Hi Sam Preston,
Thanks for reaching out to Microsoft Q&A.
The below code snippet demonstrates how to mount a datastore in Azure Machine Learning using Python. The mount
method creates a context that must be started to access the mounted files.
from azureml.core import Workspace, Datastore, Dataset
# Connect to the workspace
workspace = Workspace.from_config()
# Retrieve the datastore
datastore = Datastore.get(workspace, 'your_datastore_name')
# Create a dataset from the datastore
dataset = Dataset.File.from_files((datastore, 'path/to/your/files'))
# Mount the dataset
mounted_path = '/mnt/mount_dir'
mount_context = dataset.mount(mounted_path)
# Start the mount context
mount_context.start()
# Use the mounted path for your operations
# Stop the mount context when done
mount_context.stop()
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