pylance-cuvs

pylance-cuvs provides cuVS-backed IVF_PQ training and artifact building for Lance datasets.

The current release line is 0.1.0b2.

It covers one narrow part of the indexing pipeline:

1. Train an IVF_PQ model with cuVS.
2. Build a partition-local artifact from a Lance dataset.
3. Pass the training output and artifact back to Lance for finalization.

It does not create or register a Lance index on your behalf.

Requirements

• Linux
• Python 3.12+
• CUDA 12 runtime available on the machine
• cuVS runtime libcuvs-cu12==26.2.0
• A matching backend package: pylance-cuvs-cu12
• A Lance build at commit 6112a34bfe38618f07c099217dc3d89fd39ca6bb or a descendant that preserves the same vector-build APIs

Installation

Install the loader, the matching backend package, and the cuVS runtime into the same Python environment:

• pylance-cuvs
• pylance-cuvs-cu12
• libcuvs-cu12==26.2.0

Published beta releases are wheel-only. Source distributions are not supported.

If you are working from this repository, the shortest local setup is:

just sync-dev
just backend-develop

For Lance-side integration, pin Lance to commit 6112a34bfe38618f07c099217dc3d89fd39ca6bb first, then validate against this beta.

The loader chooses the backend from the installed cuVS runtime package. You can override detection when needed:

export LANCE_CUVS_BACKEND=cu12

Legacy overrides such as cuvs-26-02 are still accepted for compatibility.

Quick Start

from pathlib import Path

import lance
import lance_cuvs
import pyarrow as pa


def vector_array(rows: int, dim: int) -> pa.FixedSizeListArray:
    values = pa.array(
        [row + axis / 100.0 for row in range(rows) for axis in range(dim)],
        type=pa.float32(),
    )
    return pa.FixedSizeListArray.from_arrays(values, dim)


dataset_uri = Path("/tmp/example.lance")
artifact_uri = Path("/tmp/example-artifact")

table = pa.table({"vector": vector_array(rows=4096, dim=16)})
lance.write_dataset(table, dataset_uri)

training = lance_cuvs.train_ivf_pq(
    dataset_uri,
    "vector",
    metric_type="L2",
    num_partitions=8,
    num_sub_vectors=4,
    sample_rate=4,
    max_iters=20,
)

artifact = lance_cuvs.build_ivf_pq_artifact(
    dataset_uri,
    "vector",
    training=training,
    artifact_uri=artifact_uri,
)

print(type(training.ivf_centroids()))
print(type(training.pq_codebook()))
print(artifact.artifact_uri)
print(artifact.files)

Python API

lance_cuvs.train_ivf_pq(...)

Trains an IVF_PQ model with cuVS and returns IvfPqTrainingOutput.

Key fields and methods:

• num_partitions
• num_sub_vectors
• num_bits
• metric_type
• ivf_centroids() -> pyarrow.FixedSizeListArray
• pq_codebook() -> pyarrow.FixedSizeListArray

lance_cuvs.build_ivf_pq_artifact(...)

Builds a partition-local artifact from a Lance dataset and a previously trained model, then returns IvfPqArtifactOutput.

Key fields:

• artifact_uri
• files

artifact_uri must resolve to the local filesystem.

Scope

Use pylance-cuvs when you want cuVS to do the expensive GPU-side training and encoding work, but you still want Lance to own index finalization.

Do not expect this package to:

• finalize an index
• register an index in a dataset
• provide a generic Lance wrapper beyond IVF_PQ training and artifact build

Development

List available tasks with:

just

Run commands inside the shared development container with:

just container-shell

Use --platform linux/amd64 only when you explicitly want to match the GitHub-hosted runner architecture:

tools/run_in_container.sh --platform linux/amd64 -- bash

Run the CI-equivalent CPU build locally with:

just container-python-build

Run the Rust quality gates locally with:

just container-rust-build

Build all release wheels locally with:

just container-python-release

Create the root development environment with:

just sync-dev

Run loader-only tests with:

just loader-test

Build the root and backend wheels with:

just build-wheels

Test the published root wheel in a clean environment with:

just test-loader-wheel

Test the published wheels on a GPU-capable machine with:

just test-gpu-wheel

Build only the backend wheel with:

just backend-wheel

Build the cu12 backend in-place with:

just backend-develop

Run the Rust formatting check with:

just rust-fmt-check

Run Clippy with the Python bindings enabled:

just rust-clippy

Run cargo check with the Python bindings enabled:

just rust-check

Run the release-style GPU smoke end to end with:

just gpu-smoke

Repository Layout

• python/lance_cuvs
  • root loader package
• backends/cuvs_26_02
  • cuVS 26.02 source tree for the CUDA 12 backend package
• backends/cuvs_26_02/src/backend.rs
  • Lance-facing orchestration
• backends/cuvs_26_02/src/cuda.rs
  • CUDA / cuVS wrappers and tensor helpers
• backends/cuvs_26_02/src/python.rs
  • PyO3 bindings

License

Apache License 2.0. See LICENSE.