tomMoral

import multiprocessing as mp
from multiprocessing import Manager


def test(idx, list_out):
    list_out.append(idx)


if __name__ == '__main__':
    mgr = Manager()

tomMoral

from time import sleep


def func0():
    print("run func0")
    sleep(1)
    print("ran func0")


def func1():

tomMoral

$ python -c "if 1:

    import numpy as np
    from sklearn.manifold.t_sne import TSNE, trustworthiness
    from sklearn.utils import check_random_state
    random_state = check_random_state(0)
    X = random_state.randn(50, 2).astype(np.float32)
    n_iter = 250
    tsne = TSNE(n_components=2, init='pca', random_state=0,
                method='barnes_hut', verbose=20, n_iter=n_iter)

tomMoral

"""
This example highlights the fact that the ProcessPoolExecutor implementation
from concurrent.futures is not robust to unpickling error for the result
(at least in versions 3.6 and lower).
"""
from pickle import UnpicklingError
from concurrent.futures import ProcessPoolExecutor


def return_instance(cls):

tomMoral

#include <mpi.h>
#include <iostream>


using namespace MPI;
using namespace std;

int main(int argc, char**argv) {

    //Initiate the MPI API

tomMoral

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc

# activate latex text rendering
rc('text', usetex=True)


if __name__ == "__main__":

tomMoral

#!/bin/bash


install_conda(){
  _USAGE="Usage
=====
./install_conda.sh <--gpu> <--prefix|-p DIR>

Install conda with miniforge on the system.