I'm looking for shorter ways to prepare my dataset for a machine-learning task. I found that the multiprocessing library might helpful. However, because I'm a newbie in multiprocessing, I couldn't find a proper way.
I first wrote some codes like below:
class DatasetReader:
def __init__(self):
self.data_list = Read_Data_from_file
self.data = []
def _ready_data(self, ex, idx):
# Some complex functions that takes several minutes
def _dataset_creator(self, queue):
for idx, ex in enumerate(self.data_list):
queue.put(self._ready_data(ex, idx))
def _dataset_consumer(self, queue):
total_mem = 0.0
t = tqdm(range(self.num_data), total=self.num_data, desc='Building Dataset ', bar_format='{desc}:{percentage:3.0f}% ({n_fmt}/{total_fmt}) [{elapsed}<{remaining},{rate_fmt}{postfix}]')
for idx in t:
ins = queue.get()
self.data.append(ins)
gc.collect()
def _build_dataset(self):
queue = Queue()
creator = Process(target=self._dataset_creator, args=(queue,))
consumer = Process(target=self._dataset_consumer, args=(queue,))
creator.start()
consumer.start()
queue.close()
queue.join_thread()
creator.join()
consumer.join()
However, in my opinion, because the _dataset_creator processes data (here _ready_data) in serial manner, this would not be helpful for reducing time consumption.
So, I modified the code to generate multiple processes that process one datum:
class DatasetReader:
def __init__(self):
self.data_list = Read_Data_from_file
self.data = []
def _ready_data(self, ex, idx):
# Some complex functions that takes several minutes
def _dataset_creator(self, ex, idx, queue):
queue.put(self._ready_data(ex, idx))
def _dataset_consumer(self, queue):
total_mem = 0.0
t = tqdm(range(self.num_data), total=self.num_data, desc='Building Dataset ', bar_format='{desc}:{percentage:3.0f}% ({n_fmt}/{total_fmt}) [{elapsed}<{remaining},{rate_fmt}{postfix}]')
for idx in t:
ins = queue.get()
self.data.append(ins)
gc.collect()
def _build_dataset(self):
queue = Queue()
for idx, ex in enumerate(self.data_list):
p = Process(target=self._dataset_creator, args=(ex, idx, queue,))
p.start()
consumer = Process(target=self._dataset_consumer, args=(queue,))
consumer.start()
queue.close()
queue.join_thread()
consumer.join()
However, this returns me errors:
Process Process-18:
Traceback ~~~
RuntimeError: can't start new thread
Traceback ~~~
OSError: [Errno 12] Cannot allocate memory
Could you help me to process complex data in a parallel way?
EDIT 1:
Thanks to #tdelaney, I can reduce the time consumption by generating self.num_worker processes (16 in my experiment):
def _dataset_creator(self, pid, queue):
for idx, ex in list(enumerate(self.data_list))[pid::self.num_worker]:
queue.put(self._ready_data(ex, idx))
def _dataset_consumer(self, queue):
t = tqdm(range(self.num_data), total=self.num_data, desc='Building Dataset ', bar_format='{desc}:{percentage:3.0f}% ({n_fmt}/{total_fmt}) [{elapsed}<{remaining},{rate_fmt}{postfix}]')
for _ in t:
ins = queue.get()
self.data[ins['idx']] = ins
def _build_dataset(self):
queue = Queue()
procs = []
for pid in range(self.num_worker):
p = Process(target=self._dataset_creator, args=(pid, queue,))
procs.append(p)
p.start()
consumer = Process(target=self._dataset_consumer, args=(queue,))
consumer.start()
queue.close()
queue.join_thread()
for p in procs:
p.join()
consumer.join()
I'm trying to sketch out what a solution with a multiprocessing pool would look like. I got rid of the consumer process completely because it looks like the parent process is just waiting anyway (and needs the data eventually) so it can be the consumer. So, I set up a pool and use imap_unordered to handle passing the data to the worker.
