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  • 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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On this page
  • Overview of Deep Lake's Managed Tensor Database
  • Architecture
  • Interfaces for the Managed Database

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  1. Enterprise Features

Tensor Database

Deep Lake Managed Database

PreviousPerformant DataloaderNextREST API

Last updated 2 years ago

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Overview of Deep Lake's Managed Tensor Database

Deep Lake offers a Managed Tensor Database for a variety of dataset operations. The database is serverless, which simplifies self-hosting and substantially lowers costs. Currently, it only supports dataset queries, including vector search, but additional features for creating and modifying data being added.

To create a Deep Lake dataset in the Managed Tensor Database, please specify dataset_path = hub://org_id/dataset_name and runtime = {"tensor_db": True} during dataset creation. Full details on path and storage management are available here.

Architecture

The Managed Tensor Database is serverless and can deployed in the user's VPC.

Interfaces for the Managed Database

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REST API