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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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  1. High-Performance Features
  2. Deep Memory

How it Works

Understanding Deep Memory

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How Deep Memory Works

Deep Memory computes transformation of your embeddings based on several examples of embeddings for which the most relevant embedding in the Vector Store is known. The transformation is computed computed on a Deep Lake Managed Service, and it is applied at inference in Deep Lake's Tensor Query Language (TQL).

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