I guessed that the data processing doesn't really need the DatasetReader at all and moved it out to its own function. On Windows, either the entire DataReader object is serialized to the subprocess (including data you don't want) or the child version of the object is incomplete and may crash when you try to use it.
Either way, changes made to a DatasetReader object in the child processes aren't seen in the parent. This can be unexpected if the parent is dependent on updated state in that object. Its best to severely bracket what's happening in subprocesses, in my opinion.
from multiprocessing import Pool, get_start_method, cpu_count
# moved out of class (assuming it is not class dependent) so that
# the entire DatasetReader object isn't pickled and sent to
# the child on spawning systems like Microsoft Windows
def _ready_data(idx_ex):
idx, ex = idx_ex
# Some complex functions that take several minutes
result = complex_functions(ex)
return (idx, result)
class DatasetReader:
def __init__(self):
self.data_list = Read_Data_from_file
self.data = [None] * len(data_list)
def _ready_data_fork(self, idx):
# on forking system, call worker with object data
return _ready_data((idx, self.data_list[idx]))
def run(self):
t = tqdm(range(self.num_data), total=self.num_data, desc='Building Dataset ',
bar_format='{desc}:{percentage:3.0f}% ({n_fmt}/{total_fmt}) '
'[{elapsed}<{remaining},{rate_fmt}{postfix}]')
pool = Pool(min(cpu_count, len(self.data_list)))
if get_start_method() == 'fork':
# on forking system, self.data_list is in child process and
# we only pass the index
result_iter = pool.imap_unordered(self._ready_data_fork,
(idx for idx in range(len(data_list))),
chunksize=1)
else:
# on spawning system, we need to pass the data
result_iter = pool.imap_unordered(_ready_data,
enumerate(self.data_list,
chunksize=1)
for idx, result in result_iter:
next(t)
self.data[idx] = result
pool.join()
I'm messing around with python multiprocessing module. But something is not working as I was expecting it to do, so now I'm a little bit confused.
In a python script, I create two child processes, so they can work with the same resource. I was thinking that they were going to "share" the load more or less equally, but it seems that, instead of doing that, one of the processes executes just once, while the other one process almost everything.
To test it, I wrote the following code:
#!/usr/bin/python
import os
import multiprocessing
# Worker function
def worker(queueA, queueB):
while(queueA.qsize() != 0):
item = queueA.get()
item = "item: " + item + ". processed by worker " + str(os.getpid())
queueB.put(item)
return
# IPC Manager
manager = multiprocessing.Manager()
queueA = multiprocessing.Queue()
queueB = multiprocessing.Queue()
# Fill queueA with data
for i in range(0, 10):
queueA.put("hello" + str(i+1))
# Create processes
process1 = multiprocessing.Process(target = worker, args = (queueA, queueB,))
process2 = multiprocessing.Process(target = worker, args = (queueA, queueB,))
# Call processes
process1.start()
process2.start()
# Wait for processes to stop processing
process1.join()
process2.join()
for i in range(0, queueB.qsize()):
print queueB.get()
And that prints the following:
item: hello1. processed by worker 11483
item: hello3. processed by worker 11483
item: hello4. processed by worker 11483
item: hello5. processed by worker 11483
item: hello6. processed by worker 11483
item: hello7. processed by worker 11483
item: hello8. processed by worker 11483
item: hello9. processed by worker 11483
item: hello10. processed by worker 11483
item: hello2. processed by worker 11482
As you can see, one of the processes works with just one of the elements, and it doesn't continue to get more elements of the queue, while the other has to work with everything else.
I'm thinking that this is not correct, or at least not what I expected. Could you tell me which is the correct way of implementing this idea?
You're right that they won't be exactly equal, but mostly that's because your testing sample is so small. It takes time for each process to get started and start processing. The time it takes to process an item in the queue is extremely low and therefore one can quickly process 9 items before the other gets through one.
