We have built an MVP in Python Data ecosystem, but the performance is much less than desired.

Our approach:

  • multiprocessing module for task parallelisation (which is awkwardly done in Python - pickle/unpickle each task)
  • scikit-learn library
  • pandas for data transport
  • it's not about dealing with big data or training models on large data, after profiling we figured out most of the time is taken by a regular computing tasks, but we are doing a lot.


  1. We wonder what else can we use to make computing faster? After some research number of candidates popped up: Julia, Lua, Rust etc.
    • Requirements:
      • FASTER
      • Parallelisation support by design (not some workaround like Python does)
      • Existence of scientific libraries, not all of Python but major selection
      • Compiled (I guess)
  2. But before we move to another platform - we wonder if there's a way of speeding up existing Python code?

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