Comment on page

Deep Lake Docs

We hope you enjoy Docs for Deep Lake.

Activeloop Deep Lake
Deep Lake as a Vector Store for LLM Applications
Store and search embeddings and their metadata including text, jsons, images, audio, video, and more. Save the data locally, in your cloud, or on Deep Lake storage.
Build LLM Apps using or integrations with LangChain and LlamaIndex
Run computations locally or on our Managed Tensor Database​
Deep Lake as a Data Lake For Deep Learning
Store images, audios, videos, text and their metadata (i.e. annotations) in a data format optimized for Deep Learning. Save the data locally, in your cloud, or on Activeloop storage.
Rapidly train PyTorch and TensorFlow models while streaming data with no boilerplate code.
Run version control, dataset queries, and distributed workloads using a simple Python API.
Deep Lake Architecture for Inference and Model Development Applications.
To start using Deep Lake ASAP, check out our Vector Store Quickstart, Deep Learning Quickstart, Getting Started Guides, Tutorials, and Playbooks.
Please check out Deep Lake's GitHub repository and give us a ⭐ if you like the project. 
Join our Slack Community if you need help or have suggestions for improving documentation!
Deep Lake Docs Overview
Vector Store Quickstart
Deep Learning Quickstart
Storage & Credentials
Getting Started
Tutorials (w Colab)
Playbooks
Dataset Visualization
Best Practices
Low-Level API Summary
​
​

Vector Store Quickstart