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v3.8.16
v3.8.16
  • Deep Lake Docs
  • Vector Store Quickstart
  • Deep Learning Quickstart
  • Storage & Credentials
    • Storage Options
    • User Authentication
    • Storing Deep Lake Data in Your Own Cloud
      • Microsoft Azure
        • Provisioning Federated Credentials
        • Enabling CORS
      • Amazon Web Services
        • Provisioning Role-Based Access
        • Enabling CORS
  • List of ML Datasets
  • 🏢High-Performance Features
    • Introduction
    • Performant Dataloader
    • Tensor Query Language (TQL)
      • TQL Syntax
      • Sampling Datasets
    • Deep Memory
      • How it Works
    • Index for ANN Search
      • Caching and Optimization
    • Managed Tensor Database
      • REST API
      • Migrating Datasets to the Tensor Database
  • 📚EXAMPLE CODE
    • Getting Started
      • Vector Store
        • Step 1: Hello World
        • Step 2: Creating Deep Lake Vector Stores
        • Step 3: Performing Search in Vector Stores
        • Step 4: Customizing Vector Stores
      • Deep Learning
        • 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
    • Tutorials (w Colab)
      • Vector Store Tutorials
        • Vector Search Options
          • Deep Lake Vector Store API
          • REST API
          • LangChain API
        • Image Similarity Search
        • Deep Lake Vector Store in LangChain
        • Deep Lake Vector Store in LlamaIndex
        • Improving Search Accuracy using Deep Memory
      • 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
      • Concurrent Writes
        • Concurrency Using Zookeeper Locks
    • Playbooks
      • Querying, Training and Editing Datasets with Data Lineage
      • Evaluating Model Performance
      • Training Reproducibility Using Deep Lake and Weights & Biases
      • Working with Videos
    • Low-Level API Summary
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
      • Concurrent Writes
    • Data Layout
    • Version Control and Querying
    • Dataset Visualization
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in dataloaders
    • How to Contribute
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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

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  1. Storage & Credentials

Storing Deep Lake Data in Your Own Cloud

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

PreviousUser AuthenticationNextMicrosoft Azure

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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

  • 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 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.

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 Compute Engine
Deep Lake App
provisioning steps here
Authentication Using Managed Credentials