cuML

Machine learning is a fundamental capability of RAPIDS. cuML is a suite of libraries that implements a machine learning algorithms within the RAPIDS data science ecosystem. cuML enables data scientists, researchers, and software engineers to run traditional ML tasks on GPUs without going into the details of CUDA programming.

The cuML repository contains:

python: Python based GPU Dataframe (GDF) machine learning package that takes cuDF dataframes as input. cuML connects the data to C++/CUDA based cuML and ml-prims libraries without ever leaving GPU memory.
cuML: C++/CUDA machine learning algorithms. This library currently includes the following six algorithms; a) Single GPU Truncated Singular Value Decomposition (tSVD), b) Single GPU Principal Component Analysis (PCA), c) Single GPU Density-based Spatial Clustering of Applications with Noise (DBSCAN), d) Single GPU Kalman Filtering, e) Multi-GPU K-Means Clustering, f) Multi-GPU K-Nearest Neighbors (Uses Faiss).
ml-prims: Low level machine learning primitives used in cuML. ml-prims is comprised of the following components; a) Linear Algebra, b) Statistics, c) Basic Matrix Operations, d) Distance Functions, e) Random Number Generation.

Available Algorithms:

Truncated Singular Value Decomposition (tSVD).
Principal Component Analysis (PCA).
Density-based spatial clustering of applications with noise (DBSCAN).
K-Means Clustering.
K-Nearest Neighbors (Requires Faiss installation to use).

Upcoming algorithms:

Kalman Filter.

More ML algorithms in cuML and more ML primitives in ml-prims are being added currently. Example notebooks are provided in the python folder to test the functionality and performance. Goals for future versions include more algorithms and multi-gpu versions of the algorithms and primitives.

The installation option provided currently consists on building from source. Upcoming versions will add pip and conda options, along docker containers. They will be available in the coming weeks.

Setup

Dependencies

To use cuML, it must be cloned and built in an environment that already has the dependencies, including cuDF and its dependencies.

List of dependencies:

cuDF (>=0.2.0)
zlib
cmake (>= 3.8, version 3.11.4 is recommended and there are issues with version 3.12)
CUDA (>= 9.2)
Cython (>= 0.28)
gcc (>=5.4.0)
faiss-gpu (>=1.4.0) - To install with conda: conda install -c pytorch faiss-gpu cuda92

Setup steps

To clone:

git clone --recurse-submodules https://github.com/rapidsai/cuml.git

To build the python package, in the repository root folder:

python setup.py install

Building CuML:

Running tests

To test the C++ algorithms using googletests, in the repository root folder:

$ cd cuML
$ mkdir build
$ cd build
$ cmake ..
$ make -j
$ ./ml_test

Python Notebooks

Demo notebooks can be found in python/notebooks folder.

External

The external folders contains submodules that this project in-turn depends on. Appropriate location flags will be automatically populated in the main CMakeLists.txt file for these.

Current external submodules are:

Contributing

Please use issues and pull requests to report bugs and add functionality.

Contact

Find out more details on the RAPIDS site

ayushdg/cuml