I tested this below (in Python3, but it should apply for 2.7 as well just change the print() function to a print statement):
import os
import multiprocessing
# Worker function
def worker(queueA, queueB):
for item in iter(queueA.get, 'STOP'):
out = str(os.getpid())
queueB.put(out)
return
# IPC Manager
manager = multiprocessing.Manager()
queueA = multiprocessing.Queue()
queueB = multiprocessing.Queue()
# Fill queueA with data
for i in range(0, 1000):
queueA.put("hello" + str(i+1))
# Create processes
process1 = multiprocessing.Process(target = worker, args = (queueA, queueB,))
process2 = multiprocessing.Process(target = worker, args = (queueA, queueB,))
# Call processes
process1.start()
process2.start()
queueA.put('STOP')
queueA.put('STOP')
# Wait for processes to stop processing
process1.join()
process2.join()
all = {}
for i in range(1000):
item = queueB.get()
if item not in all:
all[item] = 1
else:
all[item] += 1
print(all)
My output (a count of how many were done from each process):
{'18376': 537,
'18377': 463}
While they aren't the exact same, as we approach longer times they will approach being about equal.
Edit:
Another way to confirm this is to add a time.sleep(3) inside the worker function
def worker(queueA, queueB):
for item in iter(queueA.get, 'STOP'):
time.sleep(3)
out = str(os.getpid())
queueB.put(out)
return
I ran a range(10) test like in your original example and got:
{'18428': 5,
'18429': 5}
How to use pipe correctly in multiple processes(>2)?
eg. one producer several consumer
these code is failure in Linux environment
but windows environment is well
import multiprocessing, time
def consumer(pipe,id):
output_p, input_p = pipe
input_p.close()
while True:
try:
item = output_p.recv()
except EOFError:
break
print("%s consumeļ¼%s" % (id,item))
#time.sleep(3) # if no sleep these code will fault in Linux environment
# but windows environment is well
print('Consumer done')
def producer(sequence, input_p):
for item in sequence:
print('produceļ¼',item)
input_p.send(item)
time.sleep(1)
if __name__ == '__main__':
(output_p, input_p) = multiprocessing.Pipe()
# create two consumer process
cons_p1 = multiprocessing.Process(target=consumer,args=((output_p,input_p),1))
cons_p1.start()
cons_p2 = multiprocessing.Process(target=consumer,args=((output_p,input_p),2))
cons_p2.start()
output_p.close()
sequence = [i for i in range(10)]
producer(sequence, input_p)
input_p.close()
cons_p1.join()
cons_p2.join()
Do not use pipe for multiple consumers. The documentation explicitly says it will be corrupted when more then two processes read or write. Which you do; two readers.
The two connection objects returned by Pipe() represent the two ends of the pipe. Each connection object has send() and recv() methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the same end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time.
So use Queue, or JoinableQueue even.
from multiprocessing import Process, JoinableQueue
from Queue import Empty
import time
def consumer(que, pid):
while True:
try:
item = que.get(timeout=10)
print("%s consume:%s" % (pid, item))
que.task_done()
except Empty:
break
print('Consumer done')
def producer(sequence, que):
for item in sequence:
print('produce:', item)
que.put(item)
time.sleep(1)
if __name__ == '__main__':
que = JoinableQueue()
# create two consumer process
cons_p1 = Process(target=consumer, args=(que, 1))
cons_p1.start()
cons_p2 = Process(target=consumer, args=(que, 2))
cons_p2.start()
sequence = [i for i in range(10)]
producer(sequence, que)
que.join()
cons_p1.join()
cons_p2.join()
I'm having much trouble trying to understand just how the multiprocessing queue works on python and how to implement it. Lets say I have two python modules that access data from a shared file, let's call these two modules a writer and a reader. My plan is to have both the reader and writer put requests into two separate multiprocessing queues, and then have a third process pop these requests in a loop and execute as such.
