Refresh the dataset in Azure machine learning

问题

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!

回答1:

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.

来源：https://stackoverflow.com/questions/36125274/refresh-the-dataset-in-azure-machine-learning