Deep Lake Docs

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Activeloop Deep Lake

Deep Lake as a Vector Store

• Store embeddings and their metadata including text, jsons, images, audio, video, and more. Save the data locally, in your cloud, or on Deep Lake storage.

• Perform hybrid search including embeddings and their attributes.

• Build LLM Apps using or integrations with LangChain and LlamaIndex

• Run computations on the client-side, on our Managed Tensor Database, or on a serverless deployment in your VPC.

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.

To start using Deep Lake ASAP, check out our Quickstart, Getting Started Guide, Tutorials, and Playbooks.

In addition to Deep Lake docs, you may also check out Deep Lake's GitHub repository and give us a ⭐ if you like the project!

Join Deep Lake's Slack Community if you need help or have suggestions for improving documentation!

Deep Lake Docs Overview

Vector Store QuickstartDeep Learning QuickstartStorage & CredentialsGetting StartedTutorials (w Colab)PlaybooksDataset VisualizationBest PracticesLow-Level API Summary