Deep Lake Docs

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Activeloop Deep Lake

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 as a Multi-Modal Vector Store

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

• Perform hybrid search including embeddings and their attributes.

• Build Apps using our Python API or integrations with LangChain and LlamaIndex

• No deployment necessary. All computations run on the client-side.

  • Managed or self-deployed serverless database is coming soon.

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

QuickstartDataset VisualizationStorage & CredentialsGetting StartedTutorials (w Colab)PlaybooksBest PracticesAPI Summary