Troubleshooting

Your Python interpreter is statically linked to libpython

If you use Python installed with Debian-based Linux distribution such as Ubuntu or install Python by conda, you might have noticed that PyJulia cannot be initialized properly with Julia ≥ 0.7. This is because those Python executables are statically linked to libpython. (See Limitations for why that’s a problem.)

If you are unsure if your python has this problem, you can quickly check it by:

$ ldd /usr/bin/python
        linux-vdso.so.1 (0x00007ffd73f7c000)
        libpthread.so.0 => /usr/lib/libpthread.so.0 (0x00007f10ef84e000)
        libc.so.6 => /usr/lib/libc.so.6 (0x00007f10ef68a000)
        libpython3.7m.so.1.0 => /usr/lib/libpython3.7m.so.1.0 (0x00007f10ef116000)
        /lib64/ld-linux-x86-64.so.2 => /usr/lib64/ld-linux-x86-64.so.2 (0x00007f10efaa4000)
        libdl.so.2 => /usr/lib/libdl.so.2 (0x00007f10ef111000)
        libutil.so.1 => /usr/lib/libutil.so.1 (0x00007f10ef10c000)
        libm.so.6 => /usr/lib/libm.so.6 (0x00007f10eef87000)

in Linux where /usr/bin/python should be replaced with the path to your python command (use which python to find it out). In macOS, use otool -L instead of ldd. If it does not print the path to libpython like /usr/lib/libpython3.7m.so.1.0 in above example, you need to use one of the workaround below.

Turn off compilation cache

New in version 0.3.

The easiest workaround is to pass compiled_modules=False to the Julia constructor.

>>> from julia.api import Julia
>>> jl = Julia(compiled_modules=False)

This is equivalent to julia’s command line option --compiled-modules=no and disables the precompilation cache mechanism in Julia. Note that this option slows down loading and using Julia packages especially for complex and large ones.

See also API documentation of Julia.

Create a custom system image

New in version 0.4.

A very powerful way to avoid this the issue due to precompilation cache is to create a custom system image. This also has an additional benefit that initializing PyJulia becomes instant. See Custom Julia system image for how to create and use a custom system image.

python-jl: an easy workaround

New in version 0.2.

Another easy workaround is to use the python-jl command bundled in PyJulia. This can be used instead of normal python command for basic use-cases such as:

$ python-jl your_script.py
$ python-jl -c 'from julia.Base import banner; banner()'
$ python-jl -m IPython

See python-jl --help for more information.

How python-jl works

Note that python-jl works by launching Python interpreter inside Julia. Importantly, it means that PyJulia has to be installed in the Python environment with which PyCall is configured. That is to say, following commands must work for python-jl to be usable:

julia> using PyCall

julia> pyimport("julia")
PyObject <module 'julia' from '/.../julia/__init__.py'>

In fact, you can simply use PyJulia inside the Julia REPL, if you are comfortable with working in it:

julia> using PyCall

julia> py"""
       from julia import Julia
       Julia(init_julia=False)
       # Then use your Python module:
       from your_module_using_pyjulia import function
       function()
       """

Ultimate fix: build your own Python

Alternatively, you can use pyenv to build Python with --enable-shared option (see their Wiki page). Of course, manually building from Python source distribution with the same configuration also works.

$ PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install 3.6.6
Downloading Python-3.6.6.tar.xz...
-> https://www.python.org/ftp/python/3.6.6/Python-3.6.6.tar.xz
Installing Python-3.6.6...
Installed Python-3.6.6 to /home/USER/.pyenv/versions/3.6.6

$ ldd ~/.pyenv/versions/3.6.6/bin/python3.6 | grep libpython
        libpython3.6m.so.1.0 => /home/USER/.pyenv/versions/3.6.6/lib/libpython3.6m.so.1.0 (0x00007fca44c8b000)

For more discussion, see: https://github.com/JuliaPy/pyjulia/issues/185

Segmentation fault in IPython

You may experience segmentation fault when using PyJulia in old versions of IPython. You can avoid this issue by updating IPython to 7.0 or above. Alternatively, you can use IPython via Jupyter (e.g., jupyter console) to workaround the problem.

Error due to libstdc++ version

When you use PyJulia with another Python extension, you may see an error like version `GLIBCXX_3.4.22' not found (Linux) or The procedure entry point ... could not be located in the dynamic link library libstdc++6.dll (Windows). In this case, you might have observed that initializing PyJulia first fixes the problem. This is because Julia (or likely its dependencies like LLVM) requires a recent version of libstdc++.

Possible fixes:

  • Initialize PyJulia (e.g., by from julia import Main) as early as possible. Note that just importing PyJulia (import julia) does not work.
  • Load libstdc++.so.6 first by setting environment variable LD_PRELOAD (Linux) to /PATH/TO/JULIA/DIR/lib/julia/libstdc++.so.6 where /PATH/TO/JULIA/DIR/lib is the directory which has libjulia.so. macOS and Windows likely to have similar mechanisms (untested).
  • Similarly, set environment variable LD_LIBRARY_PATH (Linux) to /PATH/TO/JULIA/DIR/lib/julia directory. Using DYLD_LIBRARY_PATH on macOS and PATH on Windows may work (untested).

See: https://github.com/JuliaPy/pyjulia/issues/180, https://github.com/JuliaPy/pyjulia/issues/223