Deep Lake (formerly known as Activeloop Hub) is a data lake for deep learning applications. Our open-source dataset format is optimized for rapid streaming and querying of data while training models at scale, and it includes a simple API for creating, storing, and collaborating on AI datasets of any size. It can be deployed locally or in the cloud, and it enables you to store all of your data in one place, ranging from simple annotations to large videos. Deep Lake is used by Google, Waymo, Red Cross, Omdena, Yale, & Oxford. Use one API to upload, download, and stream datasets to/from AWS S3/S3-compatible storage, GCP, Activeloop cloud, or local storage. Store images, audios and videos in their native compression. Deeplake automatically decompresses them to raw data only when needed, e.g., when training a model. Treat your cloud datasets as if they are a collection of NumPy arrays in your system's memory. Slice them, index them, or iterate through them.

Features

  • Storage Agnostic API
  • Native Compression
  • Lazy NumPy-like Indexing
  • Dataset Version Control
  • Dataloaders for Popular Deep Learning Frameworks
  • 100+ most-popular image, video, and audio datasets available in seconds

Project Samples

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License

Mozilla Public License 1.0 (MPL)

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Additional Project Details

Programming Language

Python

Related Categories

Python Large Language Models (LLM), Python Generative AI

Registered

2023-03-22