I have a code which is basically running an infinite loop, and in each iteration of the loop I run some instructions. Some of these instructions have to run in "parallel", which I do by using multiprocessing. Here is an example of my code structure:
from multiprocessing import Pool
from multiprocessing.dummy import Pool as ThreadPool
def buy_fruit(fruit, number):
print('I bought '+str(number)+' times the following fruit:'+fruit)
return 'ok'
def func1(parameter1, parameter2):
myParameters=(parameter1,parameter2)
pool= Threadpool(2)
data = pool.starmap(func2,zip(myParameters))
return 'ok'
def func2(parameter1):
print(parameter1)
return 'ok'
while true:
myFruits=('apple','pear','orange')
myQuantities=(5,10,2)
pool= Threadpool(2)
data = pool.starmap(buy_fruit,zip(myFruits,myQuantities))
func1('hello', 'hola')
I agree it's a bit messy, because I have multi-processes within the main loop, but also within functions.
So everything works well, until the loop runs a few minutes and I get an error:
"RuntimeError: can't start new thread"
I saw online that this is due to the fact that I have opened too many threads.
What is the simplest way to close all my Threads by the end of each loop iteration, so I can restart "fresh" at the start of the new loop iteration?
Thank you in advance for your time and help!
Best,
Julia
PS: The example code is just an example, my real function opens many threads in each loop and each function takes a few seconds to execute.
You are creating a new ThreadPool object inside the endless loop, which is a likely cause to your problem, because you are not terminating the threads at the end of the loop. Have you tried creating the object outside of the endless loop?
pool = ThreadPool(2)
while True:
myFruits = ('apple','pear','orange')
myQuantities = (5,10,2)
data = pool.starmap(buy_fruit, zip(myFruits,myQuantities))
Alternatively, and to answer your question, if your use case for some reason requires creating a new ThreadPool Object in each loop iteration, use a ContextManager (with Notation) to make sure all threads are closed upon leaving the ContextManager.
while True:
myFruits = ('apple','pear','orange')
myQuantities = (5,10,2)
with ThreadPool(2) as pool:
data = pool.starmap(buy_fruit, zip(myFruits,myQuantities))
Notice however the noticable performance difference this has compared to the above code. Creating and terminating Threads is expensive, which is why the example above will run much faster, and is probably what you'll want to use.
Regarding your edit involving "nested ThreadPools": I would suggest to maintain one single instance of your ThreadPool, and pass references to your nested functions as required.
def func1(pool, parameter1, parameter2):
...
...
pool = ThreadPool(2)
while True:
myFruits=('apple','pear','orange')
myQuantities=(5,10,2)
data = pool.starmap(buy_fruit, zip(myFruits,myQuantities))
func1(pool, 'hello', 'hola')
Related
Say I have a very large list and I'm performing an operation like so:
for item in items:
try:
api.my_operation(item)
except:
print 'error with item'
My issue is two fold:
There are a lot of items
api.my_operation takes forever to return
I'd like to use multi-threading to spin up a bunch of api.my_operations at once so I can process maybe 5 or 10 or even 100 items at once.
If my_operation() returns an exception (because maybe I already processed that item) - that's OK. It won't break anything. The loop can continue to the next item.
Note: this is for Python 2.7.3
First, in Python, if your code is CPU-bound, multithreading won't help, because only one thread can hold the Global Interpreter Lock, and therefore run Python code, at a time. So, you need to use processes, not threads.
This is not true if your operation "takes forever to return" because it's IO-bound—that is, waiting on the network or disk copies or the like. I'll come back to that later.
Next, the way to process 5 or 10 or 100 items at once is to create a pool of 5 or 10 or 100 workers, and put the items into a queue that the workers service. Fortunately, the stdlib multiprocessing and concurrent.futures libraries both wraps up most of the details for you.
The former is more powerful and flexible for traditional programming; the latter is simpler if you need to compose future-waiting; for trivial cases, it really doesn't matter which you choose. (In this case, the most obvious implementation with each takes 3 lines with futures, 4 lines with multiprocessing.)
If you're using 2.6-2.7 or 3.0-3.1, futures isn't built in, but you can install it from PyPI (pip install futures).
