How to parallelize CSV files processing?

I have a few thousand CSV files in a directory on my machine that need to be validated based on a regex that I have formulated. The path_to_validator points to a Scala script, that is run through a windows .bat file on the command line. It reads the regex and the csv file and gives it a PASS/FAIL grade, which is printed to output.txt.

The constraint is that this Scala script takes a directory as argument, not a Python list, therefore I cannot split the workload so easily between processes. I could have each process' files moved to a temporary directory, but the details of the project are such that, ideally, my deployed program should not need write privileges to the CSV files.

This is the code:

with open("output.txt", 'w') as output:
    for filename in os.listdir(path_to_csv_folder):
        print("Processing file " + str(current_file_count) + "/" + str(TOTAL_FILE_COUNT), end='\r')

        output.write(filename + ': ')
        validator = subprocess.Popen([path_to_validator, path_to_csv_folder + filename, path_to_csv_schema, "-x",
                                      CSV_ENCODING, "-y", CSV_SCHEMA_ENCODING], stdout=subprocess.PIPE,
                                     stderr=subprocess.PIPE)
        result = validator.stdout.read()
        output.write(result.decode('windows-1252'))

        current_file_count += 1

The issue is that it takes 1h 30min+ while utilizing only about 20% CPU. This should be an obvious candidate for parallelization speedup. The directory has 5000+ CSV files, and they all need to be processed. How can I split the workload onto 4 different processes in order to utilize all CPU power?

This is the code I actually made:

"""
Command line API to CSV validator using Scala implementation from:
http://digital-preservation.github.io/csv-validator/#toc7
"""

PATH_TO_VALIDATOR = r"C:\prog\csv\csv-validator-cmd-1.2-RC2\bin\validate.bat"
PATH_TO_CSV_FOLDER = r"C:\prog\csv\CSVFiles"
PATH_TO_CSV_SCHEMA = r"C:\prog\csv\ocr-schema.csvs"
# Set defaults
CSV_ENCODING = "windows-1252"
CSV_SCHEMA_ENCODING = "UTF-8"


def open_csv(CSV_LIST):
    import subprocess

    # To be used to display a simple progress indicator
    TOTAL_FILE_COUNT = len(CSV_LIST)
    current_file_count = 1

    with open("output.txt", 'w') as output:
        for filename in CSV_LIST:
            print("Processing file " + str(current_file_count) + "/" + str(TOTAL_FILE_COUNT))

            output.write(filename + ': ')
            validator = subprocess.Popen(
                [PATH_TO_VALIDATOR, PATH_TO_CSV_FOLDER + "/" + filename, PATH_TO_CSV_SCHEMA, "--csv-encoding",
                 CSV_ENCODING, "--csv-schema-encoding", CSV_SCHEMA_ENCODING, '--fail-fast', 'true'], stdout=subprocess.PIPE)
            result = validator.stdout.read()
            output.write(result.decode('windows-1252'))

            current_file_count += 1


# Split a list into n sublists of roughly equal size
def split_list(alist, wanted_parts=1):
    length = len(alist)
    return [alist[i * length // wanted_parts: (i + 1) * length // wanted_parts]
            for i in range(wanted_parts)]


if __name__ == '__main__':
    import argparse
    import multiprocessing
    import os

    parser = argparse.ArgumentParser(description="Command line API to Scala CSV validator")
    parser.add_argument('-pv', '--PATH_TO_VALIDATOR', help="Specify the path to csv-validator-cmd/bin/validator.bat",
                        required=True)
    parser.add_argument('-pf', '--PATH_TO_CSV_FOLDER', help="Specify the path to the folder containing the csv files "
                                                            "you want to validate", required=True)
    parser.add_argument('-ps', '--PATH_TO_CSV_SCHEMA', help="Specify the path to CSV schema you want to use to "
                                                            "validate the given files", required=True)

    parser.add_argument('-cenc', '--CSV_ENCODING', help="Optional parameter to specify the encoding used by the CSV "
                                                        "files. Choose UTF-8 or windows-1252. Default windows-1252")
    parser.add_argument('-csenc', '--CSV_SCHEMA_ENCODING', help="Optional parameter to specify the encoding used by "
                                                                "the CSV Schema. Choose UTF-8 or windows-1252. "
                                                                "Default UTF-8")

    args = vars(parser.parse_args())

    if args['CSV_ENCODING'] is not None:
        CSV_ENCODING = args['CSV_ENCODING']

    if args['CSV_SCHEMA_ENCODING'] is not None:
        CSV_SCHEMA_ENCODING = args['CSV_SCHEMA_ENCODING']

    PATH_TO_VALIDATOR = args["PATH_TO_VALIDATOR"]
    PATH_TO_CSV_SCHEMA = args["PATH_TO_CSV_SCHEMA"]
    PATH_TO_CSV_FOLDER = args["PATH_TO_CSV_FOLDER"]

    CPU_COUNT = multiprocessing.cpu_count()

    split_csv_directory = split_list(os.listdir(args["PATH_TO_CSV_FOLDER"]), wanted_parts=CPU_COUNT)

    # Spawn a Process for each CPU on the system
    for csv_list in split_csv_directory:
        p = multiprocessing.Process(target=open_csv, args=(csv_list,))
        p.start()

Please let me know of any pitfalls in my code.

Have a look on This introduction to the multiprocessing package.

for example, try:

import multiprocessing as mp
import os

def process_csv(csv):
    % process the csv
    return {csv: collected_debug_information}

pool = mp.Pool(processes=4)
results = pool.map(process_csv, os.listdir(path_to_csv_folder))

with the returned dict, you can have a look on the results to evaluate some parsing errors or so. It will be a list of dicts with the csv names as keys.

Also a good package for that is joblib, have a look on that too, under the hood it uses the multiprocessing package.