deeplake

What it is and what it does

Deep Lake is a serverless, multi-cloud database optimized for storing and querying AI datasets. It handles raw data types—images, videos, audio, text, embeddings, PDFs, and more—in a single unified storage layer, with built-in vector search for retrieval-augmented generation and LLM applications. Unlike traditional vector databases that store only embeddings plus light metadata, Deep Lake stores the raw data itself in native compression, enabling lazy loading and efficient streaming to training pipelines.

The package integrates with popular ML frameworks and tools to simplify deployment of LLM-based products and deep learning workflows. It supports multi-cloud backends (S3, GCP, Azure, Activeloop cloud, local, in-memory) and includes dataset versioning, visualization, and dataloaders for common frameworks. Core dependencies are numpy, deepframe, and requests.

Use it for

• Build RAG pipelines and LLM applications with vector search over embeddings and raw documents stored in one place.
• Manage and version large datasets for training deep learning models with efficient streaming and lazy loading.
• Store and search multi-modal datasets (images, videos, text) with instant visualization in the Deep Lake App.
• Deploy serverless vector stores locally or in your own cloud without managing infrastructure.
• Fine-tune large language models using Deep Lake's performant dataloaders for PyTorch or TensorFlow.

Worth the install?

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Install

deeplake on PyPI

Before you install

Medium install friction with wheels available for multiple Python versions on macOS ARM64 and Linux x86_64/aarch64. Active maintenance with a recent release and 9224 repository stars indicate ongoing development, though Python version requirements are not explicitly specified in package metadata.

License terms are unclear in package metadata; verify in the repository before production use. Python version support is unspecified.

License in practice

License treatment is unclear—no SPDX identifier or raw license text is available in the package metadata, so users cannot verify licensing terms before installation. This should be resolved by consulting the repository directly.

Quickstart

pip install deeplake

import deeplake
ds = deeplake.dataset('path/to/dataset')
ds.create_tensor('images')
ds.images.append(image_array)

Package facts

Evidence: deeplake-4.7.1-cp310-cp310-macosx_11_0_arm64.whl; deeplake-4.7.1-cp310-cp310-manylinux2014_aarch64.whl; deeplake-4.7.1-cp310-cp310-manylinux2014_x86_64.whl; deeplake-4.7.1-cp311-cp311-macosx_11_0_arm64.whl; deeplake-4.7.1-cp311-cp311-manylinux2014_aarch64.whl; deeplake-4.7.1-cp311-cp311-manylinux2014_x86_64.whl; deeplake-4.7.1-cp312-cp312-macosx_11_0_arm64.whl; deeplake-4.7.1-cp312-cp312-manylinux2014_aarch64.whl; deeplake-4.7.1-cp312-cp312-manylinux2014_x86_64.whl; deeplake-4.7.1-cp313-cp313-macosx_11_0_arm64.whl; deeplake-4.7.1-cp313-cp313-manylinux2014_aarch64.whl; deeplake-4.7.1-cp313-cp313-manylinux2014_x86_64.whl; deeplake-4.7.1-cp39-cp39-macosx_11_0_arm64.whl; deeplake-4.7.1-cp39-cp39-manylinux2014_aarch64.whl; deeplake-4.7.1-cp39-cp39-manylinux2014_x86_64.whl

Tags

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