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cuML Build From Source Guide

Setting Up Your Build Environment

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

  1. cuDF (>=0.8)
  2. zlib
  3. cmake (>= 3.14)
  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.
  8. clang-format (= 8.0.1) - enforces uniform C++ coding style; required to build cuML from source. The packages clang=8 and clang-tools=8 from the conda-forge channel should be sufficient, if you are on conda. If not using conda, install the right version using your OS package manager.
  9. NCCL (>=2.4)
  10. UCX [optional] (>= 1.7) - enables point-to-point messaging in the cuML standard communicator. This is necessary for many multi-node multi-GPU cuML algorithms to function.

It is recommended to use conda for environment/package management. If doing so, a convenience environment .yml file is located in conda/environments/cuml_dec_cudax.y.yml (replace x.y for your CUDA version). This file contains most of the dependencies mentioned above (notable exceptions are gcc and zlib). To use it, for example to create an environment named cuml_dev for CUDA 10.2 and Python 3.7, you can use the follow command:

conda create -n cuml_dev python=3.7
conda env update -n cuml_dev --file=conda/environments/cuml_dev_cuda10.2.yml

These conda environments are based on the general RAPIDS meta packages that install common dependencies for RAPIDS projects. To install different versions of packages contained in those meta packages after creating the environment, it is recommended to remove those meta packages (without removing the actual packages contained in the environment) with the following command (having the environment active):

conda remove --force rapids-build-env rapids-notebook-env rapids-doc-env

Installing from Source:

Recommended process

As a convenience, a build.sh script is provided which can be used to execute the same build commands above. Note that the libraries will be installed to the location set in $INSTALL_PREFIX if set (i.e. export INSTALL_PREFIX=/install/path), otherwise to $CONDA_PREFIX.

$ ./build.sh                           # build the cuML libraries, tests, and python package, then
                                       # install them to $INSTALL_PREFIX if set, otherwise $CONDA_PREFIX

To build individual components, specify them as arguments to build.sh

$ ./build.sh libcuml                   # build and install the cuML C++ and C-wrapper libraries
$ ./build.sh cuml                      # build and install the cuML python package
$ ./build.sh prims                     # build the ml-prims tests
$ ./build.sh bench                     # build the cuML c++ benchmark
$ ./build.sh prims-bench               # build the ml-prims c++ benchmark

Other build.sh options:

$ ./build.sh clean                     # remove any prior build artifacts and configuration (start over)
$ ./build.sh libcuml -v                # build and install libcuml with verbose output
$ ./build.sh libcuml -g                # build and install libcuml for debug
$ PARALLEL_LEVEL=4 ./build.sh libcuml  # build and install libcuml limiting parallel build jobs to 4 (make -j4)
$ ./build.sh libcuml -n                # build libcuml but do not install
$ ./build.sh prims --allgpuarch        # build the ML prims tests for all supported GPU architectures
$ ./build.sh cuml --singlegpu          # build the cuML python package without MNMG algorithms

To run the C++ unit tests (optional), from the repo root:

$ cd cpp/build
$ ./test/ml # Single GPU algorithm tests
$ ./test/ml_mg # Multi GPU algorithm tests, if --singlegpu was not used
$ ./test/prims # ML Primitive function tests

If you want a list of the available C++ tests:

$ ./test/ml --gtest_list_tests # Single GPU algorithm tests
$ ./test/ml_mg --gtest_list_tests # Multi GPU algorithm tests
$ ./test/prims --gtest_list_tests # ML Primitive function tests

To run all Python tests, including multiGPU algorithms, from the repo root:

$ cd python
$ pytest -v

If only the single GPU algos want to be run, then:

$ pytest --ignore=cuml/test/dask --ignore=cuml/test/test_nccl.py

If you want a list of the available Python tests:

$ pytest cuML/test --collect-only

Manual Process

Once dependencies are present, follow the steps below:

  1. Clone the repository.
$ git clone https://github.com/rapidsai/cuml.git
  1. Build and install libcuml++ (C++/CUDA library containing the cuML algorithms), starting from the repository root folder:
$ cd cpp
$ 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 for libcuml++ 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

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"

There are many options to configure the build process, see the customizing build section.