Finally, it's usually a lot simpler to parallelize things if you can turn the entire loop iteration into a function call (something you could, e.g., pass to map), so let's do that first:
def try_my_operation(item):
try:
api.my_operation(item)
except:
print('error with item')
Putting it all together:
executor = concurrent.futures.ProcessPoolExecutor(10)
futures = [executor.submit(try_my_operation, item) for item in items]
concurrent.futures.wait(futures)
If you have lots of relatively small jobs, the overhead of multiprocessing might swamp the gains. The way to solve that is to batch up the work into larger jobs. For example (using grouper from the itertools recipes, which you can copy and paste into your code, or get from the more-itertools project on PyPI):
def try_multiple_operations(items):
for item in items:
try:
api.my_operation(item)
except:
print('error with item')
executor = concurrent.futures.ProcessPoolExecutor(10)
futures = [executor.submit(try_multiple_operations, group)
for group in grouper(5, items)]
concurrent.futures.wait(futures)
Finally, what if your code is IO bound? Then threads are just as good as processes, and with less overhead (and fewer limitations, but those limitations usually won't affect you in cases like this). Sometimes that "less overhead" is enough to mean you don't need batching with threads, but you do with processes, which is a nice win.
So, how do you use threads instead of processes? Just change ProcessPoolExecutor to ThreadPoolExecutor.
If you're not sure whether your code is CPU-bound or IO-bound, just try it both ways.
Can I do this for multiple functions in my python script? For example, if I had another for loop elsewhere in the code that I wanted to parallelize. Is it possible to do two multi threaded functions in the same script?
Yes. In fact, there are two different ways to do it.
First, you can share the same (thread or process) executor and use it from multiple places with no problem. The whole point of tasks and futures is that they're self-contained; you don't care where they run, just that you queue them up and eventually get the answer back.
Alternatively, you can have two executors in the same program with no problem. This has a performance cost—if you're using both executors at the same time, you'll end up trying to run (for example) 16 busy threads on 8 cores, which means there's going to be some context switching. But sometimes it's worth doing because, say, the two executors are rarely busy at the same time, and it makes your code a lot simpler. Or maybe one executor is running very large tasks that can take a while to complete, and the other is running very small tasks that need to complete as quickly as possible, because responsiveness is more important than throughput for part of your program.
If you don't know which is appropriate for your program, usually it's the first.
There's multiprocesing.pool, and the following sample illustrates how to use one of them:
from multiprocessing.pool import ThreadPool as Pool
# from multiprocessing import Pool
pool_size = 5 # your "parallelness"
# define worker function before a Pool is instantiated
def worker(item):
try:
api.my_operation(item)
except:
print('error with item')
pool = Pool(pool_size)
for item in items:
pool.apply_async(worker, (item,))
pool.close()
pool.join()
Now if you indeed identify that your process is CPU bound as #abarnert mentioned, change ThreadPool to the process pool implementation (commented under ThreadPool import). You can find more details here: http://docs.python.org/2/library/multiprocessing.html#using-a-pool-of-workers
You can split the processing into a specified number of threads using an approach like this:
import threading
def process(items, start, end):
for item in items[start:end]:
try:
api.my_operation(item)
except Exception:
print('error with item')
def split_processing(items, num_splits=4):
split_size = len(items) // num_splits
threads = []
for i in range(num_splits):
# determine the indices of the list this thread will handle
start = i * split_size
# special case on the last chunk to account for uneven splits
end = None if i+1 == num_splits else (i+1) * split_size
# create the thread
threads.append(
threading.Thread(target=process, args=(items, start, end)))
threads[-1].start() # start the thread we just created
# wait for all threads to finish
for t in threads:
t.join()
split_processing(items)
import numpy as np
import threading
def threaded_process(items_chunk):
""" Your main process which runs in thread for each chunk"""
for item in items_chunk:
try:
api.my_operation(item)
except Exception:
print('error with item')
n_threads = 20
# Splitting the items into chunks equal to number of threads
array_chunk = np.array_split(input_image_list, n_threads)
thread_list = []
for thr in range(n_threads):
thread = threading.Thread(target=threaded_process, args=(array_chunk[thr]),)
thread_list.append(thread)
thread_list[thr].start()
for thread in thread_list:
thread.join()
I am trying to use the ThreadPoolExecutor() in a method of a class to create a pool of threads that will execute another method within the same class. I have the with concurrent.futures.ThreadPoolExecutor()... however it does not wait, and an error is thrown saying there was no key in the dictionary I query after the "with..." statement. I understand why the error is thrown because the dictionary has not been updated yet because the threads in the pool did not finish executing. I know the threads did not finish executing because I have a print("done") in the method that is called within the ThreadPoolExecutor, and "done" is not printed to the console.
