Package: cuda.ml 0.3.2.9000
Daniel Falbel
cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries
R interface for RAPIDS cuML (<https://github.com/rapidsai/cuml>), a suite of GPU-accelerated machine learning libraries powered by CUDA (<https://en.wikipedia.org/wiki/CUDA>).
Authors:
cuda.ml_0.3.2.9000.tar.gz
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cuda.ml.pdf |cuda.ml.html✨
cuda.ml/json (API)
| # Install 'cuda.ml' in R: |
| install.packages('cuda.ml', repos = c('https://mlverse.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mlverse/cuda.ml/issues
Last updated 3 years agofrom:54fc9575e2. Checks:OK: 3 NOTE: 3. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 01 2024 |
| R-4.5-linux-x86_64 | NOTE | Sep 01 2024 |
| R-4.4-mac-x86_64 | NOTE | Sep 01 2024 |
| R-4.4-mac-aarch64 | NOTE | Sep 01 2024 |
| R-4.3-mac-x86_64 | OK | Sep 01 2024 |
| R-4.3-mac-aarch64 | OK | Sep 01 2024 |
Exports:cuda_ml_agglomerative_clusteringcuda_ml_can_predict_class_probabilitiescuda_ml_dbscancuda_ml_elastic_netcuda_ml_fil_enabledcuda_ml_fil_load_modelcuda_ml_inverse_transformcuda_ml_is_classifiercuda_ml_kmeanscuda_ml_knncuda_ml_knn_algo_ivfflatcuda_ml_knn_algo_ivfpqcuda_ml_knn_algo_ivfsqcuda_ml_lassocuda_ml_logistic_regcuda_ml_olscuda_ml_pcacuda_ml_rand_forestcuda_ml_rand_projcuda_ml_ridgecuda_ml_serialisecuda_ml_serializecuda_ml_sgdcuda_ml_svmcuda_ml_transformcuda_ml_tsnecuda_ml_tsvdcuda_ml_umapcuda_ml_unserialisecuda_ml_unserializecuML_major_versioncuML_minor_versionhas_cuML
Dependencies:clicodetoolscolorspacecpp11dplyrellipsisfansifarvergenericsggplot2globalsgluegtablehardhatisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmeparsnippillarpkgconfigprettyunitspurrrR6RColorBrewerRcpprlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Perform Single-Linkage Agglomerative Clustering. | cuda_ml_agglomerative_clustering |
| Determine whether a CuML model can predict class probabilities. | cuda_ml_can_predict_class_probabilities |
| Run the DBSCAN clustering algorithm. | cuda_ml_dbscan |
| Train a linear model using elastic regression. | cuda_ml_elastic_net cuda_ml_elastic_net.data.frame cuda_ml_elastic_net.default cuda_ml_elastic_net.formula cuda_ml_elastic_net.matrix cuda_ml_elastic_net.recipe |
| Determine whether Forest Inference Library (FIL) functionalities are enabled in the current installation of cuda.ml. | cuda_ml_fil_enabled |
| Load a XGBoost or LightGBM model file. | cuda_ml_fil_load_model |
| Apply the inverse transformation defined by a trained cuML model. | cuda_ml_inverse_transform |
| Determine whether a CuML model is a classifier. | cuda_ml_is_classifier |
| Run the K means clustering algorithm. | cuda_ml_kmeans |
| Build a KNN model. | cuda_ml_knn cuda_ml_knn.data.frame cuda_ml_knn.default cuda_ml_knn.formula cuda_ml_knn.matrix cuda_ml_knn.recipe |
| Build a specification for the "ivfflat" KNN query algorithm. | cuda_ml_knn_algo_ivfflat |
| Build a specification for the "ivfpq" KNN query algorithm. | cuda_ml_knn_algo_ivfpq |
| Build a specification for the "ivfsq" KNN query algorithm. | cuda_ml_knn_algo_ivfsq |
| Train a linear model using LASSO regression. | cuda_ml_lasso cuda_ml_lasso.data.frame cuda_ml_lasso.default cuda_ml_lasso.formula cuda_ml_lasso.matrix cuda_ml_lasso.recipe |
| Train a logistic regression model. | cuda_ml_logistic_reg cuda_ml_logistic_reg.data.frame cuda_ml_logistic_reg.default cuda_ml_logistic_reg.formula cuda_ml_logistic_reg.matrix cuda_ml_logistic_reg.recipe |
| Train a OLS model. | cuda_ml_ols cuda_ml_ols.data.frame cuda_ml_ols.default cuda_ml_ols.formula cuda_ml_ols.matrix cuda_ml_ols.recipe |
| Perform principal component analysis. | cuda_ml_pca |
| Train a random forest model. | cuda_ml_rand_forest cuda_ml_rand_forest.data.frame cuda_ml_rand_forest.default cuda_ml_rand_forest.formula cuda_ml_rand_forest.matrix cuda_ml_rand_forest.recipe |
| Random projection for dimensionality reduction. | cuda_ml_rand_proj |
| Train a linear model using ridge regression. | cuda_ml_ridge cuda_ml_ridge.data.frame cuda_ml_ridge.default cuda_ml_ridge.formula cuda_ml_ridge.matrix cuda_ml_ridge.recipe |
| Serialize a CuML model | cuda_ml_serialise cuda_ml_serialize |
| Train a MBSGD linear model. | cuda_ml_sgd cuda_ml_sgd.data.frame cuda_ml_sgd.default cuda_ml_sgd.formula cuda_ml_sgd.matrix cuda_ml_sgd.recipe |
| Train a SVM model. | cuda_ml_svm cuda_ml_svm.data.frame cuda_ml_svm.default cuda_ml_svm.formula cuda_ml_svm.matrix cuda_ml_svm.recipe |
| Transform data using a trained cuML model. | cuda_ml_transform |
| t-distributed Stochastic Neighbor Embedding. | cuda_ml_tsne |
| Truncated SVD. | cuda_ml_tsvd |
| Uniform Manifold Approximation and Projection (UMAP) for dimension reduction. | cuda_ml_umap |
| Unserialize a CuML model state | cuda_ml_unserialise cuda_ml_unserialize |
| cuda.ml | cuda.ml |
| Get the major version of the RAPIDS cuML shared library cuda.ml was linked to. | cuML_major_version |
| Get the minor version of the RAPIDS cuML shared library cuda.ml was linked to. | cuML_minor_version |
| Determine whether cuda.ml was linked to a valid version of the RAPIDS cuML shared library. | has_cuML |
| Make predictions on new data points. | predict.cuda_ml_fil |
| Make predictions on new data points. | predict.cuda_ml_knn |
| Make predictions on new data points. | predict.cuda_ml_linear_model |
| Make predictions on new data points. | predict.cuda_ml_logistic_reg |
| Make predictions on new data points. | predict.cuda_ml_rand_forest |
| Make predictions on new data points. | predict.cuda_ml_svm |
