke tab. For more information, see the "Call a service over a common public endpoint" section of this topic. 
    client.set_token('ZGI1YWYxNmQwYjMzMDM1YTNlMmFmNmEzYjIzZTVlZGQ0NDJhYTRm****')    
    client.init()
    # Construct the request based on the model that you want to use. In this example, the input and output are of the STRING type. 
    request = StringRequest('[{"sex":0,"cp":0,"fbs":0,"restecg":0,"exang":0,"slop":0,"thal":0,"age":0,"trestbps":0,"chol":0,"thalach":0,"oldpeak":0,"ca":0}]')    
    for x in range(0, 1):
        resp = client.predict(request)
        print(resp)

The input of the client = PredictClient() function is the public endpoint that is used to call the service. For example, if the public endpoint is http://166408185518****.cn-hangzhou.pai-eas.aliyuncs.com/api/predict/heart_predict_online, the format that is used to call the PredictClient() function is client = PredictClient('http://166408185518****.cn-hangzhou.pai-eas.aliyuncs.com','heart_predict_online').

Run the call program.
```
$ python heart_predict.py
```
The heart_predict.py parameter specifies the name of the Python program. You can replace it with the actual program name. The following prediction result is returned after the call program is run:
```
IT-C02YJ0V8JHD2:Desktop wowei$ python heart_predict.py
[{"p_0":0.9941226679708888,"p_1":0.005877332029111252}]
```

Use the official SDK for Java

Add the required dependency.
You must use Maven to manage projects when you define code that runs on a Java client. Therefore, you must add the client dependency eas-sdk to the pom.xml file. The latest version of the dependency is 2.0.1. The following code provides an example on how to add the dependency:
```
<dependency>
  <groupId>com.aliyun.openservices.eas</groupId>
  <artifactId>eas-sdk</artifactId>
  <version>2.0.1</version>
</dependency>
```
For more information about how to use the SDK for Java, visit GitHub.

Compile a call program.

import com.aliyun.openservices.eas.predict.http.PredictClient;
import com.aliyun.openservices.eas.predict.http.HttpConfig;
public class Test_String {
    public static void main(String[] args) throws Exception{
        // Start and initialize the client.         
        PredictClient client = new PredictClient(new HttpConfig());
        // Use the token that is obtained on the Public Network Invoke tab. For more information, see the "Call a service over a common public endpoint" section of this topic.     
        client.setToken("ZGI1YWYxNmQwYjMzMDM1YTNlMmFmNmEzYjIzZTVlZGQ0NDJhYTRm****");
        // Set the endpoint to the public endpoint that is used to call the service. 
        client.setEndpoint("166408185518****.cn-hangzhou.pai-eas.aliyuncs.com");
        // Set the service name. 
        client.setModelName("heart_predict_online"); 
        // Define the input string. Construct the request based on the model that you want to use. In this example, the input and output are of the STRING type. 
        String request = "[{\"sex\":0,\"cp\":0,\"fbs\":0,\"restecg\":0,\"exang\":0,\"slop\":0,\"thal\":0,\"age\":0,\"trestbps\":0,\"chol\":0,\"thalach\":0,\"oldpeak\":0,\"ca\":0}]";
        System.out.println(request);  
        // Return a string by using EAS.       
        try {
            String response = client.predict(request);
            System.out.println(response);
        } catch(Exception e) {
            e.printStackTrace();
        }
        // Shut down the client. 
        client.shutdown();
        return;
    }
}

The input of the client.setEndpoint() and client.setModelName() functions is the public endpoint that is used to call the service. For example, if the public endpoint is http://166408185518****.cn-hangzhou.pai-eas.aliyuncs.com/api/predict/heart_predict_online, the format that is used to call the client.setEndpoint() function is client.setEndpoint("166408185518****.cn-hangzhou.pai-eas.aliyuncs.com") and the format that is used to call the client.setModelName() function is client.setModelName("heart_predict_online").

Run the call program.
The following result is returned after the call program is run:
```
[{"p_0":0.9941226679708888,"p_1":0.005877332029111252}]
```

Use custom call logic

EAS supports custom call logic in Python, Java, and other languages. You must customize service requests based on specific frameworks. For more information, see Construct a request for a TensorFlow service. The following code provides an example on how to define call logic:

import requests
# Use the public endpoint that is obtained on the Public Network Invoke tab as the URL. For more information, see the "Call a service over a common public endpoint" section of this topic. 
url = 'http://166408185518****.cn-hangzhou.pai-eas.aliyuncs.com/api/predict/heart_predict_online'
# Add the token that is obtained on the Public Network Invoke tab to the header. For more information, see the "Call a service over a common public endpoint" section of this topic. 
headers = {"Authorization": 'ZGI1YWYxNmQwYjMzMDM1YTNlMmFmNmEzYjIzZTVlZGQ0NDJhYTRm****'}
# Construct the service request based on the data format required by the model that you want to use. In this example, the input and output are of the STRING type. 
data = '[{"sex":0,"cp":0,"fbs":0,"restecg":0,"exang":0,"slop":0,"thal":0,"age":0,"trestbps":0,"chol":0,"thalach":0,"oldpeak":0,"ca":0}]'
resp = requests.post(url, data=data, headers=headers)
print resp
print resp.content