According to the documentation, no such control over pooled processes exists. You could however, simulate it with a lock:
import multiprocessing
import time
lock = multiprocessing.Lock()
def worker(nr):
lock.acquire()
time.sleep(0.100)
lock.release()
print(nr)
numbers = [i
for i in range(20)
]
if __name__ == '__main__':
multiprocessing.freeze_support()
pool = multiprocessing.Pool(processes = 3)
results = pool.map(worker, numbers)
pool.close()
pool.join()Note that on Windows it's not possible to inherit a lock from a parent process. Instead, you can use multiprocessing.Manager().Lock() to communicate a global lock object between processes (with additional IPC overhead, of course). The global lock object needs to be initialized in each process, as well. This would look like:
from multiprocessing
import Process, freeze_support
import multiprocessing
import time
from datetime
import datetime as dt
def worker(nr):
glock.acquire()
print('started job: {} at {}'.format(nr, dt.now()))
time.sleep(1)
glock.release()
print('ended job: {} at {}'.format(nr, dt.now()))
numbers = [i
for i in range(6)
]
def init(lock):
global glock
glock = lock
if __name__ == '__main__':
multiprocessing.freeze_support()
lock = multiprocessing.Manager().Lock()
pool = multiprocessing.Pool(processes = 3, initializer = init, initargs = (lock, ))
results = pool.map(worker, numbers)
pool.close()
pool.join()Couldn't you do something simple like this:
from multiprocessing
import Process
from time
import sleep
def f(n):
print 'started job: ' + str(n)
sleep(3)
print 'ended job: ' + str(n)
if __name__ == '__main__':
for i in range(0, 100):
p = Process(target = f, args = (i, ))
p.start()
sleep(1)Result
started job: 0 started job: 1 started job: 2 ended job: 0 started job: 3 ended job: 1 started job: 4 ended job: 2 started job: 5
could you try defining a function that yields your values slowly?
def get_numbers_on_delay(numbers, delay):
for i in numbers:
yield i
time.sleep(delay)and then:
results = pool.map(worker, get_numbers_on_delay(numbers, 5))
It spawns all the processes similar to the locking solution, but sleeps before work based on their process name number.
from multiprocessing
import current_process
from re
import search
from time
import sleep
def worker():
process_number = search('\d+', current_process().name).group()
time_between_workers = 5
sleep(time_between_workers * int(process_number))
#do your work hereThe Pool class represents a pool of worker processes. It has methods which allows tasks to be offloaded to the worker processes in a few different ways.,In particular, the Pool function provided by multiprocessing.dummy returns an instance of ThreadPool, which is a subclass of Pool that supports all the same method calls but uses a pool of worker threads rather than worker processes.,Process objects represent activity that is run in a separate process. The Process class has equivalents of all the methods of threading.Thread.,Depending on the platform, multiprocessing supports three ways to start a process. These start methods are
from multiprocessing
import Pool
def f(x):
return x * x
if __name__ == '__main__':
with Pool(5) as p:
print(p.map(f, [1, 2, 3]))[1, 4, 9]from multiprocessing
import Process
def f(name):
print('hello', name)
if __name__ == '__main__':
p = Process(target = f, args = ('bob', ))
p.start()
p.join()from multiprocessing
import Process
import os
def info(title):
print(title)
print('module name:', __name__)
print('parent process:', os.getppid())
print('process id:', os.getpid())
def f(name):
info('function f')
print('hello', name)
if __name__ == '__main__':
info('main line')
p = Process(target = f, args = ('bob', ))
p.start()
p.join()import multiprocessing as mp
def foo(q):
q.put('hello')
if __name__ == '__main__':
mp.set_start_method('spawn')
q = mp.Queue()
p = mp.Process(target = foo, args = (q, ))
p.start()
print(q.get())
p.join()import multiprocessing as mp
def foo(q):
q.put('hello')
if __name__ == '__main__':
ctx = mp.get_context('spawn')
q = ctx.Queue()
p = ctx.Process(target = foo, args = (q, ))
p.start()
print(q.get())
p.join()Python multiprocessing tutorial is an introductory tutorial to process-based parallelism in Python. , True parallelism in Python is achieved by creating multiple processes, each having a Python interpreter with its own separate GIL. , In multiprocessing, there is no guarantee that the processes finish in a certain order. , The following is a simple program that uses multiprocessing.
#!/usr/bin/python
from multiprocessing
import Process
def fun(name):
print(f 'hello {name}')
def main():
p = Process(target = fun, args = ('Peter', ))
p.start()
if __name__ == '__main__':
main()We create a new process and pass a value to it.
def fun(name):
print(f 'hello {name}')The function prints the passed parameter.
def main():
p = Process(target = fun, args = ('Peter', ))
p.start()#!/usr/bin/python
from multiprocessing
import Process
import time
def fun():
print('starting fun')
time.sleep(2)
print('finishing fun')
def main():
p = Process(target = fun)
p.start()
p.join()
if __name__ == '__main__':
print('starting main')
main()
print('finishing main')
The example calls the join on the newly created process.