I am new to threads, so if any suggestions on how to do this better are appreciated!
def tokenizer(self):
all_tokens = []
self.token_q = Queue()
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
for num in range(5):
executor.submit(self.get_tokens, num)
executor.shutdown(wait=True)
print("Hi")
results = {}
while not self.token_q.empty():
temp_result = self.token_q.get()
results[temp_result[1]] = temp_result[0]
print(temp_result[1])
for index in range(len(self.zettels)):
for zettel in results[index]:
all_tokens.append(zettel)
return all_tokens
def get_tokens(self, thread_index):
print("!!!!!!!")
switch = {
0: self.zettels[:(len(self.zettels)/5)],
1: self.zettels[(len(self.zettels)/5): (len(self.zettels)/5)*2],
2: self.zettels[(len(self.zettels)/5)*2: (len(self.zettels)/5)*3],
3: self.zettels[(len(self.zettels)/5)*3: (len(self.zettels)/5)*4],
4: self.zettels[(len(self.zettels)/5)*4: (len(self.zettels)/5)*5],
}
new_tokens = []
for zettel in switch.get(thread_index):
tokens = re.split('\W+', str(zettel))
tokens = list(filter(None, tokens))
new_tokens.append(tokens)
print("done")
self.token_q.put([new_tokens, thread_index])
'''
Expected to see all print("!!!!!!") and print("done") statements before the print ("Hi") statement.
Actually shows the !!!!!!! then the Hi, then the KeyError for the results dictionary.
As you have already found out, the pool is waiting; print('done') is never executed because presumably a TypeError raises earlier.
The pool does not directly wait for the tasks to finish, it waits for its worker threads to join, which implicitly requires the execution of the tasks to complete, one way (success) or the other (exception).
The reason you do not see that exception raising is because the task is wrapped in a Future. A Future
[...] encapsulates the asynchronous execution of a callable.
Future instances are returned by the executor's submit method and they allow to query the state of the execution and access whatever its outcome is.
That brings me to some remarks I wanted to make.
The Queue in self.token_q seems unnecessary
Judging by the code you shared, you only use this queue to pass the results of your tasks back to the tokenizer function. That's not needed, you can access that from the Future that the call to submit returns:
def tokenizer(self):
all_tokens = []
with ThreadPoolExecutor(max_workers=5) as executor:
futures = [executor.submit(get_tokens, num) for num in range(5)]
# executor.shutdown(wait=True) here is redundant, it is called when exiting the context:
# https://github.com/python/cpython/blob/3.7/Lib/concurrent/futures/_base.py#L623
print("Hi")
results = {}
for fut in futures:
try:
res = fut.result()
results[res[1]] = res[0]
except Exception:
continue
[...]
def get_tokens(self, thread_index):
[...]
# instead of self.token_q.put([new_tokens, thread_index])
return new_tokens, thread_index
It is likely that your program does not benefit from using threads
From the code you shared, it seems like the operations in get_tokens are CPU bound, rather than I/O bound. If you are running your program in CPython (or any other interpreter using a Global Interpreter Lock), there will be no benefit from using threads in that case.
In CPython, the global interpreter lock, or GIL, is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once.
That means for any Python process, only one thread can execute at any given time. This is not so much of an issue if your task at hand is I/O bound, i.e. frequently pauses to wait for I/O (e.g. for data on a socket). If your tasks need to constantly execute bytecode in a processor, there's no benefit for pausing one thread to let another execute some instructions. In fact, the resulting context switches might even prove detrimental.
