Testing the Deployed Service¶
After an AI application is deployed as a real-time service, you can debug code or add files for testing on the Prediction tab page. Based on the input request (JSON text or file) defined by the AI application, the service can be tested in either of the following ways:
JSON Text Prediction: If the input type of the AI application of the deployed service is JSON text, that is, the input does not contain files, you can enter the JSON code on the Prediction tab page for service testing.
File Prediction: If the input type of the AI application of the deployed service is file, including images, audios, and videos, you can add images on the Prediction tab page for service testing.
Note
If the input type is image, the size of a single image must be less than 8 MB.
The following image types are supported: png, psd, jpg, jpeg, bmp, gif, webp, psd, svg, and tiff.
This function is used for commissioning. In actual production, you are advised to call APIs. Access the real-time service through token-based authentication.
Input Parameters¶
For the service that you have deployed, you can learn about its input parameters of the service, that is, the input request type mentioned above, on the Usage Guides tab page of the service details page.
The input parameters displayed on the Usage Guides tab page depend on the AI application source that you select.
If your metamodel comes from ExeML or a built-in algorithm, the input and output parameters are defined by ModelArts. For details, see the Usage Guides tab page. On the Prediction tab page, enter the corresponding JSON text or file for service testing.
If you use a custom meta model with the inference code and configuration file compiled by yourself (Specifications for Writing the Model Configuration File), the Usage Guides tab page only visualizes your data. The following figure shows the mapping between the input parameters displayed on the Usage Guides tab page and the configuration file.
JSON Text Prediction¶
Log in to the ModelArts management console and choose Service Deployment > Real-Time Services.
On the Real-Time Services page, click the name of the target service. The service details page is displayed. On the Prediction tab page, enter the prediction code and click Predict to perform prediction.
File Prediction¶
Log in to the ModelArts management console and choose Service Deployment > Real-Time Services.
On the Real-Time Services page, click the name of the target service. The service details page is displayed. On the Prediction tab page, click Upload and select a test file. After the file is uploaded successfully, click Predict to perform a prediction test.