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Dependencies for Installing/Building from Source:

To install cuML from source, ensure the dependencies are met:

  1. cuDF (>=0.5.1)
  2. zlib Provided by zlib1g-dev in Ubuntu 16.04
  3. cmake (>= 3.12.4)
  4. CUDA (>= 9.2)
  5. Cython (>= 0.29)
  6. gcc (>=5.4.0)
  7. BLAS - Any BLAS compatible with Cmake's FindBLAS

Installing from Source:

Once dependencies are present, follow the steps below:

  1. Clone the repository.
$ git clone --recurse-submodules https://github.com/rapidsai/cuml.git
  1. 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 ..

If using a conda environment (recommended currently), then cmake can be configured appropriately via:

$ cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX

Note: The following warning message is dependent upon the version of cmake and the CMAKE_INSTALL_PREFIX used. If this warning is displayed, the build should still run succesfully. We are currently working to resolve this open issue. You can silence this warning by adding -DCMAKE_IGNORE_PATH=$CONDA_PREFIX/lib to your cmake command.

Cannot generate a safe runtime search path for target ml_test because files
in some directories may conflict with libraries in implicit directories:

The configuration script will print the BLAS found on the search path. If the version found does not match the version intended, use the flag -DBLAS_LIBRARIES=/path/to/blas.so with the cmake command to force your own version.

  1. Build libcuml:
$ make -j
$ make install

To run tests (optional):

$ ./ml_test

If you want a list of the available tests:

$ ./ml_test --gtest_list_tests
  1. Build the cuml python package:
$ 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
  1. Finally, install the Python package to your Python path:
$ python setup.py install

cuML's core structure contains:

  1. cuML: C++/CUDA machine learning algorithms. This library currently includes the following six algorithms:
  • Single GPU Truncated Singular Value Decomposition (tSVD)
  • Single GPU Principal Component Analysis (PCA)
  • Single GPU Density-based Spatial Clustering of Applications with Noise (DBSCAN)
  • Single GPU Kalman Filtering
  • Multi-GPU K-Means Clustering
  • Multi-GPU K-Nearest Neighbors (Uses Faiss)

  1. python: Python bindings for the above algorithms, including interfaces for cuDF. These bindings connect the data to C++/CUDA based cuML and ml-prims libraries without leaving GPU memory.

  2. ml-prims: Low level machine learning primitives header only library, used in cuML algorithms. Includes:

  • Linear Algebra
  • Statistics
  • Basic Matrix Operations
  • Distance Functions
  • Random Number Generation

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: