Background
I have a collection of Python scripts used to build and execute Verilog-AMS tesbenches. The overall design was built with threading in mind, as each major test case is its own testbench and I have all of the supporting files / data output separate for each instance. The only shared items will be the launcher script and my data extraction script. The problem that I'm faced with is that my Verilog-AMS simulator does not natively support multithreading and for my test cases it takes a substantial amount of time to complete.
Problem
The machine I'm running this on has 32GiB of RAM and 8 "cores" available for me to use and I may be able to access a machine with 32. I would like to take advantage of the available computing power and execute the simulations simultaneously. What would be the best approach?
I currently use subprocess.call to execute my simulation. I would like to execute up to n commands at once, with each one executing on a separate thread / as a separate process. Once a simulation has completed, the next one in the queue (if one exists) would execute.
I'm pretty new to Python and haven't really written a threaded application. I would like some advice on how I should proceed. I saw this question, and from that I think the multiprocessing module may be better suited to my needs.
What do you all recommend?
I had done some similar task in the past with Machine Learning and Data Mining. Using multiprocessing in your case may not be that difficult of a task. It depends on how tolerant you are keen on making the program, you can use a Threaded Pool pattern. My personal favourite is Producer - Consumer pattern using Queue, this design can handle a variety of complex task. Here is a sample toy program using multiprocessing:
import multiprocessing
from multiprocessing import Queue, Process
from Queue import Empty as QueueEmpty
# Assuming this text is very very very very large
text="Here I am writing some nonsense\nBut people will read\n..."
def read(q):
"""Read the text and put in a queue"""
for line in text.split("\n"):
q.put(line)
def work(qi, qo):
"""Put the line into the queue out"""
while True:
try:
data = qi.get(timeout = 1) # Timeout after 1 second
qo.put(data)
except QueueEmpty:
return # Exit when all work is done
except:
raise # Raise all other errors
def join(q):
"""Join all the output queue and write to a text file"""
f = open("file.txt", w)
while True:
try:
f.write(q.get(timeout=1))
except QueueEmpty:
f.close()
return
except:
raise
def main():
# Input queue
qi = Queue()
# Output queue
qo = Queue()
# Start the producer
Process(target = read, args = (qi, )).start()
# Start 8 consumers
for i in range(8):
Process(target = work, args = (qi, qo, )).start()
# Final process to handle the queue out
Process(target = join, args = (qo, )).start()
Type this from memory so if there is any error, please correct. :)
Related
So I have two webscrapers that collect data from two different sources. I am running them both simultaneously to collect a specific piece of data (e.g. covid numbers).
When one of the functions finds data I want to use that data without waiting for the other one to finish.
So far I tried the multiprocessing - pool module and to return the results with get() but by definition I have to wait for both get() to finish before I can continue with my code. My goal is to have the code as simple and as short as possible.
My webscraper functions can be run with arguments and return a result if found. It is also possible to modify them.
The code I have so far which waits for both get() to finish.
from multiprocessing import Pool
from scraper1 import main_1
from scraper2 import main_2
from twitter import post_tweet
if __name__ == '__main__':
with Pool(processes=2) as pool:
r1 = pool.apply_async(main_1, ('www.website1.com','June'))
r2 = pool.apply_async(main_2, ())
data = r1.get()
data2 = r2.get()
post_tweet("New data is {}".format(data))
post_tweet("New data is {}".format(data2))
From here I have seen that threading might be a better option since webscraping involves a lot of waiting and only little parsing but I am not sure how I would implement this.
I think the solution is fairly easy but I have been searching and trying different things all day without much success so I think I will just ask here. (I only started programming 2 months ago)
As always there are many ways to accomplish this task.
you have already mentioned using a Queue:
from multiprocessing import Process, Queue
from scraper1 import main_1
from scraper2 import main_2
def simple_worker(target, args, ret_q):
ret_q.put(target(*args)) # mp.Queue has it's own mutex so we don't need to worry about concurrent read/write
if __name__ == "__main__":
q = Queue()
p1 = Process(target=simple_worker, args=(main_1, ('www.website1.com','June'), q))
p2 = Process(target=simple_worker, args=(main_2, ('www.website2.com','July'), q))
p1.start()
p2.start()
first_result = q.get()
do_stuff(first_result)
#don't forget to get() the second result before you quit. It's not a good idea to
#leave things in a Queue and just assume it will be properly cleaned up at exit.