You might want to go for parallelism instead of concurrency. Take a look at ProcessPoolExecutor for this.However, I recommend to benchmark your code running sequentially, concurrently and in parallel. Creating processes or threads comes at a cost and, depending on the task to complete, doing so might take longer than just executing one task after the other in a sequential manner.
As an aside, this looks a bit suspicious:
for index in range(len(self.zettels)):
for zettel in results[index]:
all_tokens.append(zettel)
results seems to always have five items, because for num in range(5). If the length of self.zettels is greater than five, I'd expect a KeyError to raise here.If self.zettels is guaranteed to have a length of five, then I'd see potential for some code optimization here.
You need to loop over concurrent.futures.as_completed() as shown here. It will yield values as each thread completes.
I have a function that is used by multiple threads. Because of its nature, this function should only ever called once at a time. Multiple threads calling the function at the same time could be bad.
If the function is in use by a thread, other threads should have to wait for it to be free.
My background isn't coding so I'm not sure, but I believe this is called "locking" in the jargon? I tried Googling it up but did not find a simple example for Python3.
A simplified case:
def critical_function():
# How do I "lock" this function?
print('critical operation that should only be run once at a time')
def threaded_function():
while True:
# doing stuff and then
critical_function()
for i in range(0, 10):
threading.Thread(target=threaded_function).start()
from threading import Lock
critical_function_lock = Lock()
def critical_function():
with critical_function_lock:
# How do I "lock" this function?
print('critical operation that should only be run once at a time')
I have a problem running multiple processes in python3 .
My program does the following:
1. Takes entries from an sqllite database and passes them to an input_queue
2. Create multiple processes that take items off the input_queue, run it through a function and output the result to the output queue.
3. Create a thread that takes items off the output_queue and prints them (This thread is obviously started before the first 2 steps)
My problem is that currently the 'function' in step 2 is only run as many times as the number of processes set, so for example if you set the number of processes to 8, it only runs 8 times then stops. I assumed it would keep running until it took all items off the input_queue.
Do I need to rewrite the function that takes the entries out of the database (step 1) into another process and then pass its output queue as an input queue for step 2?
Edit:
Here is an example of the code, I used a list of numbers as a substitute for the database entries as it still performs the same way. I have 300 items on the list and I would like it to process all 300 items, but at the moment it just processes 10 (the number of processes I have assigned)
#!/usr/bin/python3
from multiprocessing import Process,Queue
import multiprocessing
from threading import Thread
## This is the class that would be passed to the multi_processing function
class Processor:
def __init__(self,out_queue):
self.out_queue = out_queue
def __call__(self,in_queue):
data_entry = in_queue.get()
result = data_entry*2
self.out_queue.put(result)
#Performs the multiprocessing
def perform_distributed_processing(dbList,threads,processor_factory,output_queue):
input_queue = Queue()
# Create the Data processors.
for i in range(threads):
processor = processor_factory(output_queue)
data_proc = Process(target = processor,
args = (input_queue,))
data_proc.start()
# Push entries to the queue.
for entry in dbList:
input_queue.put(entry)
# Push stop markers to the queue, one for each thread.
for i in range(threads):
input_queue.put(None)
data_proc.join()
output_queue.put(None)
if __name__ == '__main__':
output_results = Queue()
def output_results_reader(queue):
while True:
item = queue.get()
if item is None:
break
print(item)
# Establish results collecting thread.
results_process = Thread(target = output_results_reader,args = (output_results,))
results_process.start()
# Use this as a substitute for the database in the example
dbList = [i for i in range(300)]
# Perform multi processing
perform_distributed_processing(dbList,10,Processor,output_results)
# Wait for it all to finish.
results_process.join()
A collection of processes that service an input queue and write to an output queue is pretty much the definition of a process pool.
If you want to know how to build one from scratch, the best way to learn is to look at the source code for multiprocessing.Pool, which is pretty simply Python, and very nicely written. But, as you might expect, you can just use multiprocessing.Pool instead of re-implementing it. The examples in the docs are very nice.
