Calling Julia from Python – an experiment on data loading


See the slides.


After reading Patrick’s blog post, we decided to try to replace C++ with Julia to check:

  • How easy/hard it is
  • How much improvement can be gained with a basic version
  • How much improvement can be gained with an optimized version

A basic version is already an improvement over the pure Python version, and an optimized version was faster than the C++ version.


  • Follow Patrick’s blog post to install the C++ part.
  • Install Julia (We’ve used Julia 1.6.3)
    • I recommend using Jill
    • We’ll refer to this Julia as path/to/julia.
  • Install Python
    • Ideally, one dynamically linked to libpython.
    • To test it, use ldd path/to/python and look for libpython3.9. It should exist for the shared version.
    • If you don’t have, look into workarounds here
    • Tip: Archlinux’s system Python is dynamically linked.
    • We’ve used Python 3.9.7 from Archlinux.
  • Open Julia and enter the following commands:
    • ENV["PYTHON"] = "path/to/python"
    • using Pkg
    • Pkg.add("PyCall")
    • This will make sure that the packages we are installing use the correct Python version
  • Install juliapy with path/to/python -m pip install julia
  • Run path/to/python and enter
    • import julia
    • julia.install("julia=path/to/julia")
  • Download dataset and store in gen-data folder: Zenodo badge
  • Run – it should take several hours (over 10) and consume a moderate amount of memory.
  • Run


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