v3.6.3

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

v3.6.3

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

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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Getting Started

Deep Learning

The comprehensive guide for Deep Lake in Deep Learning applications.

PreviousStep 4: Customizing Vector Stores NextStep 1: Hello World

Last updated 1 year ago

This Deep Learning Getting Started guide is available as a Colab Notebook

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