Hanchi Wang

@JingchaoZhang thx for your feedback. subscription_id, resource_group, workspace are refereeing to local variables one should define in advance.

@edgBR I really appreciate your feedback. Pipeline jobs as endpoints are coming up in the next a couple of months in v2. I'd love to know what other gaps you're...

This is not a supported scenario yet. We don't allow customized output path. azure-ai-ml prob should raise the right error message in this scenario.

@wangchao1230 What do you think of adding a validation in Output class's constructor?

@nagydavid Thx for providing the feedback. This issue sounds like is caused by numpy not being supported natively on M1. Have you also reported this to numpy? One workaround is...

@anliakho2 Can you provide an update on this issue? Thank you.

This looks like an example issue to me. As I don't see the referenced folder, `./component/train`, in the same folder as the notebook. I'll find the owner to fix this.

@Howzaa I believe you need to use the Azure storage SDK for that. E.g., https://learn.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python?tabs=managed-identity%2Croles-azure-portal%2Csign-in-azure-cli#upload-blobs-to-a-container

Hello @monajalal. Have you tried to use compute clusters in AzureML? AzureML compute clusters automatically scale up/down based on your training job requirements. And you can subscribe to events of...

@monajalal to clarify, is this an AzureML compute instance you're using? Looks like you need a bigger VM. You can also consider creating a dedicated training cluster. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python