v24.10.0 release
·
35 commits
to branch-24.12
since this release
Release notes as follows:
- Migrated cuML based ivf-flat and ivf-pq to cuVS and added support for cosine distance.
- Added support for sparse data in UMAP.
- Added support for NNDescent based k-NN graph building for UMAP.
- Updated AWS EMR examples to EMR version 7.3.
- Updated RAPIDS dependencies to 24.10.
- Dropped support for Python 3.9 (transitive from RAPIDS).
- Multiple bug and documentation fixes for data generation, CrossValidator, UMAP, DBScan, KMeans, and approximate k-NN implementations.
- Known issues:
- LogisticRegression hangs on fitting sparse data with all zero features in a GPU
- various CUDA errors when
spark.rapids.ml.uvm.enabledorspark.python.worker.reuseare set totrueand with multiple GPUs per executor. Work around is to set either of those configs tofalsein multiple GPU per exectuor clusters. - error in multi-class RandomForest fit when one GPU does not see all class label values.
- CUDA error when fewer probes than
kinivflat-pqANN algorithm.
pip package available at https://pypi.org/project/spark-rapids-ml/24.10.0/