My main problem is that I really don't know how to implement multiprocessing.queue correctly, you cannot really instantiate the object for each process since they will be separate queues, how do you make sure that all processes relate to a shared queue (or in this case, queues)
My main problem is that I really don't know how to implement multiprocessing.queue correctly, you cannot really instantiate the object for each process since they will be separate queues, how do you make sure that all processes relate to a shared queue (or in this case, queues)
This is a simple example of a reader and writer sharing a single queue... The writer sends a bunch of integers to the reader; when the writer runs out of numbers, it sends 'DONE', which lets the reader know to break out of the read loop.
You can spawn as many reader processes as you like...
from multiprocessing import Process, Queue
import time
import sys
def reader_proc(queue):
"""Read from the queue; this spawns as a separate Process"""
while True:
msg = queue.get() # Read from the queue and do nothing
if msg == "DONE":
break
def writer(count, num_of_reader_procs, queue):
"""Write integers into the queue. A reader_proc() will read them from the queue"""
for ii in range(0, count):
queue.put(ii) # Put 'count' numbers into queue
### Tell all readers to stop...
for ii in range(0, num_of_reader_procs):
queue.put("DONE")
def start_reader_procs(qq, num_of_reader_procs):
"""Start the reader processes and return all in a list to the caller"""
all_reader_procs = list()
for ii in range(0, num_of_reader_procs):
### reader_p() reads from qq as a separate process...
### you can spawn as many reader_p() as you like
### however, there is usually a point of diminishing returns
reader_p = Process(target=reader_proc, args=((qq),))
reader_p.daemon = True
reader_p.start() # Launch reader_p() as another proc
all_reader_procs.append(reader_p)
return all_reader_procs
if __name__ == "__main__":
num_of_reader_procs = 2
qq = Queue() # writer() writes to qq from _this_ process
for count in [10**4, 10**5, 10**6]:
assert 0 < num_of_reader_procs < 4
all_reader_procs = start_reader_procs(qq, num_of_reader_procs)
writer(count, len(all_reader_procs), qq) # Queue stuff to all reader_p()
print("All reader processes are pulling numbers from the queue...")
_start = time.time()
for idx, a_reader_proc in enumerate(all_reader_procs):
print(" Waiting for reader_p.join() index %s" % idx)
a_reader_proc.join() # Wait for a_reader_proc() to finish
print(" reader_p() idx:%s is done" % idx)
print(
"Sending {0} integers through Queue() took {1} seconds".format(
count, (time.time() - _start)
)
)
print("")
Here's a dead simple usage of multiprocessing.Queue and multiprocessing.Process that allows callers to send an "event" plus arguments to a separate process that dispatches the event to a "do_" method on the process. (Python 3.4+)
import multiprocessing as mp
import collections
Msg = collections.namedtuple('Msg', ['event', 'args'])
class BaseProcess(mp.Process):
"""A process backed by an internal queue for simple one-way message passing.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.queue = mp.Queue()
def send(self, event, *args):
"""Puts the event and args as a `Msg` on the queue
"""
msg = Msg(event, args)
self.queue.put(msg)
def dispatch(self, msg):
event, args = msg
handler = getattr(self, "do_%s" % event, None)
if not handler:
raise NotImplementedError("Process has no handler for [%s]" % event)
handler(*args)
def run(self):
while True:
msg = self.queue.get()
self.dispatch(msg)
Usage:
class MyProcess(BaseProcess):
def do_helloworld(self, arg1, arg2):
print(arg1, arg2)
if __name__ == "__main__":
process = MyProcess()
process.start()
process.send('helloworld', 'hello', 'world')
The send happens in the parent process, the do_* happens in the child process.
I left out any exception handling that would obviously interrupt the run loop and exit the child process. You can also customize it by overriding run to control blocking or whatever else.
This is really only useful in situations where you have a single worker process, but I think it's a relevant answer to this question to demonstrate a common scenario with a little more object-orientation.
I had a look at multiple answers across stack overflow and the web while trying to set-up a way of doing multiprocessing using queues for passing around large pandas dataframes. It seemed to me that every answer was re-iterating the same kind of solutions without any consideration of the multitude of edge cases one will definitely come across when setting up calculations like these. The problem is that there is many things at play at the same time. The number of tasks, the number of workers, the duration of each task and possible exceptions during task execution. All of these make synchronization tricky and most answers do not address how you can go about it. So this is my take after fiddling around for a few hours, hopefully this will be generic enough for most people to find it useful.