$. / joining.py
starting main
starting fun
finishing fun
finishing mainThe John A. Dutton e-Education Institute is the learning design unit of the College of Earth and Mineral Sciences at The Pennsylvania State University.,Here the Pool variable is created without the with ... as ... statement. As a result, the statements in the last two lines are needed for telling Python that we are done adding jobs to the pool and for cleaning up all sub-processes when we are done to free up resources. We prefer to use the version using the with ... as ... construct in this course.,Our mp_handler() function is very simple. It has two main lines of code based on the multiprocessing module:,There are a couple of basic steps we need to add to our code in order to support multiprocessing. The first is that our code needs to import multiprocessing which is a Python library which as you will have guessed from the name enables multiprocessing support. We’ll add that as the first line of our code.
The second thing our code needs to have is a __main__ method defined. We’ll add that into our code at the very bottom with:
if __name__ == '__main__':
mp_handler()The first instantiates a pool with a number of workers (usually our number of processors or a number slightly less than our number of processors). There’s a function to determine how many processors we have, multiprocessing.cpu_count(), so that our code can take full advantage of whichever machine it is running on. That first line is:
with multiprocessing.Pool(multiprocessing.cpu_count()) as myPool: ...# code for setting up the pool of jobs
You have probably already seen this notation from working with arcpy cursors. This with ... as ... statement creates an object of the Pool class defined in the multiprocessing module and assigns it to variable myPool. The parameter given to it is the number of processors on my machine (which is the value that multiprocessing.cpu_count() is returning), so here we are making sure that all processor cores will be used. All code that uses the variable myPool (e.g., for setting up the pool of multiprocessing jobs) now needs to be indented relative to the "with" and the construct makes sure that everything is cleaned up afterwards. The same could be achieved with the following lines of code:
myPool = multiprocessing.Pool(multiprocessing.cpu_count()) ...# code for setting up the pool of jobs myPool.close() myPool.join()
Here’s what our revised function will look like :
def cherryO(game):
spinnerChoices = [-1, -2, -3, -4, 2, 2, 10]
turns = 0
cherriesOnTree = 10
# Take a turn as long as you have more than 0 cherries
while cherriesOnTree > 0:
# Spin the spinner
spinIndex = random.randrange(0, 7)
spinResult = spinnerChoices[spinIndex]
# Print the spin result
#print("You spun " + str(spinResult) + ".")
# Add or remove cherries based on the result
cherriesOnTree += spinResult
# Make sure the number of cherries is between 0 and 10
if cherriesOnTree > 10:
cherriesOnTree = 10
elif cherriesOnTree < 0:
cherriesOnTree = 0
# Print the number of cherries on the tree
#print("You have " + str(cherriesOnTree) + " cherries on your tree.")
turns += 1
#
return the number of turns it took to win the game
return (turns)Hopefully the Python threading examples in this article—and update—will point you in the right direction so you have an idea of where to look in the Python standard library if you need to introduce concurrency into your programs.,Now that we have all these images downloaded with our Python ThreadPoolExecutor, we can use them to test a CPU-bound task. We can create thumbnail versions of all the images in both a single-threaded, single-process script and then test a multiprocessing-based solution.,Something new since Python 3.2 that wasn’t touched upon in the original article is the concurrent.futures package. This package provides yet another way to use concurrency and parallelism with Python.,All subsequent code examples will only show import statements that are new and specific to those examples. For convenience, all of these Python scripts can be found in this GitHub repository.
This is what the script looks like:
import json
import logging
import os
from pathlib
import Path
from urllib.request
import urlopen, Request
logger = logging.getLogger(__name__)
types = {
'image/jpeg',
'image/png'
}
def get_links(client_id):
headers = {
'Authorization': 'Client-ID {}'.format(client_id)
}
req = Request('https://api.imgur.com/3/gallery/random/random/', headers = headers, method = 'GET')
with urlopen(req) as resp:
data = json.loads(resp.read().decode('utf-8'))
return [item['link']
for item in data['data']
if 'type' in item and item['type'] in types
]
def download_link(directory, link):
download_path = directory / os.path.basename(link)
with urlopen(link) as image, download_path.open('wb') as f:
f.write(image.read())
logger.info('Downloaded %s', link)
def setup_download_dir():
download_dir = Path('images')
if not download_dir.exists():
download_dir.mkdir()
return download_dirNext, we will need to write a module that will use these functions to download the images, one by one. We will name this single.py. This will contain the main function of our first, naive version of the Imgur image downloader. The module will retrieve the Imgur client ID in the environment variable IMGUR_CLIENT_ID. It will invoke the setup_download_dir to create the download destination directory. Finally, it will fetch a list of images using the get_links function, filter out all GIF and album URLs, and then use download_link to download and save each of those images to the disk. Here is what single.py looks like:
import logging
import os
from time
import time
from download
import setup_download_dir, get_links, download_link
logging.basicConfig(level = logging.INFO, format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def main():
ts = time()
client_id = os.getenv('IMGUR_CLIENT_ID')
if not client_id:
raise Exception("Couldn't find IMGUR_CLIENT_ID environment variable!")
