v23.04.0 release

This release includes:

• Getting started guide and benchmarking scripts on GCP dataproc
• Getting started guide on AWS EMR
• cpu method to convert Spark RAPIDS ML generated models to Spark ML models
• Eliminating the need for CUDA on the driver node
• Example notebook for k-NN
• Spark 3.4 compatibility
• Updating RAPIDS dependencies to 23.04

pip package available at https://pypi.org/project/spark-rapids-ml/23.4.0/

v23.02.0 release

Added GPU-accelerated PySpark-compatible APIs for the following algorithms:

• K-Means
• k-NN
• LinearRegression
• PCA
• RandomForestClassifier
• RandomForestRegressor

Pip package: https://pypi.org/project/spark-rapids-ml/

v22.02.0 release

New functionality and performance improvements for this release include:

• Refactor PCA training to leverage spark-rapids plugin.
• Move SVD computation from Driver to Executor.
• Optimize PCA API.
• Fixed a bug when training on large dataset.

v21.10.0 release

New functionality and performance improvements for this release include:

• Leverage spark-rapids plugin to speed up the PCA transform process
• Link some CUDA libraries statically to avoid multiple jars for different environment

v21.10.0 release

Tag for release version v21.10.0