multiprocessing

问题 I'm having trouble debugging this and haven't made much progress. I have along running async celery task that'll sometimes hit a signal 11 (it's a recursive/CPU-bound function that can be run into stack size issues). So, for example: Process 'ForkPoolWorker-2' pid:5494 exited with 'signal 11 (SIGSEGV)' I'd like to modify my celery task, task class and request to catch this and trigger the on_failure function of the task class. I haven't had any luck though. I'm running this with a redis

问题 I am building a GUI that takes in sensor data from the raspberry pi and displays it onto a window via matplotlib animation. The code works fine, except when being run on raspberry pi, the matplotlib animation takes some time to execute, which momentarily blocks the sensor reading GetCPM that I'm interested in. How can I make both these programs run simultaneously without one clogging the other, I've tried the multiprocessing library, but I can't seem to get it to work. Note: The sensor data

问题 I try to get ARIMA configuration some faster that I acctually do. So I use a Iterate method to compare all ARIMA combinations to select better. For that I create a function to Iterate: def difference(dataset, interval=1): diff = list() for i in range(interval, len(dataset)): value = dataset[i] - dataset[i - interval] diff.append(value) return np.array(diff) # invert differenced value def inverse_difference(history, yhat, interval=1): return yhat + history[-interval] # evaluate an ARIMA model

问题 I have broken my standard deviation function into small chunks of std_1, std_2, std_3 etc. to optimize my code to make it run faster. Since I have over 2 million arrays on my main numpy array PC_list . I have used the numba, numpy arrays and multi processing to make the code run faster however I do not see any performance difference in the way even doe the code is broken into pieces from the main function. It takes about 57 seconds for the main function to process and the divided function to

问题 I'm trying to create a timeout function in Python 2.7.11 (on Windows) with the multiprocessing library. My basic goal is to return one value if the function times out and the actual value if it doesn't timeout. My approach is the following: from multiprocessing import Process, Manager def timeoutFunction(puzzleFileName, timeLimit): manager = Manager() returnVal = manager.list() # Create worker function def solveProblem(return_val): return_val[:] = doSomeWork(puzzleFileName) # doSomeWork()

问题 I'm trying to create a timeout function in Python 2.7.11 (on Windows) with the multiprocessing library. My basic goal is to return one value if the function times out and the actual value if it doesn't timeout. My approach is the following: from multiprocessing import Process, Manager def timeoutFunction(puzzleFileName, timeLimit): manager = Manager() returnVal = manager.list() # Create worker function def solveProblem(return_val): return_val[:] = doSomeWork(puzzleFileName) # doSomeWork()

问题 I am trying to make predictions with a Keras model (using Tensorflow 2.0) using multiple CPUs. I have tried this: tf.config.threading.set_intra_op_parallelism_threads(4) tf.config.threading.set_inter_op_parallelism_threads(4) While not getting an error, I am not sure if this is the right approach. Can predictions be multithreaded? Many thanks 来源： https://stackoverflow.com/questions/58974483/make-prediction-with-keras-model-using-multiple-cpus

问题 Note that I have to sweep through more argument sets than available CPUs, so I'm not sure if Python will automatically schedule the use of the CPUs depending on their availability or what. Here is what I tried, but I get an error about the arguments: import random import multiprocessing from train_nodes import run import itertools envs = ["AntBulletEnv-v0", "HalfCheetahBulletEnv-vo", "HopperBulletEnv-v0", "ReacherBulletEnv-v0", "Walker2DBulletEnv-v0", "InvertedDoublePendulumBulletEnv-v0"]

问题 My aim is to pass a parent array to mp.Pool and fill it with 2 s while distributing it to different processes. This works for arrays of 1 dimension: import numpy as np import multiprocessing as mp import itertools def worker_function(i=None): global arr val = 2 arr[i] = val print(arr[:]) def init_arr(arr=None): globals()['arr'] = arr def main(): arr = mp.Array('i', np.zeros(5, dtype=int), lock=False) mp.Pool(1, initializer=init_arr, initargs=(arr,)).starmap(worker_function, zip(range(5)))

问题 My aim is to pass a parent array to mp.Pool and fill it with 2 s while distributing it to different processes. This works for arrays of 1 dimension: import numpy as np import multiprocessing as mp import itertools def worker_function(i=None): global arr val = 2 arr[i] = val print(arr[:]) def init_arr(arr=None): globals()['arr'] = arr def main(): arr = mp.Array('i', np.zeros(5, dtype=int), lock=False) mp.Pool(1, initializer=init_arr, initargs=(arr,)).starmap(worker_function, zip(range(5)))