pathos

问题 I'm trying to reduce a list of names, and in order to perform this I'm using the fuzzywuzzy library. I perform two for loops, both over all the names. If the two names have a fuzzy match score between the 90 and the 100, Then I rewrite the second name with the first name. Here is an example of my dataset, data . nombre 0 VICTOR MORENO MORENO 1 SERGIO HERNANDEZ GUTIERREZ 2 FRANCISCO JAVIER MUÑOZ LOPEZ 3 JUAN RAYMUNDO MORALES MARTINEZ 4 IVAN ERNESTO SANCHEZ URROZ And here is my function: def

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 trying to to make use of multiprocessing across several different computers, which pathos seems geared towards: "Pathos is a framework for heterogenous computing. It primarily provides the communication mechanisms for configuring and launching parallel computations across heterogenous resources." In looking at the documentation, however, I am at a loss as to how to get a cluster up and running. I am looking to: Set up a remote server or set of remote servers with secure authentication.

问题 I want to fit many distributions with scipy and would like to use some sort of multiprocessing for this. Something like this: import scipy.stats as ss from pathos.multiprocessing import ProcessingPool from multiprocessing import Pool mp = Pool() pp = ProcessingPool() l = [0,1,2,3,4,6,7,8,9] print map(ss.lognorm.fit,l) #method 0 print mp.map(ss.lognorm.fit,l) #method 1 print pp.map(ss.lognorm.fit,l) #method 2 Method 0 is of course not multiprocessing, but works. Method 1 and 2 both return with

问题 When i tried to run the following code: from pathos.multiprocessing import ProcessingPool as Pool list1 = [1,2,3,4,5] list2 = [6,7,8,9,10] def function1(x,y): print x print y if __name__ == '__main__': pool = Pool(5) pool.map(function1, list1, list2) It gets the followwing error: Traceback (most recent call last): File "test.py", line 9, in <module> pool.map(function1, list1, list2) File "C:\Python27\lib\site-packages\pathos\multiprocessing.py", line 136, in map return _pool.map(star(f), zip(

问题 Hie , can any one tell me how can i install pathos package of python in windows OS. I wanted to to install pathos in windows OS. I got an error for pickling and un pickling, 回答1: I'm the pathos author. This worked for me: installed Visual Studio Community 2015 RC installed Python Tools 2.2 RC for Visual Studio 2015 installed Microsoft Visual C++ Compiler for Python 2.7 installed Miniconda 3.10.1 (64-bit) for Python 2.7 installed Git for Windows 1.9.5-preview20150319 conda install pip conda

问题 I've a function that takes in a list of images and produces the output, in a list, after applying OCR to the image. I have an another function that controls the input to this function, by using multiprocessing. So, when I have a single list (i.e. no multiprocessing), each image of the list took ~ 1s, but when I increased the lists that had to be processed parallely to 4, each image took an astounding 13s. To understand where the problem really is, I tried to create a minimal working example

问题 short short version: I am having trouble parallelizing code which uses instance methods. Longer version: This python code produces the error: Error Traceback (most recent call last): File "/Users/gilzellner/dev/git/3.2.1-build/cloudify-system-tests/cosmo_tester/test_suites/stress_test_openstack/test_file.py", line 24, in test self.pool.map(self.f, [self, url]) File "/Users/gilzellner/.virtualenvs/3.2.1-build/lib/python2.7/site-packages/pathos/multiprocessing.py", line 131, in map return _pool

问题 How can I implement non-daemonic processes with pathos in python3 instead of with the multiprocessing module? To be more specific, I am referring to: Python Process Pool non-daemonic? The answer to this post implements non-daemonic processes via the multiprocessing module. Unfortunately, this module does not allow to pickle lambda functions among other objects, but pathos does in Python 2: #import multiprocessing #import multiprocessing.pool import pathos #class NoDaemonProcess