Create Web API


This section introduces how to deploy a registered model on the Platform as a Web API using the ABEJA Platform GUI.

Create a Web API for inference

The model registered in ABEJA Platform will be deployed as a Web API. In ABEJA Platform, WebAPI is called HTTP service. You can deploy a machine learning model as a WebAPI simply by specifying the model version, instance type, and number of instances.

Create a deployment

Click “Deployment” in the left menu. Click the “Create Deployment” button displayed in the upper right corner of the screen.

Set the following values ​​in the input form and click “Create Deployment” to create the deployment.

Item Value Remark
Deployment Name Any value -
Description No input required -

Create and manage inference codes

Click “Codes” in the left menu.

Create an inference code from “Create Version”.

To create an inference code, enter the following items and click “Create”.

Item Value Remark
Source Code -
Inference code in Zip format and upload / template selection
Code Version 1.0.0 Enter a managed version of the inference code
Runtime abeja-inc/all-cpu:19.04 Select a Docker image to perform inference
Model Handler predict:handler Specify Handler to execute inference code

The code is now created and can be managed in a list.

Create an HTTP service

Finally, using the model and inference code, create an HTTP service (Web API).

Click “Services” in the left menu.

To create an HTTP service, enter the following items and click Create HTTP Service.

Item Value Remark
Code 1.0.0 Specify the version of inference code managed by Deployment
Model fashion_classification Specify the model
Model Version XXXXXXXXXXXX Specify the training job ID to be used
Instance Type cpu-0.25 HTTP service specifications
Instance Number 1 Number of instances

After creation, the following screen is displayed. The “Status” item is “In Progress” immediately after creation. “Status” is automatically updated. If it is not updated, click “Refresh” at the top of the screen.

After a few minutes, make sure that the HTTP Service you created becomes “Ready”.

Create an alias

You can create any alias for the Web API endpoint. Aliases can be used to easily and safely switch Web APIs that send requests.

First, select Add Endpoint and set an alias name.

Set the following values ​​in the input form and click “Add Endpoint” to create an alias.

Item Value
Primary endpoint? Set the checkbox to ON
Alias name No input required. It becomes invalid when the above check box is turned ON.
Model version (Cloud service only) Created model version
Access control Public ・ Organization・ Access key

If it is set to `primary endopoint`, it can be called in the following format as a unique endpoint in the deployment. __https://{ORGANIZATION_NAME}{DEPLOYMENT_ID}__

Check the operation of the created Web API

Next, check the operation of the deployed Web API.

Enter data for the model and check the output results

Click the “Check” button for the created HTTP service.

If you select the “Upload File” tab, you can upload the image on the PC to the HTTP service and check the operation.

Drag and drop any image included in the zip file downloaded in Create Model and click the “Send” button to check the inference result.