Sravan Avvaru
a615306f39
Closes #3704 ## Problem Transforms can fail on bad data (e.g. nulls/NaNs from incomplete user surveys). Today any transform exception aborts iteration, and there is no way to skip invalid rows during loading. ## Solution New `on_transform_error` parameter on `StreamingDataset`: - `"raise"` (default, matches current behavior and the convention in tf.data / WebDataset / Ray Data) - `"skip"` — drop the failing rows and continue - `"warn"` — like skip, plus a logged warning per failing batch - a WebDataset-style callable `handler(exc) -> bool`, so users can skip only expected error types Key design points: - **Row-granular skipping**: when a batch fails, the transform is re-run on single-row slices so only the rows that actually fail are dropped (avoids Ray-style whole-block loss). Skips are counted in a new `rows_skipped` property. - **No crash on uneven skips**: the round-robin loop now ends the epoch at the last cycle where every split still has a row, instead of hitting `IndexError` when a split runs dry early. - **Exact resumability under skips**: checkpoints are now position-based. `state_dict` gains `positions_consumed_per_split` (exact for owned splits), and a new `merge_state_dicts` static method combines per-rank states via elementwise max for elastic resume across topology changes. Old checkpoints without the new key still load. Positions equal sample counts when nothing is skipped, so existing behavior is unchanged. - **Guardrail**: transforms returning the wrong number of rows now raise a clear `ValueError` instead of silently corrupting split accounting. ### Answers to the issue's open questions - *Can we do this?* Yes — all transforms funnel through one guarded call in the Stage 2 pipeline. - *What do other libraries do?* tf.data `ignore_errors()`, WebDataset `handler=`, Ray `max_errored_blocks`; MosaicML StreamingDataset offers nothing (skipping conflicts with its determinism model). This design follows the common conventions: raise by default, opt-in skipping, count/log drops. - *Error handling or pre-filtering?* Both: the existing `filter=` remains the recommended tool for predictable bad data (splits are built post-filter, so all guarantees hold — now documented); `on_transform_error` covers failures not expressible as a predicate. - *Impact on splits / elastic determinism?* Per-split sample sequences stay deterministic (skips are data-dependent, not topology-dependent). With unequal bad-row counts across splits the last few global steps of an epoch can differ across topologies (bounded by the skew), which is documented on the parameter. With equal counts per split, full determinism is preserved — covered by a test. ## Testing 15 new tests in `test_elastic_dataloader.py` covering: default raise, invalid values, uniform and uneven skips (including epoch-end truncation), warn logging, selective callable handlers, wrong-row-count guardrail, determinism across runs and across world sizes (1/2/3/4) with skips, exact mid-epoch resume with skips on the same topology, elastic resume via `merge_state_dicts` (ws=2 → ws=1), merge validation, and backward-compat loading of old checkpoints. Note: relying on CI for the test run — my local machine OOMs during the final link of the native extension. The change itself is pure Python. --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
LanceDB Python SDK
A Python library for LanceDB.
Installation
pip install lancedb
Pre-Haswell x86_64 hosts: lancedb-compat
The default lancedb wheel targets x86-64-haswell (AVX2 + FMA + F16C) for full performance on modern hardware. Pre-Haswell hosts — Intel Sandy Bridge / Ivy Bridge / Westmere; AMD Bulldozer / Piledriver / Steamroller — don't have AVX2 and crash with Illegal instruction at import lancedb.
For those hosts, install the lancedb-compat package instead:
pip install lancedb-compat
Same Python API (import lancedb works as usual). The compat wheel is compiled at the x86-64-v2 baseline (Nehalem-class) and uses runtime SIMD dispatch in the embedded lance crate to pick the right kernel tier (scalar / AVX / AVX+FMA / AVX2+FMA / AVX-512) at load time, so it still goes fast on modern hardware while running cleanly on the pre-Haswell silicon. Use lance.simd_info() from Python to verify which tier was selected.
lancedb and lancedb-compat install to the same lancedb/ namespace and conflict at install time. Pick one. To switch, pip uninstall lancedb first, then pip install lancedb-compat (or vice-versa).
If you need a custom baseline (or lancedb-compat isn't yet published for your platform), build from source with the override:
RUSTFLAGS="-C target-cpu=x86-64-v2" maturin build --release
pip install ./target/wheels/lancedb-*.whl
Preview Releases
Stable releases are created about every 2 weeks. For the latest features and bug fixes, you can install the preview release. These releases receive the same level of testing as stable releases, but are not guaranteed to be available for more than 6 months after they are released. Once your application is stable, we recommend switching to stable releases.
pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
Usage
Basic Example
import lancedb
db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
table = db.open_table('my_table')
results = table.search([0.1, 0.3]).limit(20).to_list()
print(results)
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.