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Tobias
7b6ee0d655
Tracks #3324. On x86_64 CPUs without AVX2 (Sandy Bridge / Ivy Bridge / Westmere on Intel; Bulldozer / Piledriver / Steamroller on AMD), `import lancedb` SIGILLs because the wheel bakes AVX2 + FMA into every compiled function. Per [westonpace's review](https://github.com/lancedb/lancedb/issues/3324#issuecomment-4328944354), the default `lancedb` wheel stays fast; pre-Haswell users get a separately-published `lancedb-compat` wheel. ## Summary - Adds a `lancedb-compat` matrix entry to `pypi-publish.yml` that builds with `RUSTFLAGS="-C target-cpu=x86-64-v2"` (Nehalem-class baseline). Same Python API (`import lancedb` works) — files install to the same namespace, so the two wheels conflict at install time and users pick one. Same pattern as `psycopg2` / `psycopg2-binary` and `tensorflow` / `tensorflow-cpu`. - Generalizes `build_linux_wheel` and `upload_wheel` composites with optional `package-name` and `rustflags` inputs (defaults preserve the existing 4 `lancedb` matrix entries verbatim). - Documents the choice in `python/README.md`: `pip install lancedb-compat` for pre-Haswell hosts. The default `.cargo/config.toml` baseline is unchanged. ## Sequencing 1. ~~lance-format/lance#6630 merges → runtime SIMD dispatch lands in lance.~~ **Done — merged.** 2. lancedb's lance dep is bumped to a release that includes it (separate PR / normal cadence). 3. This PR's `lancedb-compat` wheel build path starts producing a wheel that runs on pre-Haswell hardware. **Maintainer setup**: register `lancedb-compat` on PyPI and configure trusted publishing. ## Verified end-to-end on Sandy Bridge Xeon E5-2609 Verification was done locally against a fork-pinned lance dep that includes the runtime dispatch implementation, using the same `RUSTFLAGS="-C target-cpu=x86-64-v2"` flags this PR uses in CI: ``` $ RUSTFLAGS="-C target-cpu=x86-64-v2" maturin build --release $ pip install ./target/wheels/lancedb-*.whl $ python verify.py PASS: import + simd dispatch + table create + vector search all work. ``` Pre-fix on the same CPU (default `pip install lancedb`): `Illegal instruction (core dumped)`. Full reproducer (deps + clone + build + verification): https://gist.github.com/tobocop2/2e341358b55c143527416edfdb1e37df. Fork-internal verification PR with the dep bump and full logs: [`tobocop2/lancedb#2`](https://github.com/tobocop2/lancedb/pull/2). ## Benchmarks — no regressions on modern CPUs from the lance-side change These are the numbers I ran for the lance PR, confirming the runtime dispatch doesn't slow down the default (`target-cpu=haswell`) wheel that existing users install. Criterion, one machine, one session, base → PR, no `RUSTFLAGS` override. Full methodology, null experiments, and logs: [lance-format/lance#6630 benchmark comment](https://github.com/lance-format/lance/pull/6630#issuecomment-4933063394) and the [logs gist](https://gist.github.com/tobocop2/3c6d0f449cbd736aa2501f89a7fe56a2). | benchmark | EPYC 7B13 (`avx2`, `fma`, no `avx512f`) | Xeon Cascade Lake (`avx512f`) | |---|---|---| | `Cosine(f32, scalar)` *(control)* | +0.04% | +0.09% | | `Cosine(f64, scalar)` | −0.34% | −1.94% | | `Cosine(u8, SIMD)` | +2.30% | +3.63% | | `Dot(f16, SIMD)` | −0.58% | +0.61% | | `Dot(f32, SIMD)` | +0.34% | **−6.08%** | | `Dot(f32, arrow_arity)` | +0.02% | −0.00% | | `L2(f32, scalar)` | −0.10% | −0.02% | | `L2(f32, simd)` (dim 1024) | +2.63% | −0.53% | | **`L2(simd,f32x8)` (dim 8)** | **−45.9%** | **−25.1%** | | `L2(u8, SIMD)` | +0.42% | −3.11% | | `NormL2(f32, SIMD)` | −1.02% | −4.17% | | `NormL2(f64, SIMD)` | +3.51% | −0.58% | Nothing regresses beyond the noise floor. Dim 8 — the PQ sub-vector width — improves 25–46%. --- To be transparent: this isn't my domain of expertise and the lance-side implementation is AI-generated. I verified it works end-to-end on the failing hardware. Happy to roll in feedback.
The Multimodal AI Lakehouse
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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⭐ Click here ⭐ to see how fast we're growing!
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 |
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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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