pool

问题 I have a multiprocessing script with pool.map that works. The problem is that not all processes take as long to finish, so some processes fall asleep because they wait until all processes are finished (same problem as in this question). Some files are finished in less than a second, others take minutes (or hours). If I understand the manual (and this post) correctly, pool.imap is not waiting for all the processes to finish, if one is done, it is providing a new file to process. When I try

问题 I asked a question here about multiprocessing a few days ago, and one user sent me the answer that you can see below. Only problem is that this answer worked on his machine and does not work on my machine. I have tried on Windows (Python 3.6) and on Mac(Python 3.8). I have ran the code on basic Python IDLE that came with installation, in PyCharm on Windows and on Jupyter Notebook and nothing happens. I have 32 bit Python. This is the code: from bs4 import BeautifulSoup import requests from

问题 I have a small pool of workers (4) and a very large list of tasks (5000~). I'm using a pool and sending the tasks with map_async(). Because the task I'm running is fairly long, I'm forcing a chunksize of 1 so that one long process can't hold up some shorter ones. What I'd like to do is periodically check how many tasks are left to be submitted. I know at most 4 will be active, I'm concerned with how many are left to process. I've googled around and I can't find anybody doing this. Some simple

问题 I am confused as to what _number_left is supposed to return. I assumed it was the number tasks remaining in the pool, but it does not appear to provide the correct value in my code. For example, if I have a pool of 10 workers counting to the number 1000, I would expect result._number_left to countdown from 1000. However, it only tells me I have 40 left until the code is about finished. Am I missing something here? Code: import multiprocessing import time def do_something(x): print x time

问题 I am confused as to what _number_left is supposed to return. I assumed it was the number tasks remaining in the pool, but it does not appear to provide the correct value in my code. For example, if I have a pool of 10 workers counting to the number 1000, I would expect result._number_left to countdown from 1000. However, it only tells me I have 40 left until the code is about finished. Am I missing something here? Code: import multiprocessing import time def do_something(x): print x time

问题 I have this very simple python code that I want to speed up by parallelizing it. However no matter what I seem to do, multiprocessing.Pool.map doesn't gain anything over the standard map. I've read other threads where people use this with very small functions that don't parallelize well and lead to excessive overhead but I would think that shouldn't be the case here. Am I doing something wrong? Here's the example #!/usr/bin/python import numpy, time def AddNoise(sample): #time.sleep(0.001)

问题 I wanted to know that, is it possible to create a pool of objects? So that I can take an object from the pool and once I'm done with the work, I can put it into the pool. 回答1: I wanted to know that, is it possible to create a pool of objects? So that I can take an object from the pool and once I'm done with the work, I can put it into the pool. It is possible yes. You can see performance improvements in many situations if the construction of a new object is expensive (like establishing a

问题 How do I extend theanos downsample.max_pool_2d_same_size in order to pool not only within a feature map, but also between those - in a efficient manner? Lets say i got 3 feature maps, each of size 10x10, that would be a 4D Tensor (1,3,10,10). First lets max pool ((2,2), no overlapping) each of the (10,10) feature map. The results are 3 sparse feature maps, still (10,10) but most values equal to zero: within a (2,2) window is at most one value greater than zero. This is what downsample.max

问题 After some time the following error page is shown. What can I do to prevent this? I have large numbers of users and the app uses Oracle 11g. Please suggest me pool size so can be extended default max pool size is 100. I have checked all connections properly closed. I am using OracleDataReader and Datatable in my application Method that i am using is as under: public OracleDataReader BidNoIncr() { OracleConnection objOracleConnection = new OracleConnection(objDBClass.GetConnSring());

问题 I'm using boost.pool, but I don't know when to use boost::pool<>::malloc and boost::pool<>::ordered_malloc ? so, what's the difference of boost::pool<>::malloc and boost::pool<>::ordered_malloc ? when should I use boost::pool<>::ordered_malloc ? 回答1: First, we should know the basic idea behind the Boost Pool library: simple_segregated_storage , it is similar to a singly linked list, and responsible for partitioning a memory block into fixed-size chunks: A memory pool keeps a free list of