Some thoughts before any coding examples. Since queue.Empty or queue.qsize() or any other similar method is unreliable for flow control, any code of the like
while True:
try:
task = pending_queue.get_nowait()
except queue.Empty:
break
is bogus. This will kill the worker even if milliseconds later another task turns up in the queue. The worker will not recover and after a while ALL the workers will disappear as they randomly find the queue momentarily empty. The end result will be that the main multiprocessing function (the one with the join() on the processes) will return without all the tasks having completed. Nice. Good luck debugging through that if you have thousands of tasks and a few are missing.
The other issue is the use of sentinel values. Many people have suggested adding a sentinel value in the queue to flag the end of the queue. But to flag it to whom exactly? If there is N workers, assuming N is the number of cores available give or take, then a single sentinel value will only flag the end of the queue to one worker. All the other workers will sit waiting for more work when there is none left. Typical examples I've seen are
while True:
task = pending_queue.get()
if task == SOME_SENTINEL_VALUE:
break
One worker will get the sentinel value while the rest will wait indefinitely. No post I came across mentioned that you need to submit the sentinel value to the queue AT LEAST as many times as you have workers so that ALL of them get it.
The other issue is the handling of exceptions during task execution. Again these should be caught and managed. Moreover, if you have a completed_tasks queue you should independently count in a deterministic way how many items are in the queue before you decide that the job is done. Again relying on queue sizes is bound to fail and returns unexpected results.
In the example below, the par_proc() function will receive a list of tasks including the functions with which these tasks should be executed alongside any named arguments and values.
import multiprocessing as mp
import dill as pickle
import queue
import time
import psutil
SENTINEL = None
def do_work(tasks_pending, tasks_completed):
# Get the current worker's name
worker_name = mp.current_process().name
while True:
try:
task = tasks_pending.get_nowait()
except queue.Empty:
print(worker_name + ' found an empty queue. Sleeping for a while before checking again...')
time.sleep(0.01)
else:
try:
if task == SENTINEL:
print(worker_name + ' no more work left to be done. Exiting...')
break
print(worker_name + ' received some work... ')
time_start = time.perf_counter()
work_func = pickle.loads(task['func'])
result = work_func(**task['task'])
tasks_completed.put({work_func.__name__: result})
time_end = time.perf_counter() - time_start
print(worker_name + ' done in {} seconds'.format(round(time_end, 5)))
except Exception as e:
print(worker_name + ' task failed. ' + str(e))
tasks_completed.put({work_func.__name__: None})
def par_proc(job_list, num_cpus=None):
# Get the number of cores
if not num_cpus:
num_cpus = psutil.cpu_count(logical=False)
print('* Parallel processing')
print('* Running on {} cores'.format(num_cpus))
# Set-up the queues for sending and receiving data to/from the workers
tasks_pending = mp.Queue()
tasks_completed = mp.Queue()
# Gather processes and results here
processes = []
results = []
# Count tasks
num_tasks = 0
# Add the tasks to the queue
for job in job_list:
for task in job['tasks']:
expanded_job = {}
num_tasks = num_tasks + 1
expanded_job.update({'func': pickle.dumps(job['func'])})
expanded_job.update({'task': task})
tasks_pending.put(expanded_job)
