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cuML Build From Source Guide

Setting Up Your Build Environment

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

  1. cuDF (>=0.7)
  2. zlib
  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. Note that the blas has to be installed to the same folder system as cmake, for example if using conda installed cmake, the blas implementation should also be installed in the conda environment.

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
$ export CUDA_BIN_PATH=$CUDA_HOME # (optional env variable if cuda binary is not in the PATH. Default CUDA_HOME=/path/to/cuda/)
$ cmake ..

If using a conda environment (recommended), 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.

If using conda and a conda installed cmake, the openblas conda package is recommended and can be explicitly specified for blas and lapack:

cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -DBLAS_LIBRARIES=$CONDA_PREFIX/lib/libopenblas.so -DLAPACK_LIBRARIES=$CONDA_PREFIX/lib/libopenblas.so

Additionally, to reduce compile times, you can specify a GPU compute capability to compile for, for example for Volta GPUs:

$ cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -DGPU_ARCHS="70"
  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
  1. You can also build and run tests for the machine learning primitive header only library located in the ml-prims folder. From the repository root:
$ cd ml-prims
$ mkdir build
$ cd build
$ cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -DGPU_ARCHS="70" # specifying GPU_ARCH is optional, but significantly reduces compile time
$ make -j

To run the ml-prim tests:

$./test/mlcommon_test