Tracking progress of joblib.Parallel execution

The documentation you linked to states that Parallel has an optional progress meter. It’s implemented by using the callback keyword argument provided by multiprocessing.Pool.apply_async:

# This is inside a dispatch function
self._lock.acquire()
job = self._pool.apply_async(SafeFunction(func), args,
            kwargs, callback=CallBack(self.n_dispatched, self))
self._jobs.append(job)
self.n_dispatched += 1

…

class CallBack(object):
    """ Callback used by parallel: it is used for progress reporting, and
        to add data to be processed
    """
    def __init__(self, index, parallel):
        self.parallel = parallel
        self.index = index

    def __call__(self, out):
        self.parallel.print_progress(self.index)
        if self.parallel._original_iterable:
            self.parallel.dispatch_next()

And here’s print_progress:

def print_progress(self, index):
    elapsed_time = time.time() - self._start_time

    # This is heuristic code to print only 'verbose' times a messages
    # The challenge is that we may not know the queue length
    if self._original_iterable:
        if _verbosity_filter(index, self.verbose):
            return
        self._print('Done %3i jobs       | elapsed: %s',
                    (index + 1,
                     short_format_time(elapsed_time),
                    ))
    else:
        # We are finished dispatching
        queue_length = self.n_dispatched
        # We always display the first loop
        if not index == 0:
            # Display depending on the number of remaining items
            # A message as soon as we finish dispatching, cursor is 0
            cursor = (queue_length - index + 1
                      - self._pre_dispatch_amount)
            frequency = (queue_length // self.verbose) + 1
            is_last_item = (index + 1 == queue_length)
            if (is_last_item or cursor % frequency):
                return
        remaining_time = (elapsed_time / (index + 1) *
                    (self.n_dispatched - index - 1.))
        self._print('Done %3i out of %3i | elapsed: %s remaining: %s',
                    (index + 1,
                     queue_length,
                     short_format_time(elapsed_time),
                     short_format_time(remaining_time),
                    ))

The way they implement this is kind of weird, to be honest – it seems to assume that tasks will always be completed in the order that they’re started. The index variable that goes to print_progress is just the self.n_dispatched variable at the time the job was actually started. So the first job launched will always finish with an index of 0, even if say, the third job finished first. It also means they don’t actually keep track of the number of completed jobs. So there’s no instance variable for you to monitor.

I think your best best is to make your own CallBack class, and monkey patch Parallel:

from math import sqrt
from collections import defaultdict
from joblib import Parallel, delayed

class CallBack(object):
    completed = defaultdict(int)

    def __init__(self, index, parallel):
        self.index = index
        self.parallel = parallel

    def __call__(self, index):
        CallBack.completed[self.parallel] += 1
        print("done with {}".format(CallBack.completed[self.parallel]))
        if self.parallel._original_iterable:
            self.parallel.dispatch_next()

import joblib.parallel
joblib.parallel.CallBack = CallBack

if __name__ == "__main__":
    print(Parallel(n_jobs=2)(delayed(sqrt)(i**2) for i in range(10)))

Output:

done with 1
done with 2
done with 3
done with 4
done with 5
done with 6
done with 7
done with 8
done with 9
done with 10
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]

That way, your callback gets called whenever a job completes, rather than the default one.

Here’s another answer to your question with the following syntax:

aprun = ParallelExecutor(n_jobs=5)

a1 = aprun(total=25)(delayed(func)(i ** 2 + j) for i in range(5) for j in range(5))
a2 = aprun(total=16)(delayed(func)(i ** 2 + j) for i in range(4) for j in range(4))
a2 = aprun(bar='txt')(delayed(func)(i ** 2 + j) for i in range(4) for j in range(4))
a2 = aprun(bar=None)(delayed(func)(i ** 2 + j) for i in range(4) for j in range(4))

https://stackoverflow.com/a/40415477/232371

Expanding on dano’s answer for the newest version of the joblib library. There were a couple of changes to the internal implementation.

from joblib import Parallel, delayed
from collections import defaultdict

# patch joblib progress callback
class BatchCompletionCallBack(object):
  completed = defaultdict(int)

  def __init__(self, time, index, parallel):
    self.index = index
    self.parallel = parallel

  def __call__(self, index):
    BatchCompletionCallBack.completed[self.parallel] += 1
    print("done with {}".format(BatchCompletionCallBack.completed[self.parallel]))
    if self.parallel._original_iterator is not None:
      self.parallel.dispatch_next()

import joblib.parallel
joblib.parallel.BatchCompletionCallBack = BatchCompletionCallBack

Text progress bar

One more variant for those, who want text progress bar without additional modules like tqdm. Actual for joblib=0.11, python 3.5.2 on linux at 16.04.2018 and shows progress upon subtask completion.