# Use as many workers as there are cores (usually chokes the system so better use less)
num_workers = num_cpus
# We need as many sentinels as there are worker processes so that ALL processes exit when there is no more
# work left to be done.
for c in range(num_workers):
tasks_pending.put(SENTINEL)
print('* Number of tasks: {}'.format(num_tasks))
# Set-up and start the workers
for c in range(num_workers):
p = mp.Process(target=do_work, args=(tasks_pending, tasks_completed))
p.name = 'worker' + str(c)
processes.append(p)
p.start()
# Gather the results
completed_tasks_counter = 0
while completed_tasks_counter < num_tasks:
results.append(tasks_completed.get())
completed_tasks_counter = completed_tasks_counter + 1
for p in processes:
p.join()
return results
And here is a test to run the above code against
def test_parallel_processing():
def heavy_duty1(arg1, arg2, arg3):
return arg1 + arg2 + arg3
def heavy_duty2(arg1, arg2, arg3):
return arg1 * arg2 * arg3
task_list = [
{'func': heavy_duty1, 'tasks': [{'arg1': 1, 'arg2': 2, 'arg3': 3}, {'arg1': 1, 'arg2': 3, 'arg3': 5}]},
{'func': heavy_duty2, 'tasks': [{'arg1': 1, 'arg2': 2, 'arg3': 3}, {'arg1': 1, 'arg2': 3, 'arg3': 5}]},
]
results = par_proc(task_list)
job1 = sum([y for x in results if 'heavy_duty1' in x.keys() for y in list(x.values())])
job2 = sum([y for x in results if 'heavy_duty2' in x.keys() for y in list(x.values())])
assert job1 == 15
assert job2 == 21
plus another one with some exceptions
def test_parallel_processing_exceptions():
def heavy_duty1_raises(arg1, arg2, arg3):
raise ValueError('Exception raised')
return arg1 + arg2 + arg3
def heavy_duty2(arg1, arg2, arg3):
return arg1 * arg2 * arg3
task_list = [
{'func': heavy_duty1_raises, 'tasks': [{'arg1': 1, 'arg2': 2, 'arg3': 3}, {'arg1': 1, 'arg2': 3, 'arg3': 5}]},
{'func': heavy_duty2, 'tasks': [{'arg1': 1, 'arg2': 2, 'arg3': 3}, {'arg1': 1, 'arg2': 3, 'arg3': 5}]},
]
results = par_proc(task_list)
job1 = sum([y for x in results if 'heavy_duty1' in x.keys() for y in list(x.values())])
job2 = sum([y for x in results if 'heavy_duty2' in x.keys() for y in list(x.values())])
assert not job1
assert job2 == 21
Hope that is helpful.
in "from queue import Queue" there is no module called queue, instead multiprocessing should be used. Therefore, it should look like "from multiprocessing import Queue"
Just made a simple and general example for demonstrating passing a message over a Queue between 2 standalone programs. It doesn't directly answer the OP's question but should be clear enough indicating the concept.
Server:
multiprocessing-queue-manager-server.py
import asyncio
import concurrent.futures
import multiprocessing
import multiprocessing.managers
import queue
import sys
import threading
from typing import Any, AnyStr, Dict, Union
class QueueManager(multiprocessing.managers.BaseManager):
def get_queue(self, ident: Union[AnyStr, int, type(None)] = None) -> multiprocessing.Queue:
pass
def get_queue(ident: Union[AnyStr, int, type(None)] = None) -> multiprocessing.Queue:
global q
if not ident in q:
q[ident] = multiprocessing.Queue()
return q[ident]
q: Dict[Union[AnyStr, int, type(None)], multiprocessing.Queue] = dict()
delattr(QueueManager, 'get_queue')
def init_queue_manager_server():
if not hasattr(QueueManager, 'get_queue'):
QueueManager.register('get_queue', get_queue)
def serve(no: int, term_ev: threading.Event):
manager: QueueManager
with QueueManager(authkey=QueueManager.__name__.encode()) as manager:
print(f"Server address {no}: {manager.address}")
while not term_ev.is_set():
try:
item: Any = manager.get_queue().get(timeout=0.1)
print(f"Client {no}: {item} from {manager.address}")
except queue.Empty:
continue
async def main(n: int):
init_queue_manager_server()
term_ev: threading.Event = threading.Event()
executor: concurrent.futures.ThreadPoolExecutor = concurrent.futures.ThreadPoolExecutor()
