Troubleshooting

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Troubleshooting#

This chapter discusses issues that may be encountered during the build or runtime.

OMP: Error #15:#

Description: Python crashes when running a code that imports PyHarm and some other library (for instance, Matplotlib).

Error message:

OMP: Error #15: Initializing libiomp5md.dll, but found libomp140.x86_64.dll
already initialized.

OMP: Hint This means that multiple copies of the OpenMP runtime have been
linked into the program. That is dangerous, since it can degrade performance
or cause incorrect results. The best thing to do is to ensure that only
a single OpenMP runtime is linked into the process, e.g. by avoiding static
linking of the OpenMP runtime in any library. As an unsafe, unsupported,
undocumented workaround you can set the environment variable
KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but
that may cause crashes or silently produce incorrect results. For more
information, please see http://www.intel.com/software/products/support/.

Fatal Python error: Aborted

Cause: Two or more of your Python modules are linked against different OpenMP implementations (e.g., PyHarm and Matplotlib).

Solution: You have to ensure that only one OpenMP library is loaded during the runtime. There are several ways to achieve this, each with its pros and cons.

  • Solution 1: Follow the hint from the error message and put this at the top of your code, that is, before any import statements:

    >>> import os
>>> os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

    This, however, is only a workaround and not a solution, as is clear from the error message. Having said this, users report that Python behaves as they expect after adding this to their code.

  • Solution 2: If you have installed numpy using the default conda’s channel, the MKL module may have been installed on your machine during the installation. mkl is the Intel’s optimized math library that includes the Intel’s OpenMP implementation. On the other hand, the PyHarm wheels distributed through pip are linked against other than the Intel’s OpenMP library. Therefore, if you import a Python module that is linked against the Intel’s OpenMP library from the mkl module and PyHarm, which relies on a different OpenMP implementation, you may encounter the error.

    If you do not insist on using the Intel’s math library and are willing to replace it by open source implementations, install the nomkl module via conda using the conda-forge channel:

    conda install -c conda-forge nomkl

    This may resolve the issue.

  • Solution 3: Compile PyHarm from source (see Installing) using an OpenMP implementation that is compatible with your other modules.

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