I am not expert in this matter but I will try to give you some hints that may help you to resolve your issue.
Even though the logs are long, they are the best source of information on what might be going wrong. Look for keywords like "error," "fail," or "exception" to identify specific issues.
# Check deployment logsaz ml model deploy --name mymodel --model model:1 --instance-type Standard_DS3_v2 --debug
Verify that your compute instance has enough resources (CPU, memory, ...) to handle the deployment.
Double-check that all necessary permissions are correctly configured:
- Blob Storage: Ensure that the service principal or identity being used has access to the blob storage.
- Compute Resources: Ensure that the deployment has the necessary permissions to create and manage compute resources.
Sometimes, issues can arise from the Docker image or environment setup:
- Base Image: Make sure you are using a compatible base image that supports the libraries and frameworks your model requires.
- Custom Dockerfile: If you're using a custom Dockerfile, verify that it is correctly set up and that all necessary libraries are installed.
Check if you are hitting any quota limits on your Azure subscription, such as the number of compute instances or concurrent deployments.
If the issue persists after troubleshooting, consider reaching out to Azure Support for detailed assistance.