Miscellaneous information / Troubleshooting

Here, you find some more advanced notes and troubleshooting tips related to Set up your development environment.

Notes on OpenMP

Even though the default C compiler on macOS (Apple clang) is confusingly aliased as /usr/bin/gcc, it does not directly support OpenMP.

Note

If OpenMP is not supported by the compiler, the build will be done with OpenMP functionalities disabled. This is not recommended since it will force some estimators to run in sequential mode instead of leveraging thread-based parallelism. Setting the SKLEARN_FAIL_NO_OPENMP environment variable (before cythonization) will force the build to fail if OpenMP is not supported.

To check if scikit-learn has been built correctly with OpenMP, run

python -c "import sklearn; sklearn.show_versions()"

and check if it contains Built with OpenMP: True.

When using conda on Mac, you can also check that the custom compilers are properly installed from conda-forge using the following command:

conda list

which should include compilers and llvm-openmp.

The compilers meta-package will automatically set custom environment variables:

echo $CC
echo $CXX
echo $CFLAGS
echo $CXXFLAGS
echo $LDFLAGS

They point to files and folders from your sklearn-dev conda environment (in particular in the bin/, include/ and lib/ subfolders). For instance -L/path/to/conda/envs/sklearn-dev/lib should appear in LDFLAGS.

Notes on Conda

Sometimes it can be necessary to open a new prompt before activating a newly created conda environment.

If you get any conflicting dependency error messages on Mac or Linux, try commenting out any custom conda configuration in the $HOME/.condarc file. In particular the channel_priority: strict directive is known to cause problems for this setup.

Note on dependencies for other Linux distributions

When precompiled wheels of the runtime dependencies are not available for your architecture (e.g. ARM), you can install the system versions:

sudo apt-get install cython3 python3-numpy python3-scipy

Notes on Meson

When building scikit-learn from source, existing scikit-learn installations and meson builds can lead to conflicts. You can use the Makefile provided in the scikit-learn repository to remove conflicting builds by calling:

make clean