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Multiprocessing - Running PythonPr in parallel

PythonPr can be run in parallel using duplication, e.g. by running a pipeline that contains the PythonPr processing resource via gcp or gcp-direct (see

When running under gcp, the pipeline gets duplicated as many times as needed so each parallel process has its own copy. Each duplication has its own duplicate of the PythonPr resource and each PythonPr resource starts its own Python process. So the code run for each duplicate is completely separate and variables cannot be shared between them.

Each duplicate gets passed the following keyword arguments to the start, _call__ and finish methods:

When each of the process finishes, the finish method is invoked. If the finish method returns a map/dictionary, processing is a bit different if there are duplicates: since there are several duplicates, the maps returned by each of them are collected in a list and then passed on method reduce or the first duplicate, if that method is defined in the class.

The reduce method is expected to use all the results from all the processes to calculate and return a map of the overall result for the whole processing which is then treated as a result returned from finish in the single process case.

The following code uses the reduce method to calculate, print and return the overall results of counting words (as in PythonPrResult) when running multiple duplicates:

 import sys
import re
from collections import Counter
from gatenlp import interact, GateNlpPr, Document

class MyProcessor:

  def __init__(self):
    self.words_total = 0
    self.nr_docs = 0
  def start(self, **kwargs):
    self.words_total = 0
    self.nr_docs = 0
  def finish(self, **kwargs):
    return {"nrdocs": self.nr_docs, "nrwords": self.words_total}
  def reduce(self, resultlist):
    totals = Counter()
    for r in resultlist:
    print("Total number of documents: ", totals["nrdocs"])
    print("Total number of words: ", totals["nrwords"])
    return totals
  def __call__(self, doc, **kwargs):
    text = doc.text
    whitespaces = [m for m in re.finditer(r"[\s,.!?]+|^[\s,.!?]*|[\s,.!?]*$",text)]
    nrwords = len(whitespaces)-1
    self.words_total += nrwords
    self.nr_docs += 1

if __name__ == '__main__':