🏠Deep Lake Docs

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Activeloop Deep Lake

Use Cases for Deep Lake

Please note this is the documentation for Deep Lake version 3.9.16 and earlier. For Deep Lake 4.0.0 and above, please use the following link. We will be working on transitioning the documentation very soon - stay tuned!

Deep Lake as a Data Lake For Deep Learning

• Store and organize unstructured data (images, audios, nifti, videos, text, metadata, and more) in a versioned data format optimized for Deep Learning performance.

• Rapidly query and visualize your data in order to create optimal training sets.

• Stream training data from your cloud to multiple GPUs, without any copying or bottlenecks.

Deep Lake as a Vector Store for RAG 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 Retrieval Augmented Generation (RAG) Apps using our integrations with LangChain and LlamaIndex

• Run computations locally or on our Managed Tensor Database

Deep Lake Architecture for Inference and Model Development Applications.

To start using Deep Lake ASAP, check out our Deep Learning Quickstart, RAG Quickstart, and Deep Learning 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

User Authentication

Deep Learning Quickstart

RAG Quickstart

Deep Learning Playbooks

Deep Learning Tutorials

Best Practices

API Summary