NOTE: For the latest stable README.md ensure you are on the
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.
cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming.
As an example, the following Python snippet loads input and computes DBSCAN clusters, all on GPU:
import cudf from cuml import DBSCAN # Create and populate a GPU DataFrame gdf_float = cudf.DataFrame() gdf_float['0'] = [1.0, 2.0, 5.0] gdf_float['1'] = [4.0, 2.0, 1.0] gdf_float['2'] = [4.0, 2.0, 1.0] # Setup and fit clusters dbscan_float = DBSCAN(eps=1.0, min_samples=1) dbscan_float.fit(gdf_float) print(dbscan_float.labels_)
0 0 1 1 2 2 dtype: int32
- Truncated Singular Value Decomposition (tSVD) - Single GPU, Muti-GPU in Conda CUDA 10 package
- Linear Regression (Ordinary Least Squares) - Single GPU, Muti-GPU in Conda CUDA 10 package
- Principal Component Analysis (PCA) - Single GPU
- Density-based spatial clustering of applications with noise (DBSCAN) - Single GPU
- K-Means Clustering - Multi-GPU
- K-Nearest Neighbors - Multi-GPU (Requires Faiss installation to use)
- Ridge Regression - Single GPU
- Kalman Filter - Single GPU
- Stochastic Gradient Descent for linear regression, logistic regression, and linear svm with L1, L2, and elastic-net penalties.
Algorithms in progress:
- More Kalman Filter versions
- Logistic Regression
More ML algorithms in cuML and more ML primitives in ml-prims are being worked on. Goals for future versions include more algorithms and multi-gpu versions of the algorithms and primitives.
libopenblas are installed, for example via apt:
sudo apt install libopenblas-base libomp-dev
cuML can be installed using the
rapidsai conda channel:
conda install -c nvidia -c rapidsai -c conda-forge -c pytorch -c defaults cuml
cuML can also be installed using pip. Select the package based on your version of CUDA.
# cuda 9.2 pip install cuml-cuda92 # cuda 10.0 pip install cuml-cuda100
Build/Install from Source
See build instructions
Please use GitHub issues and pull requests to report bugs and add or request functionality.
Find out more details on the RAPIDS site
The RAPIDS suite of open source software libraries aim to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.