Package index • cuda.ml

Install and manage the backend

cuda_ml_linear_reg(): Train a regularized linear regression model
cuda_ml_logistic_reg(): Train a logistic or multinomial regression model
cuda_ml_ols(): Train an OLS model.
cuda_ml_ridge(): Train a linear model using ridge regression.
cuda_ml_lasso(): Train a linear model using LASSO regression.
cuda_ml_elastic_net(): Train a linear model using elastic net regression.
cuda_ml_sgd(): Train a linear model using mini-batch stochastic gradient descent.
predict(<cuda_ml_linear_model>): Make predictions on new data points.
predict(<cuda_ml_logistic_reg>): Predict from a logistic or multinomial regression model

cuda_ml_knn(): Build a KNN model.
cuda_ml_knn_algo_ivfflat(): Build a specification for the "ivfflat" KNN query algorithm.
cuda_ml_knn_algo_ivfpq(): Build a specification for the "ivfpq" KNN query algorithm.
cuda_ml_rand_forest(): Train a random forest model
cuda_ml_svm(): Train a SVM model.
predict(<cuda_ml_knn>): Make predictions on new data points.
predict(<cuda_ml_svm>): Make predictions on new data points.

cuda_ml_agglomerative_clustering(): Perform single-linkage agglomerative clustering.
cuda_ml_dbscan(): Run the DBSCAN clustering algorithm.
cuda_ml_kmeans(): Run the k-means clustering algorithm.

cuda_ml_pca(): Perform principal component analysis.
cuda_ml_tsvd(): Truncated SVD.
cuda_ml_umap(): Uniform Manifold Approximation and Projection (UMAP) for dimension reduction.
cuda_ml_tsne(): Perform t-distributed stochastic neighbor embedding.
cuda_ml_transform(): Transform data using a trained cuML model.
cuda_ml_inverse_transform(): Apply the inverse transformation defined by a trained cuML model.

cuda_ml_serialize(): Serialize a cuML model
cuda_ml_unserialize(): Unserialize a cuML model state
bundle(<cuda_ml_model>) bundle(<cuda_ml_nvforest>): Bundle a cuda.ml model
cuda_ml_nvforest_export() cuda_ml_nvforest_import(): Export and import an nvForest checkpoint pair