One progress bar for a parallel job python

The loop runs over some number of models (n_mod) and is distributed among the n_cpus. As you will note, running this code as mpirun -np 4 python test_mpi.py produces 4 progress bars. This is understandable. But is there a way to use tdqm to get one progress bar which tells me how many models have been completed?

from tqdm import tqdm
from mpi4py import MPI
import time

comm = MPI.COMM_WORLD
cpu_ind = comm.Get_rank()
n_cpu = comm.Get_size()

n_mod=100
for i in tqdm(range(n_mod)):
    if (cpu_ind == int(i/int(n_mod/n_cpu))%n_cpu):
        #some task here which is a function of i
        time.sleep(0.02)

This can be achieved with a main node and worker node(s) setup.

Essentially only the rank == 0 node will be updating a progress bar whilst the worker nodes will simply be informing the main node that they have completed the task.

Worker:

(Defined similarly to your above code)

def worker(n_mod, size, rank):
    comm = MPI.COMM_WORLD
    step = size - 1  # Number of worker processes
    start = rank - 1  

    for i in range(start, n_mod, step):
        time.sleep(0.02)  # Work
        # Notify the master that a task is done
        comm.send('done', dest=0, tag=1)

Main Node:

def pbar_node(n_mod, size):

    comm = MPI.COMM_WORLD
    pbar = tqdm(total=n_mod, desc="Processing Models")
    completed = 0

    while completed < n_mod:
        # Receive a message from any worker
        status = MPI.Status()
        comm.recv(source=MPI.ANY_SOURCE, tag=1, status=status)
        completed += 1
        pbar.update(1)

    pbar.close()

I have tested this with mpirun -np 4 python test.py and get a single progress bar for 4 models sharing 100 tasks as shown in the screenshot.

Ensure that you take into account the fact there will be the number of workers plus a main node when dealing with distributing tasks so you do not get off-by-one errors.

You can use these like so:

comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()

n_mod = N_TASKS

if rank == 0:
    # pbar process
    pbar_node(n_mod)else:
    # Worker processes
    worker(n_mod, size, rank)