GiganticMole91 if you wan to hack it, this is how:
` from clearml.storage.helper import StorageHelper
from clearml.backend_config.bucket_config import AzureContainerConfig
StorageHelper._azure_configurations._container_configs.append(
AzureContainerConfig(account_name="<account_name>", account_key="<account_key>", container_name="<container_name>")
) `
Perfect! Thanks SuccessfulKoala55 , that would be an acceptable workaround until setup_upload also supports Azure 🙂 🙌
GiganticMole91 for S3, I think you're looking for task.setup_upload()
Hi GiganticMole91 ,
I see that the storage settings are also available through environment variables, but I'm worried that the environment variables have already been parsed at that time.
I'm not sure I understand. Can you elaborate? How do you run in remotely? Do you raise an instance each time or are your instances persistent?
Hi CostlyOstrich36 , thanks for answering. We are using compute instances through the Machine Learning Studio in Azure. They basically work by spinning up an instance, loading a docker-image and executing a specific script in a folder that you upload along with the docker-image. Nothing is persisted between runs and there is no clear notion of a "user" (when thinking of ~/.clearml.conf at least).
SuccessfulKoala55 yeah, sorry, should have mentioned that our storage is also Azure (blob storage). I couldn't find the documentation for task.setup_upload() online, but the current version of the source code states that
Setup upload options (currently only S3 is supported)
as you mentioned. I'm using v1.5.0.