i: int
for i in range(n):
asyncio.ensure_future(asyncio.get_running_loop().run_in_executor(executor, serve, i, term_ev))
# Gracefully shut down
try:
await asyncio.get_running_loop().create_future()
except asyncio.CancelledError:
term_ev.set()
executor.shutdown()
raise
if __name__ == '__main__':
asyncio.run(main(int(sys.argv[1])))
Client:
multiprocessing-queue-manager-client.py
import multiprocessing
import multiprocessing.managers
import os
import sys
from typing import AnyStr, Union
class QueueManager(multiprocessing.managers.BaseManager):
def get_queue(self, ident: Union[AnyStr, int, type(None)] = None) -> multiprocessing.Queue:
pass
delattr(QueueManager, 'get_queue')
def init_queue_manager_client():
if not hasattr(QueueManager, 'get_queue'):
QueueManager.register('get_queue')
def main():
init_queue_manager_client()
manager: QueueManager = QueueManager(sys.argv[1], authkey=QueueManager.__name__.encode())
manager.connect()
message = f"A message from {os.getpid()}"
print(f"Message to send: {message}")
manager.get_queue().put(message)
if __name__ == '__main__':
main()
Usage
Server:
$ python3 multiprocessing-queue-manager-server.py N
N is a integer indicating how many servers should be created. Copy one of the <server-address-N> output by the server and make it the first argument of each multiprocessing-queue-manager-client.py.
Client:
python3 multiprocessing-queue-manager-client.py <server-address-1>
Result
Server:
Client 1: <item> from <server-address-1>
Gist: https://gist.github.com/89062d639e40110c61c2f88018a8b0e5
UPD: Created a package here.
Server:
import ipcq
with ipcq.QueueManagerServer(address=ipcq.Address.AUTO, authkey=ipcq.AuthKey.AUTO) as server:
server.get_queue().get()
Client:
import ipcq
client = ipcq.QueueManagerClient(address=ipcq.Address.AUTO, authkey=ipcq.AuthKey.AUTO)
client.get_queue().put('a message')
We implemented two versions of this, one a simple multi thread pool that can execute many types of callables, making our lives much easier and the second version that uses processes, which is less flexible in terms of callables and requires and extra call to dill.
Setting frozen_pool to true will freeze execution until finish_pool_queue is called in either class.
Thread Version:
'''
Created on Nov 4, 2019
#author: Kevin
'''
from threading import Lock, Thread
from Queue import Queue
import traceback
from helium.loaders.loader_retailers import print_info
from time import sleep
import signal
import os
class ThreadPool(object):
def __init__(self, queue_threads, *args, **kwargs):
self.frozen_pool = kwargs.get('frozen_pool', False)
self.print_queue = kwargs.get('print_queue', True)
self.pool_results = []
self.lock = Lock()
self.queue_threads = queue_threads
self.queue = Queue()
self.threads = []
for i in range(self.queue_threads):
t = Thread(target=self.make_pool_call)
t.daemon = True
t.start()
self.threads.append(t)
def make_pool_call(self):
while True:
if self.frozen_pool:
#print '--> Queue is frozen'
sleep(1)
continue
item = self.queue.get()
if item is None:
break
call = item.get('call', None)
args = item.get('args', [])
kwargs = item.get('kwargs', {})
keep_results = item.get('keep_results', False)
try:
result = call(*args, **kwargs)
if keep_results:
self.lock.acquire()
self.pool_results.append((item, result))
self.lock.release()
except Exception as e:
self.lock.acquire()
print e
traceback.print_exc()
self.lock.release()
os.kill(os.getpid(), signal.SIGUSR1)
self.queue.task_done()
def finish_pool_queue(self):
self.frozen_pool = False
while self.queue.unfinished_tasks > 0:
if self.print_queue:
print_info('--> Thread pool... %s' % self.queue.unfinished_tasks)
sleep(5)
self.queue.join()
for i in range(self.queue_threads):
self.queue.put(None)
for t in self.threads:
t.join()
del self.threads[:]
def get_pool_results(self):
return self.pool_results