download_dir = setup_download_dir()
links = get_links(client_id)
for link in links:
download_link(download_dir, link)
logging.info('Took %s seconds', time() - ts)
if __name__ == '__main__':
main()This is almost the same as the previous one, with the exception that we now have a new class, DownloadWorker, which is a descendent of the Python Thread class. The run method has been overridden, which runs an infinite loop. On every iteration, it calls self.queue.get() to try and fetch a URL to from a thread-safe queue. It blocks until there is an item in the queue for the worker to process. Once the worker receives an item from the queue, it then calls the same download_link method that was used in the previous script to download the image to the images directory. After the download is finished, the worker signals the queue that that task is done. This is very important, because the Queue keeps track of how many tasks were enqueued. The call to queue.join() would block the main thread forever if the workers did not signal that they completed a task.
import logging
import os
from queue
import Queue
from threading
import Thread
from time
import time
from download
import setup_download_dir, get_links, download_link
logging.basicConfig(level = logging.INFO, format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class DownloadWorker(Thread):
def __init__(self, queue):
Thread.__init__(self)
self.queue = queue
def run(self):
while True:
# Get the work from the queue and expand the tuple
directory, link = self.queue.get()
try:
download_link(directory, link)
finally:
self.queue.task_done()
def main():
ts = time()
client_id = os.getenv('IMGUR_CLIENT_ID')
if not client_id:
raise Exception("Couldn't find IMGUR_CLIENT_ID environment variable!")
download_dir = setup_download_dir()
links = get_links(client_id)
# Create a queue to communicate with the worker threads
queue = Queue()
# Create 8 worker threads
for x in range(8):
worker = DownloadWorker(queue)
# Setting daemon to True will
let the main thread exit even though the workers are blocking
worker.daemon = True
worker.start()
# Put the tasks into the queue as a tuple
for link in links:
logger.info('Queueing {}'.format(link))
queue.put((download_dir, link))
# Causes the main thread to wait
for the queue to finish processing all the tasks
queue.join()
logging.info('Took %s', time() - ts)
if __name__ == '__main__':
main()One last step we need to do is to start up some workers. RQ provides a handy script to run workers on the default queue. Just run rqworker in a terminal window and it will start a worker listening on the default queue. Please make sure your current working directory is the same as where the scripts reside in. If you want to listen to a different queue, you can run rqworker queue_name and it will listen to that named queue. The great thing about RQ is that as long as you can connect to Redis, you can run as many workers as you like on as many different machines as you like; therefore, it is very easy to scale up as your application grows. Here is the source for the RQ version:
import logging
import os
from redis
import Redis
from rq
import Queue
from download
import setup_download_dir, get_links, download_link
logging.basicConfig(level = logging.DEBUG, format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logging.getLogger('requests').setLevel(logging.CRITICAL)
logger = logging.getLogger(__name__)
def main():
client_id = os.getenv('IMGUR_CLIENT_ID')
if not client_id:
raise Exception("Couldn't find IMGUR_CLIENT_ID environment variable!")
download_dir = setup_download_dir()
links = get_links(client_id)
q = Queue(connection = Redis(host = 'localhost', port = 6379))
for link in links:
q.enqueue(download_link, download_dir, link)
if __name__ == '__main__':
main()Using a concurrent.futures.ThreadPoolExecutor makes the Python threading example code almost identical to the multiprocessing module.
import logging
import os
from concurrent.futures
import ThreadPoolExecutor
from functools
import partial
from time
import time
from download
import setup_download_dir, get_links, download_link
logging.basicConfig(level = logging.INFO, format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def main():
client_id = os.getenv('IMGUR_CLIENT_ID')
if not client_id:
raise Exception("Couldn't find IMGUR_CLIENT_ID environment variable!")
