Changelog

Source: NEWS.md

cuml 0.3.0

Linear Models

  • Added support for OLS, ridge regression, and LASSO regression.

Misc

  • Fixed issue with CUDA architecture string being empty when building {cuda.ml}

  • {cuda.ml} source code was revised to be compatible with libcuml++ version 21.06, 21.08, and 21.10

  • Added support for automatically downloading a pre-built version of libcuml++ and bundling & linking the downloaded libcuml++ with the rest of the {cuda.ml} installation when no pre-existing copy of libcuml++ is found. This is done so that new users can try out {cuda.ml} quickly without having to install Conda or to build libcuml++ from source manually.

cuml 0.2.0

R Interface Improvements

  • Re-wrote R interfaces of all supervised ML algorithms using {hardhat} to support data-frame, matrix, formula, and recipe inputs, per suggestion from @topepo in https://github.com/mlverse/cuml/issues/78 and https://github.com/mlverse/cuml/issues/77.

  • Added {parsnip} bindings for random forest, SVM, and KNN models.

  • Improved warning message for missing linkage to the RAPIDS CuML shared library. If the C++/CUDA source code of this package was not linked with a valid version of the RAPIDS CuML shared library when the package was installed, then a warning will be emitted whenever the package is loaded.

Clustering

  • Added support K-Means initialization options (namely, “kmeans++”, “random”, and “array”) and other configuration parameters for K-Means clustering in cuML.

  • Added ‘cuml_log_level’ option to cuml_dbscan().

  • Implemented R interface for single-linkage agglomerative clustering.

Dimensionality Reduction

  • Implemented R interfaces for Principal Component Analysis (PCA), Truncated Singular Value Decomposition (TSVD), T-distributed Stochastic Neighbor Embedding (T-SNE), Uniform Manifold Approximation and Projection (UMAP), and Random Projection routines in cuML (including inverse transformations from lower-dimensional representation to the original feature space when applicable).

Nonlinear Models for Regression or Classification

  • Added R interface for CuML Forest Inference Library (FIL). Users can load any existing XGBoost or LightGBM model using Treelite and use the model to perform high-throughput batch inference using GPU acceleration provided by FIL.

  • Implemented R interface for K-Nearest Neighbor (KNN) classification and regression.

Concurrency

  • Fixed a missing cudaEventRecord() call in cuml4r::async_copy().

Misc

cuml 0.1.0

Clustering

  • Implemented R interfaces for single-GPU versions of DBSCAN and K-Means clustering algorithms from cuML.

Nonlinear Models for Regression or Classification

  • Implemented R interfaces for cuML Random Forest classification and regression routines.

  • Implemented R interfaces for cuML Support Vector Machine classifier and regressor.

  • Support for SVM multi-class classification was implemented using the one-vs- rest strategy (as SVM classifier from cuML currently only supports binary classifications).

Documentation

  • Included suggestions on how to build and install cuML libraries from source with or without multi-GPU support in https://github.com/yitao-li/cuml-installation-notes. All suggestions are known to be working for RAPIDS cuML version 21.08. Please note the building-from- source option is more for advanced use cases that require customizations of RAPIDS cuML libraries’ build parameters, compilers, etc, and is somewhat time- consuming and not as beginner-friendly as installing cuML directly from Conda.

  • Found and fixed a few typos and inconsistencies.

  • Some examples were simplified.

  • Added documentation for predict() functions per suggestion from @topepo in https://github.com/mlverse/cuml/issues/80.

Misc

  • Configuration script was revised to work with RAPIDS cuML libraries installed via Conda or built from source. If RAPIDS cuML libraries could not be located during the configuration process, then a warning message will be emitted.

  • Improved on the initial prototype of {cuml} by utilizing modern C++ constructs from thrust (https://github.com/NVIDIA/thrust), making the C++ source code of this project more readable and maintainable.

  • Formatted all human-written C++ source code with clang-format and all human- written R source code with styler. Rcpp-generated C++ and R source files will not be formatted.

  • Caching of build artifacts using ccache can be enabled by setting the env variable CUML4R_ENABLE_CCACHE (e.g., one can run R CMD build cuml followed by CUML4R_ENABLE_CCACHE=1 R CMD INSTALL cuml_0.1.0.tar.gz to avoid re- compiling the same artifacts across builds. Notice this feature is intended for {cuml} contributors or advanced users who need to build {cuml} frequently, and is not enabled by default for other users.

  • Some larger cpp files were split into more granular ones for faster build speed (if parallel build is enabled) and also greater maintainability.