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v3.1.5
v3.1.5
  • Deep Lake Docs
  • List of ML Datasets
  • Quickstart
  • Dataset Visualization
  • Storage & Credentials
    • Storage Options
    • Managed Credentials
      • Enabling CORS
      • Provisioning Role-Based Access
  • API Reference
  • Enterprise Features
    • Querying Datasets
      • Sampling Datasets
    • Performant Dataloader
  • EXAMPLE CODE
  • Getting Started
    • Step 1: Hello World
    • Step 2: Creating Deep Lake Datasets
    • Step 3: Understanding Compression
    • Step 4: Accessing 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)
    • Creating Datasets
      • Creating Complex Datasets
      • Creating Object Detection Datasets
      • Creating Time-Series Datasets
      • Creating Datasets with Sequences
      • Creating Video Datasets
    • Training Models
      • 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
    • Data Processing Using Parallel Computing
  • Playbooks
    • Querying, Training and Editing Datasets with Data Lineage
    • Evaluating Model Performance
    • Training Reproducibility Using Deep Lake and Weights & Biases
    • Working with Videos
  • API Summary
  • How Deep Lake Works
    • Data Layout
    • Version Control and Querying
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in ds.pytorch()
    • Storage Synchronization
    • How to Contribute
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Storage & Credentials

How to access datasets in other clouds and manage their credentials.

PreviousDataset VisualizationNextStorage Options

Last updated 2 years ago

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Accessing datasets in AWS, Google Cloud, and more

Deep Lake can be used as a pure OSS package without any registration or relationship with Activeloop. However, registering with Activeloop offers several benefits:

  • Storage provided by Activeloop

  • Access to , which provides dataset visualization, querying, version control UI, dataset analytics, and other powerful features

  • Managed credentials for Deep Lake datasets and tensor data stored outside of Activeloop

Deep Lake UI
Storage Options
Managed Credentials