Database for
- All AI Data
- Videos
- Text
- Images
- PDFs
- Vectors
- AI
Store anything. Deploy anywhere. Fine-tune your own LLM models.
Loved by devs, trusted by enterprises
Trended #1 in Python
7K+Github Stars
+10%
100+Contributors
+31%
1.8K+Community members
WHAT IS DEEP LAKE?
Not another vector database.
We support all AI data.
Generative AI may be new, but we’ve been building for this day for the past 5 years. Deep Lake is multi-modal, which means we support any AI data - and not just embeddings. Deep Lake combines the power of both Data Lakes & Vector Databases to build, fine-tune, & deploy enterprise-grade LLM solutions, & iteratively improve them over time.
Dive into Deep Lake![Serverless Tensor Query Engine]()
Serverless Tensor Query Engine
Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop
![Visualize & Version Data]()
Visualize & Version Data
Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model
![Stream Data to Training]()
Stream Data to Training
Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained
How Deep Lake fits into your Large Language Model-based stack?
How Deep Lake compares to Pinecone, ChromaDB, or Weaviate?
FEATURES
Multi-modal
Fine-tuning
Deployment
Visualization
Version control
Open-source
Deep Lake
Serverless
Pinecone
Managed Service
Chroma
Self-Hosted
Weaviate
Managed
Self-Hosted
Loved by 100+ data teams and counting
“As the datasets enlarge and become multi-modal, next-gen solutions built specifically to address those use cases, like Deep Lake, will help AI teams deliver models to production faster, and more efficiently.”
CTO – Enterprise Analytics & AI, Head of Strategy – Enterprise & Cloud Group
Intel“Downloading data every time you run an experiment is bound to break you and the training process. Deep Lake's on-the-fly streaming was an excellent choice for us: it was really easy to set up, and it started to bring the value from day one.”
Lead ML Engineer
Ubenwa AI“Just needed to deploy a solution that works - and Activeloop made it simpler to ship our AI app quickly!”
Director, Machine Learning
SDSC“They started out with a vector store integration, so it's flown under the radar, but... @activeloopai's Deep Lake is an intriguing fully-fledged serverless data lake that supports attribute based filtering, multiple distance functions, MMR search.”
CEO & Founder
LangChainAI“Awesome!”
Researcher
MILA QuebecIncredible tool! One of our researchers at National Center for Supercomputing Applications had great success using Deep Lake for multimodal pipelining for self supervised video embeddings. We are now trying to move away from HDF5's as they are too slow, annoying to work with, and just don't have the features we need to pipe efficiently into PyTorch. Exciting!
Researcher
NCSA“A 100x speedup of Tensor Query execution for semantic search and question answering on legal documents. Deep Lake’s minimalistic architecture provided flexibility and light touch installation for our customers without introducing complexity such as adding a microservice. With Deep Lake’s ultrafast data loader, PyTorch was able to natively access the data and distribute it automatically across MPI workers, allowing for highly parallel embedding search.”
CTO
Zero“Davit & team are super responsive & hands-on with onboarding. Highly recommend the tool for managing large & complex datasets.”
Co-Founder
Dream 3D“New models deployed in a matter of days instead of weeks.”
Director, Machine Learning
IntelinAirYour ML projects will never be dead in the water(If you use Deep Lake)
1import deeplake
2from PIL import Image
3
4ds = deeplake.load('hub://activeloop/mnist_train')
5
6# Display an image
7Image.fromarray(ds.images[0].numpy())Visualize, query, version, & stream datasets
Deep Lake datasets are visualized right in your browser or Jupyter notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.
- Rapidly visualize different versions of your data
- Understand your data and improve its quality
- Query, train, & edit datasets with data lineage
- Evaluate model performance
Deep Lake Integrations
LangChain
Deep Lake acts as a VectorStore for LangChain. From chatting with your docs to code understanding, we've got you covered.
Get Started
LlamaIndex
Deep Lake is integrated into Llamaverse in two main ways: as a Vector Index and as a loader.
Get Started
OpenAI
Store the embeddings you compute with OpenAI APIs with Deep Lake. Deep Lake also integrates with GPT-4 to provide the Text to Tensor Query Language feature.
Get StartedDeep Lake is revolutionizing Deep Learning. Dive into it.
Drive revenue growth by shipping AI products faster, saving money by saving on GPUs, increasing data scientists’ focus on core business problems, & eliminating failed ML project risk due to the lack of a solid data foundation.
> pip install deeplake
Dive into
Deep LakeCreate
an accountDeep Lake open source. Join the community
Stay in the loop