v3.6.18

v3.9.16 v3.9.0 v3.8.27 v3.8.19 v3.8.16 v3.8.2 v3.8.0 v3.7.2 v3.7.1 v3.7.0 v3.6.18 v3.6.10 v3.6.8 v3.6.3 v3.6.2 v3.6.1 v3.6.0 v3.5.0 v3.4.0 v3.2.22 v3.2.21 v3.2.9 v3.2.0 v3.1.5 v3.1.1 v3.1.0 v3.0.16 v3.0.10 v3.0.0

API Reference GitHub Slack Service Status Login

API Reference GitHub Slack Service Status Login

Deep Lake Docs
Vector Store Quickstart
Deep Learning Quickstart
Storage & Credentials
List of ML Datasets
🏢High-Performance 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)
Playbooks
Low-Level API Summary
🔬Technical Details

Powered by GitBook

On this page

Vector Store use-cases are implemented using an API that balances simplicity and customizability
Deep Learning use-cases are implemented using the low-level API that offers maximum customizability

Was this helpful?

Getting Started

Comprehensive guides for getting started with Deep Lake

PreviousMigrating Datasets to the Tensor Database NextVector Store

Vector Store use-cases are implemented using an API that balances simplicity and customizability

Deep Learning use-cases are implemented using the low-level API that offers maximum customizability