But really, you could make this even simpler by using an executor instead of a pool. It's hard to explain the difference (again, read the docs for both modules), but basically, a future is a "smart" result object, which means instead of a pool with a variety of different ways to run jobs and get results, you just need a dumb thing that doesn't know how to do anything but return futures. (Of course in the most trivial cases, the code looks almost identical either way…)
from concurrent.futures import ProcessPoolExecutor
def Processor(data_entry):
return data_entry*2
def perform_distributed_processing(dbList, threads, processor_factory):
with ProcessPoolExecutor(processes=threads) as executor:
yield from executor.map(processor_factory, dbList)
if __name__ == '__main__':
# Use this as a substitute for the database in the example
dbList = [i for i in range(300)]
for result in perform_distributed_processing(dbList, 8, Processor):
print(result)
Or, if you want to handle them as they come instead of in order:
def perform_distributed_processing(dbList, threads, processor_factory):
with ProcessPoolExecutor(processes=threads) as executor:
fs = (executor.submit(processor_factory, db) for db in dbList)
yield from map(Future.result, as_completed(fs))
Notice that I also replaced your in-process queue and thread, because it wasn't doing anything but providing a way to interleave "wait for the next result" and "process the most recent result", and yield (or yield from, in this case) does that without all the complexity, overhead, and potential for getting things wrong.
Don't try to rewrite the whole multiprocessing library again. I think you can use any of multiprocessing.Pool methods depending on your needs - if this is a batch job you can even use the synchronous multiprocessing.Pool.map() - only instead of pushing to input queue, you need to write a generator that yields input to the threads.
I have two different functions f, and g that compute the same result with different algorithms. Sometimes one or the other takes a long time while the other terminates quickly. I want to create a new function that runs each simultaneously and then returns the result from the first that finishes.
I want to create that function with a higher order function
h = firstresult(f, g)
What is the best way to accomplish this in Python?
I suspect that the solution involves threading. I'd like to avoid discussion of the GIL.
I would simply use a Queue for this. Start the threads and the first one which has a result ready writes to the queue.
Code
from threading import Thread
from time import sleep
from Queue import Queue
def firstresult(*functions):
queue = Queue()
threads = []
for f in functions:
def thread_main():
queue.put(f())
thread = Thread(target=thread_main)
threads.append(thread)
thread.start()
result = queue.get()
return result
def slow():
sleep(1)
return 42
def fast():
return 0
if __name__ == '__main__':
print firstresult(slow, fast)
Live demo
http://ideone.com/jzzZX2
Notes
Stopping the threads is an entirely different topic. For this you need to add some state variable to the threads which needs to be checked in regular intervals. As I want to keep this example short I simply assumed that part and assumed that all workers get the time to finish their work even though the result is never read.
Skipping the discussion about the Gil as requested by the questioner. ;-)
Now - unlike my suggestion on the other answer, this piece of code does exactly what you are requesting:
from multiprocessing import Process, Queue
import random
import time
def firstresult(func1, func2):
queue = Queue()
proc1 = Process(target=func1,args=(queue,))
proc2 = Process(target=func2, args=(queue,))
proc1.start();proc2.start()
result = queue.get()
proc1.terminate(); proc2.terminate()
return result
def algo1(queue):
time.sleep(random.uniform(0,1))
queue.put("algo 1")
def algo2(queue):
time.sleep(random.uniform(0,1))
queue.put("algo 2")
print firstresult(algo1, algo2)
Run each function in a new worker thread, the 2 worker threads send the result back to the main thread in a 1 item queue or something similar. When the main thread receives the result from the winner, it kills (do python threads support kill yet? lol.) both worker threads to avoid wasting time (one function may take hours while the other only takes a second).
Replace the word thread with process if you want.
You will need to run each function in another process (with multiprocessing) or in a different thread.
If both are CPU bound, multithread won help much - exactly due to the GIL -
so multiprocessing is the way.
If the return value is a pickleable (serializable) object, I have this decorator I created that simply runs the function in background, in another process:
https://bitbucket.org/jsbueno/lelo/src
It is not exactly what you want - as both are non-blocking and start executing right away. The tirck with this decorator is that it blocks (and waits for the function to complete) as when you try to use the return value.
But on the other hand - it is just a decorator that does all the work.