Troubleshoot model deployment
There are a lot of elements to a service deployment, including the trained model, the runtime environment configuration, the scoring script, the container image, and the container host. Troubleshooting a failed deployment, or an error when consuming a deployed service can be complex.
Check the service state
As an initial troubleshooting step, you can check the status of a service by examining its state:
from azureml.core.webservice import AksWebservice
# Get the deployed service
service = AksWebservice(name='classifier-service', workspace=ws)
# Check its state
print(service.state)
Note
To view the state of a service, you must use the compute-specific service type (for example AksWebservice) and not a generic WebService object.
For an operational service, the state should be Healthy.
Review service logs
If a service is not healthy, or you are experiencing errors when using it, you can review its logs:
print(service.get_logs())
The logs include detailed information about the provisioning of the service, and the requests it has processed; and can often provide an insight into the cause of unexpected errors.
Deploy to a local container
Deployment and runtime errors can be easier to diagnose by deploying the service as a container in a local Docker instance, like this:
from azureml.core.webservice import LocalWebservice
deployment_config = LocalWebservice.deploy_configuration(port=8890)
service = Model.deploy(ws, 'test-svc', [model], inference_config, deployment_config)
You can then test the locally deployed service using the SDK:
print(service.run(input_data = json_data))
You can then troubleshoot runtime issues by making changes to the scoring file that is referenced in the inference configuration, and reloading the service without redeploying it (something you can only do with a local service):
service.reload()
print(service.run(input_data = json_data))