download_dir = setup_download_dir()
links = get_links(client_id)
# By placing the executor inside a with block, the executors shutdown method
# will be called cleaning up threads.
#
# By
default, the executor sets number of workers to 5 times the number of
# CPUs.
with ThreadPoolExecutor() as executor:
# Create a new partially applied
function that stores the directory
# argument.
#
# This allows the download_link
function that normally takes two
# arguments to work with the map
function that expects a
function of a
# single argument.
fn = partial(download_link, download_dir)
# Executes fn concurrently using threads on the links iterable.The
# timeout is
for the entire process, not a single call, so downloading
# all images must complete within 30 seconds.
executor.map(fn, links, timeout = 30)
if __name__ == '__main__':
main()06/13/2022
This YAML file has a job that runs on a Microsoft-hosted agent and outputs Hello world.
pool:
vmImage: 'ubuntu-latest'
steps:
-bash: echo "Hello world"You may want to specify additional properties on that job. In that case, you can use the job keyword.
jobs:
-job: myJob
timeoutInMinutes: 10
pool:
vmImage: 'ubuntu-latest'
steps:
-bash: echo "Hello world"Your pipeline may have multiple jobs. In that case, use the jobs keyword.
jobs:
-job: A
steps:
-bash: echo "A"
-
job: B
steps:
-bash: echo "B"Your pipeline may have multiple stages, each with multiple jobs. In that case, use the stages keyword.
stages: -stage: A jobs: -job: A1 - job: A2 - stage: B jobs: -job: B1 - job: B2
The full syntax to specify a job is:
-job: string # name of the job, A - Z, a - z, 0 - 9, and underscore
displayName: string # friendly name to display in the UI
dependsOn: string | [string]
condition: string
strategy:
parallel: # parallel strategy
matrix: # matrix strategy
maxParallel: number # maximum number simultaneous matrix legs to run
# note: `parallel`
and `matrix`
are mutually exclusive
# you may specify one or the other;
including both is an error
# `maxParallel`
is only valid with `matrix`
continueOnError: boolean # 'true'
if future jobs should run even
if this job fails;
defaults to 'false'
pool: pool # agent pool
workspace:
clean: outputs | resources | all # what to clean up before the job runs
container: containerReference # container to run this job inside
timeoutInMinutes: number # how long to run the job before automatically cancelling
cancelTimeoutInMinutes: number # how much time to give 'run always even if cancelled tasks'
before killing them
variables: {
string: string
} | [variable | variableReference]
steps: [script | bash | pwsh | powershell | checkout | task | templateReference]
services: {
string: string | container
}
# container resources to run as a service containerThe full syntax to specify a job is:
-job: string # name of the job, A - Z, a - z, 0 - 9, and underscore
displayName: string # friendly name to display in the UI
dependsOn: string | [string]
condition: string
strategy:
parallel: # parallel strategy
matrix: # matrix strategy
maxParallel: number # maximum number simultaneous matrix legs to run
# note: `parallel`
and `matrix`
are mutually exclusive
# you may specify one or the other;
including both is an error
# `maxParallel`
is only valid with `matrix`
continueOnError: boolean # 'true'
if future jobs should run even
if this job fails;
defaults to 'false'
pool: pool # agent pool
workspace:
clean: outputs | resources | all # what to clean up before the job runs
container: containerReference # container to run this job inside
timeoutInMinutes: number # how long to run the job before automatically cancelling
cancelTimeoutInMinutes: number # how much time to give 'run always even if cancelled tasks'
before killing them
variables: {
string: string
} | [variable | variableReference]
steps: [script | bash | pwsh | powershell | checkout | task | templateReference]
services: {
string: string | container
}
# container resources to run as a service container
uses: # Any resources(repos or pools) required by this job that are not already referenced
repositories: [string] # Repository references to Azure Git repositories
pools: [string] # Pool names, typically when using a matrix strategy
for the jobThe syntax for a deployment job is:
-deployment: string # instead of job keyword, use deployment keyword pool: name: string demands: string | [string] environment: string strategy: runOnce: deploy: steps: -script: echo Hi!
pool: name: myPrivateAgents # your job runs on an agent in this pool demands: agent.os - equals Windows_NT # the agent must have this capability to run the job steps: -script: echo hello world
Or multiple demands:
pool: name: myPrivateAgents demands: -agent.os - equals Darwin - anotherCapability - equals somethingElse steps: -script: echo hello world
Traditional ( heavyweight ) processes have a single thread of control - There is one program counter, and one sequence of instructions that can be carried out at any given time.,As shown in Figure 4.1, multi-threaded applications have multiple threads within a single process, each having their own program counter, stack and set of registers, but sharing common code, data, and certain structures such as open files. ,Resource sharing - By default threads share common code, data, and other resources, which allows multiple tasks to be performed simultaneously in a single address space.,Economy - Creating and managing threads ( and context switches between them ) is much faster than performing the same tasks for processes.
#pragma omp parallel {
/* some parallel code here */
}