I am trying to use multiprocessing.Pool to run my code in parallel. To instantiate Pool, you have to set the number of processes. I am trying to figure out how many I should set for this. I understand this number shouldn’t be more than the number of cores you have but I’ve seen different ways to determine what your system
Tag: multiprocessing
`SolverResults Error` When Parallelising Pyomo Optimisations
I’m trying to optimise multiple linear programming problems in parallel using Pyomo and the standard Python multiprocessing library. When switching to using multi-processing I keep running into the error: ValueError: Cannot load a SolverResults object with bad status: error. A similar issue was reported in this question, where their problem seemed to be that the solver (n.b. they used cbc
Run function in another thread
So say there are two running codes: script1 and script2. I want script2 to be able to run a function in script1. script1 will be some kind of background process that will run “forever”. The point is to be able to make an API for a background process, E.G. a server. The unclean way to do it would be to
Graceful cleanup for multiprocess pool python
I am running a multiprocessing pool, mapped over a number of inputs. My worker processes have an initialization step that spins up a connection to selenium and a database. When the pool finishes its job, what is the graceful way to close these connections rather than just relying on python’s memory management and del definitions? EDIT: Because some_args is large,
When using Pool.map from integrated Python’s multiprocessing, program works slower and slower
Here is a similar question Why does python multiprocessing script slow down after a while? Sample of code that uses Pool: After few iterations the program slows down and finally it becomes even slower than without multiprocessing. Maybe the problem is that the function related to Selenium? Here is full code: Answer You are creating 6 processes to process 14
How SharedMemory in python define the size?
I have some prolem about SharedMemory in python3.8,any help will be good. Question 1. SharedMemory has one parameter SIZE,the doc tell me the unit is byte.I created a instance of 1 byte size,then, let shm.buf=bytearray[1,2,3,4], it can work and no any exception!why? Question 2. why print buffer is a memory address? why i set size is 1byte,but result show it
AttributeError: Can’t get attribute ‘journalerReader’ on <module '__mp_main__
I tried to implement Lmax in python .I tried to handle data in 4 processes but I get this Error in my code : Answer Your first problem is that the target of a Process call cannot be within the if __name__ == ‘__main__’: block. But: As I mentioned in an earlier post of yours, the only way I see
How to get passed parameters to return values in multiprocessing Pool?
For example: I suppose the new_numbers will print: 1, 2, 3, 4…, but it is empty in print(). How to get new_numbers to be populated after the call? Answer First an observation. You have: This is using a loop to create a list with values 0, 1, 2 … 99. But you could have just as easily and more efficiently
Fail the build/script as per os.system()’s output
I’m trying to create helm charts and push them to Nexus repository in parallel(multiprocessing) on 100s of folders and its working good. But, I would like to fail the script or the build, incase the exit status is other than 0. With my current code set up, even though the exit code returns non zero value, here 512, the build
Using threading/multiprocessing in Python to download images concurrently
I have a list of search queries to build a dataset: classes = […]. There are 100 search queries in this list. Basically, I divide the list into 4 chunks of 25 queries. And below, I’ve created a function that downloads queries from each chunk iteratively: However, I want to run each 4 chunks concurrently. In other words, I want