second_result = q.get()
p1.join()
p2.join()
You could also still use a Pool by using imap_unordered and just taking the first result:
from multiprocessing import Pool
from scraper1 import main_1
from scraper2 import main_2
def simple_worker2(args):
target, arglist = args #unpack args
return target(*arglist)
if __name__ == "__main__":
tasks = ((main_1, ('www.website1.com','June')),
(main_2, ('www.website2.com','July')))
with Pool() as p: #Pool context manager handles worker cleanup (your target function may however be interrupted at any point if the pool exits before a task is complete
for result in p.imap_unordered(simple_worker2, tasks, chunksize=1):
do_stuff(result)
break #don't bother with further results
I've seen people use queues in such cases: create one and pass it to both parsers so that they put their results in queue instead of returning them. Then do a blocking pop on the queue to retrieve the first available result.
I have seen that threading might be a better option
Almost true but not quite. I'd say that asyncio and async-based libraries is much better than both threading and multiprocessing when we're talking about code with a lot of blocking I/O. If it's applicable in your case, I'd recommend rewriting both your parsers in async.
Objective
a process (.exe) with multiple input arguments
Multiple files. For each the above mentioned process shall be executed
I want to use python to parallelize the process
I am using subprocess.Popen to create the processes and afterwards keep a maximum of N parallel processes.
For testing purposes, I want to parallelize a simple script like "cmd timeout 5".
State of work
import subprocess
count = 10
parallel = 2
processes = []
for i in range(0,count):
while (len(processes) >= parallel):
for process in processes:
if (process.poll() is None):
processes.remove(process)
break
process = subprocess.Popen(["cmd", "/c timeout 5"])
processes.append(process)
[...]
I read somewhere that a good approach for checking if a process is running would be is not None like shown in the code.
Question
I am somehow struggling to set it up correctly, especially the Popen([...]) part. In some cases, all processes are executed without considering the maximum parallel count and in other cases, it doesnt work at all.
I guess that there has to be a part where the process is closed if finished.
Thanks!
You will probably have a better time using the built-in multiprocessing module to manage the subprocesses running your tasks.
The reason I've wrapped the command in a dict is that imap_unordered (which is faster than imap but doesn't guarantee ordered execution since any worker process can grab any job – whether that's okay for you is your business problem) doesn't have a starmap alternative, so it's easier to unpack a single "job" within the callable.
import multiprocessing
import subprocess
def run_command(job):
# TODO: add other things here?
subprocess.check_call(job["command"])
def main():
with multiprocessing.Pool(2) as p:
jobs = [{"command": ["cmd", "/c timeout 5"]} for x in range(10)]
for result in p.imap_unordered(run_command, jobs):
pass
if __name__ == "__main__":
main()
I frequently use the pattern below to parallelify tasks in python. I do it this way because filling the input queue is quick, and once the processes are launched and running asynchronously, I can call a blocking get() in a loop and pull the results out as they are ready. For tasks which take days, this is great because I can do things like report progress.
from multiprocessing import Process, Queue
class worker():
def __init__(self, init_dict,):
self.init_dict = init_dict
def __call__(self, task_queue, done_queue):
for task_args in task_queue.get()
task_result = self.do_work(task_args)
done_queue.put(task_result)
if __name__=="__main__":
n_threads = 8
init_dict = {} # whatever we need to setup our class
worker_class = worker(init_dict)
task_queue = Queue()
done_queue = Queue()
some_iterator = [1,2,3,4,5] # or a list of files to chew through normally
for task in some_iterator:
task_queue.put(task)
for i in range(n_threads):
Process(target=worker_class, args=(task_queue, done_queue)).start()
for i in range(len(some_iterator)):
result = done_queue.get()
# do something with result
# print out progress stats, whatever, as tasks complete
I have glossed over a few detail like catching errors, dealing with things that fail, killing zombie process, exiting at the end of the task queue and catching tracebacks, but you get the idea. I really love this pattern and it works perfectly for my needs. I have a lot of code that uses it.
I need more computing power though and want to spread the work across a cluster. Ray offers a multiprocessing pool with an API that matches that of python multiprocessing. I just can't work out how to get the above pattern to work. Mainly I get:
RuntimeError: Queue objects should only be shared between processes through inheritance
Does anybody have any recommendations of how I can get results as they are ready from a queue when using a pool, rather than n separate processes?
