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Deep Lake Docs
Vector Store Quickstart
Deep Learning Quickstart
Storage & Credentials
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 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 Learning Tutorials
  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
Low-Level API Summary
🔬Technical Details

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Overview of Deep Lake's Managed Tensor Database
Architecture
Interfaces for the Managed Database

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Tensor Database

Deep Lake Managed Database

PreviousPerformant Dataloader NextREST API

Last updated 1 year ago

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