Lei XuandClaude Sonnet 5.5
bedb6ab3b1
## Summary - Rework `docs/web/training/torch.mdx` for people who already know the PyTorch `DataLoader`, built around `lancedb.streaming.StreamingDataset`. - The quickstart trains a state-to-action policy on the real `lance-format/lerobot-xvla-soft-fold` dataset loaded via `hf://`, using a zero-copy Arrow-to-tensor transform. - Add a short table mapping PyTorch idioms (`IterableDataset`, `DistributedSampler`, `collate_fn`, `random_split`, `StatefulDataLoader`) to their LanceDB equivalents. - Add an Optimizations section: Transform (including an `ExtractFrames` example using blob v2 and torchcodec), Distributed Training, and the existing Output Formats content moved underneath. ## Notes - The quickstart depends on #4602 (permutation building over tables with schema metadata), which is merged. - `ExtractFrames` takes a URI and opens the table lazily because a local `LanceTable` cannot yet be pickled into DataLoader workers. - The Permutation-based "Selecting columns" and "Using multiple DataLoader workers" sections further down are unchanged from main. ## Test plan - [x] Ran the quickstart, transform, `ExtractFrames` (main process and forkserver workers) and distributed snippets locally. - [x] Rendered the page with `mint dev` and checked for console errors. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Sonnet 5.5 <noreply@anthropic.com>
The Multimodal Lakehouse for AI
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
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Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
Join Us and Contribute
We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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