I appreciate that if I do a massive rewrite, then there are probably other ways to get what I want from ray, but I have a lot of code like this, so want to try and keep changes minimal.
Thanks
My goal is create one main python script that executes multiple independent python scripts in windows server 2012 at the same time. One of the benefits in my mind is that I can point taskscheduler to one main.py script as opposed to multiple .py scripts. My server has 1 cpu. I have read on multiprocessing,thread & subprocess which only added to my confusion a bit. I am basically running multiple trading scripts for different stock symbols all at the same time after market open at 9:30 EST. Following is my attempt but I have no idea whether this is right. Any direction/feedback is highly appreciated!
import subprocess
subprocess.Popen(["python", '1.py'])
subprocess.Popen(["python", '2.py'])
subprocess.Popen(["python", '3.py'])
subprocess.Popen(["python", '4.py'])
I think I'd try to do this like that:
from multiprocessing import Pool
def do_stuff_with_stock_symbol(symbol):
return _call_api()
if __name__ == '__main__':
symbols = ["GOOG", "APPL", "TSLA"]
p = Pool(len(symbols))
results = p.map(do_stuff_with_stock_symbol, symbols)
print(results)
(Modified example from multiprocessing introduction: https://docs.python.org/3/library/multiprocessing.html#introduction)
Consider using a constant pool size if you deal with a lot of stock symbols, because every python process will use some amount of memory.
Also, please note that using threads might be a lot better if you are dealing with an I/O bound workload (calling an API, writing and reading from disk). Processes really become necessary with python when dealing with compute bound workloads (because of the global interpreter lock).
An example using threads and the concurrent futures library would be:
import concurrent.futures
TIMEOUT = 60
def do_stuff_with_stock_symbol(symbol):
return _call_api()
if __name__ == '__main__':
symbols = ["GOOG", "APPL", "TSLA"]
with concurrent.futures.ThreadPoolExecutor(max_workers=len(symbols)) as executor:
results = {executor.submit(do_stuff_with_stock_symbol, symbol, TIMEOUT): symbol for symbol in symbols}
for future in concurrent.futures.as_completed(results):
symbol = results[future]
try:
data = future.result()
except Exception as exc:
print('{} generated an exception: {}'.format(symbol, exc))
else:
print('stock symbol: {}, result: {}'.format(symbol, data))
(Modified example from: https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example)
Note that threads will still use some memory, but less than processes.
You could use asyncio or green threads if you want to reduce memory consumption per stock symbol to a minimum, but at some point you will run into network bandwidth problems because of all the concurrent API calls :)
While what you're asking might not be the best way to handle what you're doing, I've wanted to do similar things in the past and it took a while to find what I needed so to answer your question:
I'm not promising this to be the "best" way to do it, but it worked in my use case.
I created a class I wanted to use to extend threading.
thread.py
"""
Extends threading.Thread giving access to a Thread object which will accept
A thread_id, thread name, and a function at the time of instantiation. The
function will be called when the threads start() method is called.
"""
import threading
class Thread(threading.Thread):
def __init__(self, thread_id, name, func):
threading.Thread.__init__(self)
self.threadID = thread_id
self.name = name
# the function that should be run in the thread.
self.func = func
def run(self):
return self.func()
I needed some work done that was part of another package
work_module.py
import...
def func_that_does_work():
# do some work
pass
def more_work():
# do some work
pass
Then the main script I wanted to run
main.py
from thread import Thread
import work_module as wm
mythreads = []
mythreads.append(Thread(1, "a_name", wm.func_that_does_work))
mythreads.append(Thread(2, "another_name", wm.more_work))
for t in mythreads:
t.start()
The threads die when the run() is returned. Being this extends a Thread from threading there are several options available in the docs here: https://docs.python.org/3/library/threading.html
If all you're looking to do is automate the startup, creating a .bat file is a great and simple alternative to trying to do it with another python script.
the example linked in the comments shows how to do it with bash on unix based machines, but batch files can do a very similar thing with the START command:
start_py.bat:
START "" /B "path\to\python.exe" "path\to\script_1.py"
START "" /B "path\to\python.exe" "path\to\script_2.py"
START "" /B "path\to\python.exe" "path\to\script_3.py"
the full syntax for START can be found here.
Suppose I have the following in Python
# A loop
for i in range(10000):
Do Task A
# B loop
for i in range(10000):
Do Task B
How do I run these loops simultaneously in Python?