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

To run tests (optional):

$ ./test/ml # Single GPU algorithm tests
$ ./test/ml_mg # Multi GPU algorithm tests
$ ./test/prims # ML Primitive function tests

If you want a list of the available tests:

$ ./test/ml --gtest_list_tests # Single GPU algorithm tests
$ ./test/ml_mg --gtest_list_tests # Multi GPU algorithm tests
$ ./test/prims --gtest_list_tests # ML Primitive function tests

To run cuML c++ benchmarks (optional):

$ ./bench/sg_benchmark  # Single GPU benchmarks

Refer to --help option to know more on its usage

To run ml-prims C++ benchmarks (optional):

$ ./bench/prims_benchmark  # ml-prims benchmarks

Refer to --help option to know more on its uage

To build doxygen docs for all C/C++ source files

$ make doc
  1. Build the cuml python package:
$ cd ../../python
$ python setup.py build_ext --inplace

To run Python tests (optional):

$ pytest -v

If only the single GPU algos want to be run, then:

$ pytest --ignore=cuml/test/dask --ignore=cuml/test/test_nccl.py

If you want a list of the available tests:

$ pytest cuML/test --collect-only
  1. Finally, install the Python package to your Python path:
$ python setup.py install

Custom Build Options

libcuml & libcuml++

cuML's cmake has the following configurable flags available:

Flag Possible Values Default Value Behavior
BLAS_LIBRARIES path/to/blas_lib "" Optional variable allowing to manually specify location of BLAS library.
BUILD_CUML_CPP_LIBRARY [ON, OFF] ON Enable/disable building libcuml++ shared library. Setting this variable to OFF sets the variables BUILD_CUML_C_LIBRARY, BUILD_CUML_TESTS, BUILD_CUML_MG_TESTS and BUILD_CUML_EXAMPLES to OFF
BUILD_CUML_C_LIBRARY [ON, OFF] ON Enable/disable building libcuml shared library. Setting this variable to ON will set the variable BUILD_CUML_CPP_LIBRARY to ON
BUILD_CUML_STD_COMMS [ON, OFF] ON Enable/disable building cuML NCCL+UCX communicator for running multi-node multi-GPU algorithms. Note that UCX support can also be enabled/disabled (see below). Note that BUILD_CUML_STD_COMMS and BUILD_CUML_MPI_COMMS are not mutually exclusive and can both be installed simultaneously.
WITH_UCX [ON, OFF] OFF Enable/disable UCX support for the standard cuML communicator. Algorithms requiring point-to-point messaging will not work when this is disabled. This has no effect on the MPI communicator.
BUILD_CUML_MPI_COMMS [ON, OFF] OFF Enable/disable building cuML MPI+NCCL communicator for running multi-node multi-GPU C++ tests. Note that BUILD_CUML_STD_COMMS and BUILD_CUML_MPI_COMMS are not mutually exclusive, and can both be installed simultaneously.
BUILD_CUML_TESTS [ON, OFF] ON Enable/disable building cuML algorithm test executable ml_test.
BUILD_CUML_MG_TESTS [ON, OFF] ON Enable/disable building cuML algorithm test executable ml_mg_test.
BUILD_PRIMS_TESTS [ON, OFF] ON Enable/disable building cuML algorithm test executable prims_test.
BUILD_CUML_EXAMPLES [ON, OFF] ON Enable/disable building cuML C++ API usage examples.
BUILD_CUML_BENCH [ON, OFF] ON Enable/disable building of cuML C++ benchark.
BUILD_CUML_PRIMS_BENCH [ON, OFF] ON Enable/disable building of ml-prims C++ benchark.
CMAKE_CXX11_ABI [ON, OFF] ON Enable/disable the GLIBCXX11 ABI
DETECT_CONDA_ENV [ON, OFF] ON Use detection of conda environment for dependencies. If set to ON, and no value for CMAKE_INSTALL_PREFIX is passed, then it'll assign it to $CONDA_PREFIX (to install in the active environment).
DISABLE_OPENMP [ON, OFF] OFF Set to ON to disable OpenMP
GPU_ARCHS List of GPU architectures, semicolon-separated 60;70;75 List of GPU architectures that all artifacts are compiled for.
KERNEL_INFO [ON, OFF] OFF Enable/disable kernel resource usage info in nvcc.
LINE_INFO [ON, OFF] OFF Enable/disable lineinfo in nvcc.
NVTX [ON, OFF] OFF Enable/disable nvtx markers in libcuml++.