Hi, We are facing on deploying the model over Azure Machine learning workspace described below: A custom model was built over notebook on AML Workspace and it is deployed following the below documentation from MS https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2&tabs=azure-studio Currently, on deploying the model using real time endpoint, below error is occuring : ResourceOperationFailure : ResourceNotReady: User container has crashed or terminated. Please see troubleshooting guide, available here: https://aka.ms/oe-tsg#error-resourcenotready We have tried troubleshooting but the issue persists. Please share your ideas/thoughts to cross over the issue. Regards, Sudheer

Issue with Model Deployment over Azure Machine learning Workspace

Naga Sudheer Madduru 1

Hi,

We are facing on deploying the model over Azure Machine learning workspace described below:

A custom model was built over notebook on AML Workspace and it is deployed following the below documentation from MS https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2&tabs=azure-studio
Currently, on deploying the model using real time endpoint, below error is occuring : ResourceOperationFailure: ResourceNotReady: User container has crashed or terminated. Please see troubleshooting guide, available here: https://aka.ms/oe-tsg#error-resourcenotready

We have tried troubleshooting but the issue persists.

Please share your ideas/thoughts to cross over the issue.

Regards,

Sudheer

romungi-MSFT 42,976 Reputation points Microsoft Employee

2024-05-09T18:09:34.38+00:00

@Naga Sudheer Madduru User container has crashed or terminated" usually means that the init() function of score.py failed to load the model. So I would suggest to check the init() to see if model path is correct or not, and if can load model successfully or not. Thanks!!
Naga Sudheer Madduru 1 Reputation point

2024-05-14T12:39:18.17+00:00

Hi,

In the logs it says package is missing.

But I have made sure that in the environment package is there.

But still the issue issue persists.

Also, i tried couple of more ways to deploy i.e.

Through Kubernetes cluster and through code .

Please share your ideas on this issue.

Regards,

Sudheer