Storing Deep Lake Data in Your Own Cloud
How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
Connecting data from your own cloud and managing credentials in Deep Lake unlocks several important capabilities:
- Simpler access to Deep Lake datasets stored in your own cloud using the Python API
- No need for continuously specifying cloud access keys in Python
In order for the Deep Lake to access datasets or linked tensors stored in the user's cloud, Deep Lake must authenticate the respective cloud resources. Access can be provided using access keys or using role-based access (provisioning steps here). The video below summarizes the UI for managing your cloud credentials.
Authentication Using Managed Credentials
Default storage enables you to map the Deep Lake path
hub://org_id/dataset_name
to a cloud path of your choice. Subsequently, all datasets created using the Deep Lake path will be stored at the user-specified specified, and they can be accessed using API tokens and managed credentials from Deep Lake. By default, the default storage is set as Activeloop Storage, and you may change it using the UI below.Note: that in order to visualize data in the Deep Lake browser application, it is necessary to enable CORS in the bucket containing any source data.
If you do not set the Default Storage as your own cloud, Datasets in user's clouds can be connected to the Deep Lake App using the Python API below. Once a dataset is connected to Deep Lake, it is assigned a Deep Lake path
hub://org_id/dataset_name
, and it can be accessed using API tokens and managed credentials from Deep Lake, without continuously having to specify cloud credentials.# Step 1: Create the dataset directly in the cloud using your own cloud creds
ds = deeplake.empty('s3://my_bucket/dataset_name', creds = {...})
# Step 2: Connect the dataset to Deep Lake and specify the managed credentials
# (creds_key) for accessing the data (See Managed Credentials above)
ds.connect(org_id = 'org_id', creds_key = 'my_creds_key', token = 'my_token')
OR
ds.connect(dest_path = 'hub://org_id/dataset_name', creds_key = 'my_creds_key', token = 'my_token')
Specifying
org_id
creates the dataset in the specified org using the dataset_name
from the cloud path. Specifying the
dest_path
creates the dataset at the org_id
and dataset_name
from the specified path. Managed credentials can be used for accessing data stored in linked tensors. Simply add the managed credentials to the dataset's
creds_keys
and assign them to each sample.ds.create_tensors('images', htype = 'link[image]', sample_compression = 'jpeg')
ds.add_creds_key('my_creds_key', managed=True)
ds.images.append(deeplake.link(link_to_sample, creds_key = 'my_creds_key')