LogoLogo
API ReferenceGitHubSlackService StatusLogin
v3.9.16
v3.9.16
  • 🏠Deep Lake Docs
  • List of ML Datasets
  • 🏗️SETUP
    • Installation
    • User Authentication
      • Workload Identities (Azure Only)
    • Storage and Credentials
      • Storage Options
      • Setting up Deep Lake in Your Cloud
        • Microsoft Azure
          • Configure Azure SSO on Activeloop
          • Provisioning Federated Credentials
          • Enabling CORS
        • Google Cloud
          • Provisioning Federated Credentials
          • Enabling CORS
        • Amazon Web Services
          • Provisioning Role-Based Access
          • Enabling CORS
  • 📚Examples
    • Deep Learning
      • Deep Learning Quickstart
      • Deep Learning Guide
        • Step 1: Hello World
        • Step 2: Creating Deep Lake Datasets
        • Step 3: Understanding Compression
        • Step 4: Accessing and Updating Data
        • Step 5: Visualizing Datasets
        • Step 6: Using Activeloop Storage
        • Step 7: Connecting Deep Lake Datasets to ML Frameworks
        • Step 8: Parallel Computing
        • Step 9: Dataset Version Control
        • Step 10: Dataset Filtering
      • Deep Learning Tutorials
        • Creating Datasets
          • Creating Complex Datasets
          • Creating Object Detection Datasets
          • Creating Time-Series Datasets
          • Creating Datasets with Sequences
          • Creating Video Datasets
        • Training Models
          • Splitting Datasets for Training
          • Training an Image Classification Model in PyTorch
          • Training Models Using MMDetection
          • Training Models Using PyTorch Lightning
          • Training on AWS SageMaker
          • Training an Object Detection and Segmentation Model in PyTorch
        • Updating Datasets
        • Data Processing Using Parallel Computing
      • Deep Learning Playbooks
        • Querying, Training and Editing Datasets with Data Lineage
        • Evaluating Model Performance
        • Training Reproducibility Using Deep Lake and Weights & Biases
        • Working with Videos
      • Deep Lake Dataloaders
      • API Summary
    • RAG
      • RAG Quickstart
      • RAG Tutorials
        • Vector Store Basics
        • Vector Search Options
          • LangChain API
          • Deep Lake Vector Store API
          • Managed Database REST API
        • Customizing Your Vector Store
        • Image Similarity Search
        • Improving Search Accuracy using Deep Memory
      • LangChain Integration
      • LlamaIndex Integration
      • Managed Tensor Database
        • REST API
        • Migrating Datasets to the Tensor Database
      • Deep Memory
        • How it Works
    • Tensor Query Language (TQL)
      • TQL Syntax
      • Index for ANN Search
        • Caching and Optimization
      • Sampling Datasets
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
      • Concurrent Writes
        • Concurrency Using Zookeeper Locks
    • Deep Lake Data Format
      • Tensor Relationships
      • Version Control and Querying
    • Dataset Visualization
      • Visualizer Integration
    • Shuffling in Dataloaders
    • How to Contribute
Powered by GitBook
On this page
  • Connecting Data From Your Cloud Using Deep Lake Managed Credentials
  • Managed Credentials
  • Default Storage
  • Connecting Deep Lake Dataset in your Cloud to the Deep Lake to App
  • Using Manage Credentials with Linked Tensors

Was this helpful?

Edit on GitHub
  1. SETUP
  2. Storage and Credentials

Setting up Deep Lake in Your Cloud

How to store Deep Lake data in your own cloud and manage credentials with Deep Lake

PreviousStorage OptionsNextMicrosoft Azure

Last updated 11 months ago

Was this helpful?

Connecting Data From Your Cloud Using Deep Lake Managed Credentials

Connecting data from your own cloud and managing credentials in Deep Lake unlocks several important capabilities:

  • Access to performant features such as the Deep Lake Compute Engine

  • Access to the for datasets stored in your own cloud

  • 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

Managed Credentials

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 (). The video below summarizes the UI for managing your cloud credentials.

Default Storage

Default storage enables you to map the Deep Lake path hub://org_id/dataset_nameto 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.

Connecting Deep Lake Dataset in your Cloud to the Deep Lake to App

Connecting Datasets in the Python API

# Step 1: Create/load the dataset directly in the cloud using your org_id and
# Managed Credentials (creds_key) for accessing the data (See Managed Credentials above)
ds = deeplake.empty/load('s3://my_bucket/dataset_name', 
                    creds={'creds_key': 'managed_creds_key'}, org_id='my_org_id')

# Step 2a: Connect the dataset to Deep Lake, inheriting the dataset_name above
ds.connect()
## ->>> This produces a Deep Lake path for accessing the dataset such as:
## ---- 'hub://my_org_id/dataset_name'

## OR

# Step 2b: Specify your own path and dataset name for future access to the dataset.
# You can also specify different managed credentials, if desired
ds.connect(dest_path = 'hub://org_id/dataset_name', creds_key = 'my_creds_key')

Using Manage Credentials with Linked Tensors

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')

Note: that in order to visualize data in the Deep Lake browser application, it is necessary to 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 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.

Managed credentials can be used for accessing data stored in . Simply add the managed credentials to the dataset's creds_keys and assign them to each sample.

🏗️
enable CORS
Deep Lake App
linked tensors
Deep Lake App
provisioning steps here
Authentication Using Managed Credentials