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We hope you enjoy Docs for Deep Lake. |
- Store and search embeddings and their metadata including text, jsons, images, audio, video, and more. Save the data locally, in your cloud, or on Deep Lake storage.
- Build LLM Apps using or integrations with LangChain and LlamaIndex
- Run computations locally or on our Managed Tensor Database
- 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 Architecture for Inference and Model Development Applications.
To start using Deep Lake ASAP, check out our Vector Store Quickstart, Deep Learning Quickstart, Getting Started Guides, Tutorials, and Playbooks.
Please check out Deep Lake's GitHub repository and give us a ⭐ if you like the project.
Join our Slack Community if you need help or have suggestions for improving documentation!
{% content-ref url="quickstart.md" %} quickstart.md {% endcontent-ref %}
{% content-ref url="quickstart-dl.md" %} quickstart-dl.md {% endcontent-ref %}
{% content-ref url="storage-and-credentials/" %} storage-and-credentials {% endcontent-ref %}
{% content-ref url="getting-started/" %} getting-started {% endcontent-ref %}
{% content-ref url="tutorials/" %} tutorials {% endcontent-ref %}
{% content-ref url="playbooks/" %} playbooks {% endcontent-ref %}
{% content-ref url="technical-details/dataset-visualization.md" %} dataset-visualization.md {% endcontent-ref %}
{% content-ref url="technical-details/best-practices/" %} best-practices {% endcontent-ref %}
{% content-ref url="api-basics.md" %} api-basics.md {% endcontent-ref %}