Reputation: 1829
I have an experiment (exp) which is published as a web service (exp [Predictive Exp.]) in the azure machine learning studio, the data used by this experiment was pushed by R using AzureML package
library(AzureML)
ws <- workspace(
id = 'xxxxxxxxx',
auth = 'xxxxxxxxxxx'
)
upload.dataset(data_for_azure, ws, "data_for_azure")
The above thing worked, but lets say I want to update the dataset(same schema just added more rows)
I tired this but this does not work:
delete.datasets(ws, "data_for_azure")
refresh(ws, what = c("everything", "data_for_azure", "exp", "exp [Predictive Exp.]"))
I get the error stating the following:
Error: AzureML returns error code:
HTTP status code : 409
Unable to delete dataset due to lingering dependants
I went through the documentation, and I know that a simple refresh is not possible(same name), the only alternative I see is to delete the web service and perform everything again
Any solution will be greatly helped!
Upvotes: 1
Views: 1535
Reputation: 7207
From the R doc.
The AzureML API does not support uploads for replacing datasets with new data by re-using a name. If you need to do this, first delete the dataset from the AzureML Studio interface, then upload a new version.
Now, I think this is particular for the R sdk, as the Python SDK, and the AzureML Studio UI lets you upload a new dataset. Will check in with the R team about this.
I would recommend uploading it as a new dataset with a new name, and then replacing the dataset in your experiment with this new dataset. Sorry this seem's round about, but I think is the easier option.
Unless you want to upload the new version using the AzureML Studio, in which case go to +NEW, Dataset, select your file and select the checkbox that says this is an existing dataset. The filename should be the same.
Upvotes: 1