Activeloop

Deep Lake provides a storage format and API designed for deep‑learning workloads, allowing users to keep images, audio, video, text, embeddings, PDFs, DICOM files and other complex data types together in a single repository. It supports vector search, data streaming, versioning and lineage, and works with datasets of any size while remaining serverless, so data can reside in a cloud bucket or on‑premise storage.

The library includes native compression and lazy NumPy‑style indexing, loading only the portions of data needed for training or querying. Built‑in dataloaders for PyTorch and TensorFlow simplify model pipelines, and the same API can address S3, GCP, Azure, Activeloop cloud, local or in‑memory stores.

Integrations connect Deep Lake to LangChain, LlamaIndex, Weights & Biases, MMDetection and MMSegmentation, enabling it to serve as a vector store for LLM applications and as a dataset backend for computer‑vision tasks. It is used by organizations such as Intel, Bayer Radiology, Matterport, ZERO Systems, Red Cross, Yale and Oxford.