# Use this script to upload data to blob store # AzureML libraries from azureml.core import Workspace from azureml.core.dataset import Dataset from azureml.data.datapath import DataPath ws = Workspace.from_config() print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\n') data_dir = "bookcorpus_data" # Local directory for where data is located that includes .bin and .idx files blobstore_datadir = data_dir # Blob store directory to store data in datastore = ws.get_default_datastore() # Book Corpus Data print("upload dataset to blob store") uploaded_data = Dataset.File.upload_directory( src_dir=data_dir, target=DataPath(datastore, blobstore_datadir), show_progress=True ) # Usage after uploading the directory # To refer to the folder directly: train_dataset = Dataset.File.from_files(path=[(datastore, blobstore_datadir)]) print(train_dataset) # To refer to a specific file: # train_dataset = Dataset.File.from_files(path=[(datastore, blobstore_datadir + "/filename.ext")]) # Create DatasetConsumptionConfig to specify how to deliver the dataset to a compute target. # In the submitted run, files in the datasets will be either mounted or downloaded to local path on the compute target. # input_data_dir = train_dataset.as_mount() # input_data_dir = train_dataset.as_download()