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v3.6.1
v3.6.1
  • 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
      • Amazon Web Services
        • Provisioning Role-Based Access
        • Enabling CORS
  • List of ML Datasets
  • API Reference
  • 🏢Enterprise Features
    • Compute Engine
      • Tensor Query Language (TQL)
        • TQL Syntax
        • Sampling Datasets
      • Performant Dataloader
    • 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 the Vector Store
      • 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
      • Deep Lake Vector Store in LangChain
    • 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
    • Updating Datasets
    • 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
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
    • Data Layout
    • Version Control and Querying
    • Dataset Visualization
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in dataloaders
    • How to Contribute
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