def clear_pool_results(self):
del self.pool_results[:]
Process Version:
'''
Created on Nov 4, 2019
#author: Kevin
'''
import traceback
from helium.loaders.loader_retailers import print_info
from time import sleep
import signal
import os
from multiprocessing import Queue, Process, Value, Array, JoinableQueue, Lock,\
RawArray, Manager
from dill import dill
import ctypes
from helium.misc.utils import ignore_exception
from mem_top import mem_top
import gc
class ProcessPool(object):
def __init__(self, queue_processes, *args, **kwargs):
self.frozen_pool = Value(ctypes.c_bool, kwargs.get('frozen_pool', False))
self.print_queue = kwargs.get('print_queue', True)
self.manager = Manager()
self.pool_results = self.manager.list()
self.queue_processes = queue_processes
self.queue = JoinableQueue()
self.processes = []
for i in range(self.queue_processes):
p = Process(target=self.make_pool_call)
p.start()
self.processes.append(p)
print 'Processes', self.queue_processes
def make_pool_call(self):
while True:
if self.frozen_pool.value:
sleep(1)
continue
item_pickled = self.queue.get()
if item_pickled is None:
#print '--> Ending'
self.queue.task_done()
break
item = dill.loads(item_pickled)
call = item.get('call', None)
args = item.get('args', [])
kwargs = item.get('kwargs', {})
keep_results = item.get('keep_results', False)
try:
result = call(*args, **kwargs)
if keep_results:
self.pool_results.append(dill.dumps((item, result)))
else:
del call, args, kwargs, keep_results, item, result
except Exception as e:
print e
traceback.print_exc()
os.kill(os.getpid(), signal.SIGUSR1)
self.queue.task_done()
def finish_pool_queue(self, callable=None):
self.frozen_pool.value = False
while self.queue._unfinished_tasks.get_value() > 0:
if self.print_queue:
print_info('--> Process pool... %s' % (self.queue._unfinished_tasks.get_value()))
if callable:
callable()
sleep(5)
for i in range(self.queue_processes):
self.queue.put(None)
self.queue.join()
self.queue.close()
for p in self.processes:
with ignore_exception: p.join(10)
with ignore_exception: p.terminate()
with ignore_exception: del self.processes[:]
def get_pool_results(self):
return self.pool_results
def clear_pool_results(self):
del self.pool_results[:]
def test(eg):
print 'EG', eg
Call with either:
tp = ThreadPool(queue_threads=2)
tp.queue.put({'call': test, 'args': [random.randint(0, 100)]})
tp.finish_pool_queue()
or
pp = ProcessPool(queue_processes=2)
pp.queue.put(dill.dumps({'call': test, 'args': [random.randint(0, 100)]}))
pp.queue.put(dill.dumps({'call': test, 'args': [random.randint(0, 100)]}))
pp.finish_pool_queue()
A multi-producers and multi-consumers example, verified. It should be easy to modify it to cover other cases, single/multi producers, single/multi consumers.
from multiprocessing import Process, JoinableQueue
import time
import os
q = JoinableQueue()
def producer():
for item in range(30):
time.sleep(2)
q.put(item)
pid = os.getpid()
print(f'producer {pid} done')
def worker():
while True:
item = q.get()
pid = os.getpid()
print(f'pid {pid} Working on {item}')
print(f'pid {pid} Finished {item}')
q.task_done()
for i in range(5):
p = Process(target=worker, daemon=True).start()
# send thirty task requests to the worker
producers = []
for i in range(2):
p = Process(target=producer)
producers.append(p)
p.start()
# make sure producers done
for p in producers:
p.join()
# block until all workers are done
q.join()
print('All work completed')
Explanation:
Two producers and five consumers in this example.
JoinableQueue is used to make sure all elements stored in queue will be processed. 'task_done' is for worker to notify an element is done. 'q.join()' will wait for all elements marked as done.
With #2, there is no need to join wait for every worker.
But it is important to join wait for every producer to store element into queue. Otherwise, program exit immediately.