If you want concurrency, here's a very simple example:
from multiprocessing import Process
def loop_a():
while 1:
print("a")
def loop_b():
while 1:
print("b")
if __name__ == '__main__':
Process(target=loop_a).start()
Process(target=loop_b).start()
This is just the most basic example I could think of. Be sure to read http://docs.python.org/library/multiprocessing.html to understand what's happening.
If you want to send data back to the program, I'd recommend using a Queue (which in my experience is easiest to use).
You can use a thread instead if you don't mind the global interpreter lock. Processes are more expensive to instantiate but they offer true concurrency.
There are many possible options for what you wanted:
use loop
As many people have pointed out, this is the simplest way.
for i in xrange(10000):
# use xrange instead of range
taskA()
taskB()
Merits: easy to understand and use, no extra library needed.
Drawbacks: taskB must be done after taskA, or otherwise. They can't be running simultaneously.
multiprocess
Another thought would be: run two processes at the same time, python provides multiprocess library, the following is a simple example:
from multiprocessing import Process
p1 = Process(target=taskA, args=(*args, **kwargs))
p2 = Process(target=taskB, args=(*args, **kwargs))
p1.start()
p2.start()
merits: task can be run simultaneously in the background, you can control tasks(end, stop them etc), tasks can exchange data, can be synchronized if they compete the same resources etc.
drawbacks: too heavy!OS will frequently switch between them, they have their own data space even if data is redundant. If you have a lot tasks (say 100 or more), it's not what you want.
threading
threading is like process, just lightweight. check out this post. Their usage is quite similar:
import threading
p1 = threading.Thread(target=taskA, args=(*args, **kwargs))
p2 = threading.Thread(target=taskB, args=(*args, **kwargs))
p1.start()
p2.start()
coroutines
libraries like greenlet and gevent provides something called coroutines, which is supposed to be faster than threading. No examples provided, please google how to use them if you're interested.
merits: more flexible and lightweight
drawbacks: extra library needed, learning curve.
Why do you want to run the two processes at the same time? Is it because you think they will go faster (there is a good chance that they wont). Why not run the tasks in the same loop, e.g.
for i in range(10000):
doTaskA()
doTaskB()
The obvious answer to your question is to use threads - see the python threading module. However threading is a big subject and has many pitfalls, so read up on it before you go down that route.
Alternatively you could run the tasks in separate proccesses, using the python multiprocessing module. If both tasks are CPU intensive this will make better use of multiple cores on your computer.
There are other options such as coroutines, stackless tasklets, greenlets, CSP etc, but Without knowing more about Task A and Task B and why they need to be run at the same time it is impossible to give a more specific answer.
from threading import Thread
def loopA():
for i in range(10000):
#Do task A
def loopB():
for i in range(10000):
#Do task B
threadA = Thread(target = loopA)
threadB = Thread(target = loobB)
threadA.run()
threadB.run()
# Do work indepedent of loopA and loopB
threadA.join()
threadB.join()
You could use threading or multiprocessing.
How about: A loop for i in range(10000): Do Task A, Do Task B ? Without more information i dont have a better answer.
I find that using the "pool" submodule within "multiprocessing" works amazingly for executing multiple processes at once within a Python Script.
See Section: Using a pool of workers
Look carefully at "# launching multiple evaluations asynchronously may use more processes" in the example. Once you understand what those lines are doing, the following example I constructed will make a lot of sense.
import numpy as np
from multiprocessing import Pool
def desired_function(option, processes, data, etc...):
# your code will go here. option allows you to make choices within your script
# to execute desired sections of code for each pool or subprocess.
return result_array # "for example"
result_array = np.zeros("some shape") # This is normally populated by 1 loop, lets try 4.
processes = 4
pool = Pool(processes=processes)
args = (processes, data, etc...) # Arguments to be passed into desired function.
multiple_results = []
for i in range(processes): # Executes each pool w/ option (1-4 in this case).
multiple_results.append(pool.apply_async(param_process, (i+1,)+args)) # Syncs each.
results = np.array(res.get() for res in multiple_results) # Retrieves results after
# every pool is finished!
for i in range(processes):
result_array = result_array + results[i] # Combines all datasets!
The code will basically run the desired function for a set number of processes. You will have to carefully make sure your function can distinguish between each process (hence why I added the variable "option".) Additionally, it doesn't have to be an array that is being populated in the end, but for my example, that's how I used it. Hope this simplifies or helps you better understand the power of multiprocessing in Python!