Multiprocessing.pool with a function that has multiple args and kwargs
来源： https://stackoverflow.com/questions/59756754/multiprocessing-pool-with-a-function-that-has-multiple-args-and-kwargs
pool
Multiprocessing.pool with a function that has multiple args and kwargs
来源： https://stackoverflow.com/questions/59756754/multiprocessing-pool-with-a-function-that-has-multiple-args-and-kwargs
python process pool with timeout on each process not all of the pool
问题 I need to run many processes, but not all together, for example 4 processes at same time. multiprocessing.Pool is exactly what I need. But the problem is that I need to terminate a process if it lasts more than a timeout (e.g. 3 seconds). Pool just supports wait for a timeout for all processes not each of them. This is what I need: def f(): process_but_kill_if_it_takes_more_than_3_sec() pool.map(f, inputs) I couldn't find a simple way to use Pool with timeouts. There is a solution from Eli
python process pool with timeout on each process not all of the pool
问题 I need to run many processes, but not all together, for example 4 processes at same time. multiprocessing.Pool is exactly what I need. But the problem is that I need to terminate a process if it lasts more than a timeout (e.g. 3 seconds). Pool just supports wait for a timeout for all processes not each of them. This is what I need: def f(): process_but_kill_if_it_takes_more_than_3_sec() pool.map(f, inputs) I couldn't find a simple way to use Pool with timeouts. There is a solution from Eli
Multiprocessing for creating objects + calling functions using starmap() Python
问题 I would like to create objects of class Training and create multiple processes which call the print() function. I have a class Training : class Training(): def __init__(self, param1, param2): self.param1 = param1 self.param2 = param2 def print(self): print(self.param1) print(self.param2) I have tried to use the starmap function to create 5 processes in the following way: import multiprocessing as mp num_devices = 5 func_args = [] for i in range (0, num_devices): func_args.append((i, i*10))
Custom memory manager works fine in release mode, but not in debug mode
问题 I'm trying to implement a simple memory manager to experiment with memory pooling mechanism and track memory leaks. I'm using VS2019 and so far my code only runs in release x86 mode. Changing the build configuration to debug or setting target platform to x64, results in an access violation error. Specifically, in debug mode the following line which calculates the available pool size, throws an exception "Unhandled exception thrown: read access violation. p was nullptr." return p->end - p-
how to add specific number of additional workers to an exisiting multiprocessing pool?
问题 In below situation I've created a default pool with two workers and perform tasks. During task processing the task_queue is checked regularly so it doesn't exceeds a certain length limit and prevents up/down stream clutter. How to add dynamically more workers to reduce the task queue length? import multiprocessing as mp ... code snippet... def main(poolsize, start_process): pool = mp.Pool(processes=poolsize, initializer=start_process) done = False task_queue = [] while True: ... snippet code
how to add specific number of additional workers to an exisiting multiprocessing pool?
问题 In below situation I've created a default pool with two workers and perform tasks. During task processing the task_queue is checked regularly so it doesn't exceeds a certain length limit and prevents up/down stream clutter. How to add dynamically more workers to reduce the task queue length? import multiprocessing as mp ... code snippet... def main(poolsize, start_process): pool = mp.Pool(processes=poolsize, initializer=start_process) done = False task_queue = [] while True: ... snippet code
Python ValueError: Pool not running in Async Multiprocessing
问题 I have a simple code: path = [filepath1, filepath2, filepath3] def umap_embedding(filepath): file = np.genfromtxt(filepath,delimiter=' ') if len(file) > 20000: file = file[np.random.choice(file.shape[0], 20000, replace=False), :] neighbors = len(file)//200 if neighbors >= 2: neighbors = neighbors else: neighbors = 2 embedder = umap.UMAP(n_neighbors=neighbors, min_dist=0.1, metric='correlation', n_components=2) embedder.fit(file) embedded = embedder.transform(file) name = 'file' np.savetxt
Python ValueError: Pool not running in Async Multiprocessing
问题 I have a simple code: path = [filepath1, filepath2, filepath3] def umap_embedding(filepath): file = np.genfromtxt(filepath,delimiter=' ') if len(file) > 20000: file = file[np.random.choice(file.shape[0], 20000, replace=False), :] neighbors = len(file)//200 if neighbors >= 2: neighbors = neighbors else: neighbors = 2 embedder = umap.UMAP(n_neighbors=neighbors, min_dist=0.1, metric='correlation', n_components=2) embedder.fit(file) embedded = embedder.transform(file) name = 'file' np.savetxt