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.
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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.
Truncated Singular Value Decomposition (tSVD),
Principal Component Analysis (PCA),
Density-based spatial clustering of applications with noise (DBSCAN),
K-Nearest Neighbors (Requires Faiss installation to use),
Linear Regression (Ordinary Least Squares),
More Kalman Filter versions,
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
conda options, along docker containers. They will be available in the coming weeks.
cuML can be installed using the
rapidsai conda channel:
conda install -c nvidia -c rapidsai -c conda-forge -c pytorch -c defaults cuml=0.5
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
You also need to ensure
libopenblas are installed:
apt install libopenblas-base libomp-dev
Note: There is no faiss-gpu package installable by pip, so the KNN algorithm will not work unless you install Faiss manually or via conda (see below).
Dependencies for Installing/Building from Source:
To install cuML from source, ensure the dependencies are met:
- cuDF (>=0.5.0)
- zlib Provided by zlib1g-dev in Ubuntu 16.04
- cmake (>= 3.12.4)
- CUDA (>= 9.2)
- Cython (>= 0.29)
- gcc (>=5.4.0)
- faiss-gpu (>=1.4.0) - faiss-gpu is required to run the KNN algorithm.
- For using KNN with CUDA 9.2 or CUDA 10.0, faiss-gpu can be installed using conda:
# cuda 9.2 conda install -c pytorch faiss-gpu cuda92 # cuda 10.0 conda install -c pytorch faiss-gpu cuda100
Installing from Source:
Once dependencies are present, follow the steps below:
- Clone the repository.
$ git clone --recurse-submodules https://github.com/rapidsai/cuml.git
- Build and install
libcuml(the C++/CUDA library containing the cuML algorithms), starting from the repository root folder:
$ cd cuML $ mkdir build $ cd build $ cmake ..
Note: if using a conda environment (recommended currently), then cmake can be configured appropriately via:
$ cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX
$ make -j $ make install
To run tests (optional):
If you want a list of the available tests:
$ ./ml_test --gtest_list_tests
- Build the
$ cd ../../python $ python setup.py build_ext --inplace
To run Python tests (optional):
$ py.test -v
If you want a list of the available tests:
$ py.test cuML/test --collect-only
- Finally, install the Python package to your Python path:
$ python setup.py install
Demo notebooks for the cuML Python algorithms can be found in the rapidsai/notebooks repository on Github.
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:
Please use issues and pull requests to report bugs and add 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.