Platform For AI:Deploy and call a model service on EAS

Prerequisites

Activate PAI and create a workspace using your Alibaba Cloud main account. Log on to the PAI console, select a region in the top-left corner, and complete the one-click authorization and product activation.

Billing

This example uses public resources to create a model service. The billing method is pay-as-you-go. For more information about billing rules, see EAS billing.

Prepare resources

To deploy a model service, prepare model files and code files, such as web interfaces. If the official platform image does not meet your deployment requirements, also build your own image.

# Download the model
from modelscope import snapshot_download
model_dir = snapshot_download('Qwen/Qwen3-0.6B')

from flask import Flask

app = Flask(__name__)

@app.route('/hello/model')
def hello_world():
    # You can call the model here to get results.
    return 'Hello World'

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)

Deploy the service

1. Log on to the PAI console. Select a region on the top of the page. Then, select the desired workspace and click Elastic Algorithm Service (EAS).

2. Click Deploy Service. In the Custom Model Deployment section, click Custom Deployment.

3. Configure the deployment parameters. Configure key parameters as follows and keep default values for other parameters.

   • Deployment Method: Select Image-based Deployment.

   • Image Configuration: In the Alibaba Cloud Image list, select vllm:0.11.2-mows0.5.1.

   • Mount storage: This example stores the model file in OSS at path oss://examplebucket/models/Qwen/Qwen3-0___6B. Select OSS and configure as follows.

     • Uri: OSS path where the model is located. Set to oss://examplebucket/models/.

     • Mount Path: Destination path in the service instance where the file is mounted, such as /mnt/data/.

   • Command: The official image has a default startup command. Modify as needed. For this example, change to vllm serve /mnt/data/Qwen/Qwen3-0___6B.

   • Resource Type: Select Public Resources. For Resource Specification, select ecs.gn7i-c16g1.4xlarge. To use other resource types, see Resource configurations.

4. Click Deploy. Service deployment takes about 5 minutes. When Service Status changes to Running, the service is successfully deployed.

Test the service

After the service is deployed, use the online debugging feature to test whether the service runs correctly. Configure the request method, request path, and request body based on your model service.

The online debugging method for the service deployed in this example:

1. On the Inference Service tab, click the destination service to go to the service overview page. Switch to the Online Debugging tab.

2. In the Request Parameter Online Tuning section of the debugging page, set request parameters and click Send Request. Request parameters:

   • Chat interface: Append /v1/chat/completions to the existing URL.

   • Headers: Add a request header. Set key to Content-Type and value to application/json.

     

   • Body:

     {
       "model": "/mnt/data/Qwen/Qwen3-0___6B",
       "messages": [
         {
           "role": "user",
           "content": "Hello!"
         }
       ],
       "max_tokens": 1024
     }

3. The response is shown in the following figure:

   

Call the service

curl http://16********.cn-hangzhou.pai-eas.aliyuncs.com/api/predict/****/v1/chat/
completions \
-H "Content-Type: application/json" \
-H "Authorization: *********5ZTM1ZDczg5OT**********" \
-X POST \
-d '{
  "model": "/mnt/data/Qwen/Qwen3-0___6B",
  "messages": [
    {
      "role": "user",
      "content": "Hello!"
    }
  ],
  "max_tokens": 1024
}' 

import requests

# Replace with the actual endpoint.
url = 'http://16********.cn-hangzhou.pai-eas.aliyuncs.com/api/predict/***/v1/chat/completions'
# For the header, set Authorization value to the actual token.
headers = {
    "Content-Type": "application/json",
    "Authorization": "*********5ZTM1ZDczg5OT**********",
}
# Construct the service request based on the data format required by the model.
data = {
  "model": "/mnt/data/Qwen/Qwen3-0___6B",
  "messages": [
    {
      "role": "user",
      "content": "Hello!"
    }
  ],
  "max_tokens": 1024
}
# Send the request.
resp = requests.post(url, json=data, headers=headers)
print(resp)
print(resp.content)

Stop or delete the service

This example uses public resources to create the EAS service, which is billed pay-as-you-go. When no longer needed, stop or delete the service to avoid further charges.

References

• To improve LLM service efficiency, see Deploy an LLM Intelligent Router.

• For more information about EAS features, see EAS overview.