I am not expert but the error message can tell that there is an issue with the underlying compute resources or the configuration related to network restrictions, especially since you mentioned that both the workspace and the container registry are in a private endpoint with a VNET.
Start by verifying that your compute target (for example your compute instance or cluster) is properly configured to communicate with your workspace and container registry through the private endpoints.
Make sure that the required outbound ports for Azure Machine Learning are open in your VNET, especially for accessing storage (port 443), container registry, and any external dependencies such as package repositories (for Conda or pip).
Based on what I saw on some forums also the issue could be related to the environment setup, especially considering the mix of dependencies between Conda and pip. Some suggestions:
- Check that
prophet
and its dependencies are being installed correctly. It might require specific versions ofpystan
orcmdstanpy
, so you may need to explicitly specify these. - Try isolating the
prophet
installation by including its dependencies under pip or conda, rather than mixing them.
If you're using a custom compute target (like a managed compute cluster), ensure that any required IP (from your workspace or storage) are whitelisted in your VNET or firewall configuration.
Some links to help you :
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-private-endpoint
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-training-environments