multiprocessing

问题 How can I use multiprocessing to create a dictionary with generator objects as values? Here is my problem in greater detail, using basic examples: I have a large dictionary of lists whereby I am applying functions to compute on the dictionary values using ProcessPoolExecutor in concurrent.futures . (Note I am using ProcessPoolExecutor , not threads---there is no GIL contention here.) Here is an example dictionary of lists: example_dict1 = {'key1':[367, 30, 847, 482, 887, 654, 347, 504, 413,

问题 First of, I am new to Python. It's irrelevant to the question, but I have to mention it. I am creating an crawler as my first project, to understand how things work in Python, but so far this is my major issue... Understanding "how to get multiple progress bars" in Terminal while using requests and pathos.multiprocessing . I managed to go through everything, I just want to have prettier output, so I decide to add progressbars. I am using tqdm as I like the looks and it seems easiest to

问题 First of, I am new to Python. It's irrelevant to the question, but I have to mention it. I am creating an crawler as my first project, to understand how things work in Python, but so far this is my major issue... Understanding "how to get multiple progress bars" in Terminal while using requests and pathos.multiprocessing . I managed to go through everything, I just want to have prettier output, so I decide to add progressbars. I am using tqdm as I like the looks and it seems easiest to

问题 I am using Python to do object detection in a video stream. I have a TensorFlow Lite model which takes a relatively long time to evaluate. Using interpreter.invoke() , it takes about 500 ms per evaluation. I'd like to use parallelism to get more evaluations per second. I see that I can call the TensorFlow config tf.config.experimental.set_synchronous_execution . I was hoping that setting this would magically cause the interpreter to run in multiple processes. However, running help(tf.lite

问题 I am using Python to do object detection in a video stream. I have a TensorFlow Lite model which takes a relatively long time to evaluate. Using interpreter.invoke() , it takes about 500 ms per evaluation. I'd like to use parallelism to get more evaluations per second. I see that I can call the TensorFlow config tf.config.experimental.set_synchronous_execution . I was hoping that setting this would magically cause the interpreter to run in multiple processes. However, running help(tf.lite

问题 I am working on task where I am creating multiple process to run code in parallel to speed up process. below is my code. def update_value(value): print('module name:\n', __name__) print('parent process:\n', os.getppid()) print('process id:\n', os.getpid()) value_read = server_connect_read(channel, value) if value_read.server_connect() is False: return False print("updating values") update = server_read.update_value(old_values.xlsx) if value_read.server_disconnet() is False: return False Pool

问题 I have a python code that performs filtering on a matrix. I have created a C++ interface using pybind11 that successfully runs in serialized fashion (please see the code in below). I am trying to make it parallel-processing to hopefully reduce the computation time compared to its serialized version. To do this, I have splitted my array of size M×N into three sub-matrices of size M×(N/3) to process them in parallel using the same interface. I used ppl.h library to make a parallel for-loop and

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

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

问题 I'm currently trying to use multiprocessing to process a big result obtained after a psycopg2 query. I split the result in several lists of 100 then send them for multiprocessing. When I do so though, I get the following error TypeError: can't pickle psycopg2.extensions.connection objects Here is my psycopg2 query: def get_employees(self): logging.info('POSTGRESQL QUERY: get_employees') try: self.cur = self.conn.cursor(cursor_factory=RealDictCursor) self.cur.execute( "SELECT ..." ) employees