Managed Credentials
How to manage your credentials with Activeloop Platform
Managing your Credentials with Activeloop
Managing credentials in Deep Lake enables:
Access to the Deep Lake UI for datasets stored in your own cloud
Simpler access to Deep Lake dataset stored in your own cloud using the Pyton API
No need for continuously specifying cloud access keys in Python
Managed Credentials
In order for the Deep Lake UI to access datasets or linked tensors stored in the user's cloud, Deep Lake must be able to authenticate and gain access the respective cloud resources. Access can be provided using access keys, or using role-based access (provisioning steps here) that grants Deep Lake permissions to specific cloud resources. The video below summarizes the UI for managing your cloud credentials.
Connecting Deep Lake Datasets to the UI
Datasets in Activeloop storage are automatically connected to Activeloop Platform. Datasets in user's clouds can be connected to Activeloop Platform using python API or UI below. In order to visualize data in the Deep Lake browser application, it is necessary to enable CORS in the bucket containing any source data.
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.
Connecting Datasets in the Python API
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.
Connecting Datasets in the Deep Lake UI
Default Storage
By default, any dataset created using the Deep Lake path hub://org_id/dataset_name
, is stored in Activeloop storage. You may change the default storage location for Deep Lake paths to a location of your choice using the UI below. Subsequently, all datasets created using the Deep Lake path will be stored at the specified location.
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