Python_Parallel_Code.py

# Install the joblib library if needed.
# conda install joblib

import joblib as jb
import numpy as np


def my_function_that_take_time(arg1, arg2):
  
  # Simulate a long computation.
  import time
  time.sleep(5)
  
  # Here we return the addition of arg1 and arg2
  return arg1 + arg2

  
# Generate random list of parameters for our dummy function.
# It takes the form of a dict.
parameters_list = [{'arg1': val1, 'arg2': val2} for val1, val2 in np.random.randint(0, 50, size=(20, 2))]

# Generate th arguments for the executor.
executor_args = [jb.delayed(my_function_that_take_time)(**params) for params in parameters_list]

# Instantiate the executor.
# `n_jobs` can also be -1 to use all available CPUs
# or 1 to disable parallelization at all.
n_jobs = 4
executor = jb.Parallel(n_jobs=n_jobs)

# Execute your parallel job.
results = executor(executor_args)

# If you want to track progress
# of your computations, you can 
# use a progress bar.
# (anamic is required for this part)

import anamic

executor = anamic.utils.parallel_executor(n_jobs=n_jobs)

# Execute your parallel job.
results = executor()(executor_args)