1. Cloud Storage

1.1. External Storage Dataset

If you already have your data managed and organized on a cloud storage service, such as GCS/S3/Azure, you may want toutilize that with Dataloop, and not upload the binaries and create duplicates.

1.1.1. Cloud Storage Integration

Access & Permissions - Creating an integration with GCS/S2/Azure cloud requires adding a key/secret with the followingpermissions:

List (Mandatory) - allowing Dataloop to list all the items in the storage.Get (Mandatory) - get the items and perform pre-process functionalities like thumbnails, item info etc.Put / Write (Mandatory) - lets you upload your itemsdirectly to the external storage from the Dataloop platform.Delete - lets you delete your items directly from the external storage using the Dataloop platform.

1.1.2. Create Integration With GCS

1.1.3. Creating an integration GCS requires having JSON file with GCS configuration.

import dtlpy as dl
if dl.token_expired():
organization = dl.organizations.get(organization_name=org_name)
with open(r"C:\gcsfile.json", 'r') as f:
    gcs_json = json.load(f)
gcs_to_string = json.dumps(gcs_json)
                                 options={'key': '',
                                          'secret': '',
                                          'content': gcs_to_string})

1.1.4. Create Integration With Amazon S3

import dtlpy as dl
if dl.token_expired():
organization = dl.organizations.get(organization_name='my-org')
organization.integrations.create(name='S3integration', integrations_type=dl.ExternalStorage.S3,
                                 options={'key': "my_key", 'secret': "my_secret"})

1.1.5. Create Integration With Azure

import dtlpy as dl
if dl.token_expired():
organization = dl.organizations.get(organization_name='my-org')
                                 options={'key': 'my_key',
                                          'secret': 'my_secret',
                                          'clientId': 'my_clientId',
                                          'tenantId': 'my_tenantId'})

1.1.6. External Storage Driver

Once you have an integration, you can set up a driver, which adds a specific bucket (and optionally with a specificpath/folder) as a storage resource.

1.1.7. Create Drivers in the Platform (browser)

# param name: the driver name
# param driver_type: ExternalStorage.S3, ExternalStorage.GCS , ExternalStorage.AZUREBLOB
# param integration_id: the integration id
# param bucket_name: the external bucket name
# param project_id:
# param allow_external_delete:
# param region: relevant only for s3 - the bucket region
# param storage_class: relevant only for s3
# param path: Optional. By default, path is the root folder. Path is case sensitive.
# return: driver object
import dtlpy as dl
project = dl.projects.get('prject_name')
driver = project.drivers.create(name='driver_name',

Once the integration and drivers are ready, you can create a Dataloop Dataset and sync all the data:

# create a dataset from a driver name, you can also create by the driver ID
import dtlpy as dl
project: dl.Project
dataset = project.datasets.create(dataset_name=dataset_name,