Redefine native class:

class BatchCompletionCallBack(object):
    # Added code - start
    global total_n_jobs
    # Added code - end
    def __init__(self, dispatch_timestamp, batch_size, parallel):
        self.dispatch_timestamp = dispatch_timestamp
        self.batch_size = batch_size
        self.parallel = parallel

    def __call__(self, out):
        self.parallel.n_completed_tasks += self.batch_size
        this_batch_duration = time.time() - self.dispatch_timestamp

        self.parallel._backend.batch_completed(self.batch_size,
                                           this_batch_duration)
        self.parallel.print_progress()
        # Added code - start
        progress = self.parallel.n_completed_tasks / total_n_jobs
        print(
            "rProgress: [{0:50s}] {1:.1f}%".format('#' * int(progress * 50), progress*100)
            , end="", flush=True)
        if self.parallel.n_completed_tasks == total_n_jobs:
            print('n')
        # Added code - end
        if self.parallel._original_iterator is not None:
            self.parallel.dispatch_next()

import joblib.parallel
import time
joblib.parallel.BatchCompletionCallBack = BatchCompletionCallBack

Define global constant before usage with total number of jobs:

total_n_jobs = 10

This will result in something like this:

Progress: [########################################          ] 80.0%

In Jupyter tqdm starts a new line in the output each time it outputs.
So for Jupyter Notebook it will be:

For use in Jupyter notebook.
No sleeps:

from joblib import Parallel, delayed
from datetime import datetime
from tqdm import notebook

def myfun(x):
    return x**2

results = Parallel(n_jobs=8)(delayed(myfun)(i) for i in notebook.tqdm(range(1000)))  

100% 1000/1000 [00:06<00:00, 143.70it/s]

With time.sleep:

from joblib import Parallel, delayed
from datetime import datetime
from tqdm import notebook
from random import randint
import time

def myfun(x):
    time.sleep(randint(1, 5))
    return x**2

results = Parallel(n_jobs=7)(delayed(myfun)(i) for i in notebook.tqdm(range(100)))

What I’m currently using instead of joblib.Parallel:

import concurrent.futures
from tqdm import notebook
from random import randint
import time

iterable = [i for i in range(50)]

def myfun(x):
    time.sleep(randint(1, 5))
    return x**2

def run(func, iterable, max_workers=8):
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        results = list(notebook.tqdm(executor.map(func, iterable), total=len(iterable)))
    return results

run(myfun, iterable)

Yet another step ahead from dano’s and Connor’s answers is to wrap the whole thing as a context manager:

import contextlib
import joblib
from tqdm import tqdm

@contextlib.contextmanager
def tqdm_joblib(tqdm_object):
    """Context manager to patch joblib to report into tqdm progress bar given as argument"""
    class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack):
        def __call__(self, *args, **kwargs):
            tqdm_object.update(n=self.batch_size)
            return super().__call__(*args, **kwargs)

    old_batch_callback = joblib.parallel.BatchCompletionCallBack
    joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback
    try:
        yield tqdm_object
    finally:
        joblib.parallel.BatchCompletionCallBack = old_batch_callback
        tqdm_object.close()

Then you can use it like this and don’t leave monkey patched code once you’re done:

from math import sqrt
from joblib import Parallel, delayed

with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar:
    Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10))

which is awesome I think and it looks similar to tqdm pandas integration.

TLDR solution:

Works with joblib 0.14.0 and tqdm 4.46.0 using python 3.5. Credits to frenzykryger for contextlib suggestions, dano and Connor for monkey patching idea.

import contextlib
import joblib
from tqdm import tqdm
from joblib import Parallel, delayed

@contextlib.contextmanager
def tqdm_joblib(tqdm_object):
    """Context manager to patch joblib to report into tqdm progress bar given as argument"""

    def tqdm_print_progress(self):
        if self.n_completed_tasks > tqdm_object.n:
            n_completed = self.n_completed_tasks - tqdm_object.n
            tqdm_object.update(n=n_completed)

    original_print_progress = joblib.parallel.Parallel.print_progress
    joblib.parallel.Parallel.print_progress = tqdm_print_progress

    try:
        yield tqdm_object
    finally:
        joblib.parallel.Parallel.print_progress = original_print_progress
        tqdm_object.close()

You can use this the same way as described by frenzykryger

import time
def some_method(wait_time):
    time.sleep(wait_time)

with tqdm_joblib(tqdm(desc="My method", total=10)) as progress_bar:
    Parallel(n_jobs=2)(delayed(some_method)(0.2) for i in range(10))

Longer explanation:

The solution by Jon is simple to implement, but it only measures the dispatched task. If the task takes a long time, the bar will be stuck at 100% while waiting for the last dispatched task to finish execution.

The context manager approach by frenzykryger, improved from dano and Connor, is better, but the BatchCompletionCallBack can also be called with ImmediateResult before the task completes (See Intermediate results from joblib). This is going to get us a count that is over 100%.

Instead of monkey patching the BatchCompletionCallBack, we can just patch the print_progress function in Parallel. The BatchCompletionCallBack already calls this print_progress anyway. If the verbose is set (i.e. Parallel(n_jobs=2, verbose=100)), the print_progress will be printing out completed tasks, though not as nice as tqdm. Looking at the code, the print_progress is a class method, so it already has self.n_completed_tasks that logs the number we want. All we have to do is just to compare this with the current state of joblib’s progress and update only if there is a difference.

This was tested in joblib 0.14.0 and tqdm 4.46.0 using python 3.5.

Setting verbose=13 was enough for me: https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html

I get a line on stderr that says something like:

[Parallel(n_jobs=16)]: Done 134 tasks      | elapsed:  7.7min

import joblib
class ProgressParallel(joblib.Parallel):
    def __init__(self, n_total_tasks=None, **kwargs):
        super().__init__(**kwargs)
        self.n_total_tasks = n_total_tasks

    def __call__(self, *args, **kwargs):
        with tqdm() as self._pbar:
            return joblib.Parallel.__call__(self, *args, **kwargs)

    def print_progress(self):
        if self.n_total_tasks:
            self._pbar.total = self.n_total_tasks
        else:
            self._pbar.total = self.n_dispatched_tasks
        self._pbar.n = self.n_completed_tasks
        self._pbar.refresh()