Issues with multiprocess
See original GitHub issueI having some issues with multiprocessing
where on some clusters the PSO will not start even waiting for a hour. When I manually interrupt the code, I found that the code is stuck at the following block:
Issue Analytics
- State:
- Created 2 years ago
- Comments:12 (1 by maintainers)
Top Results From Across the Web
7 Multiprocessing Pool Common Errors in Python
Common Errors When Using Multiprocessing Pool · Forgetting __main__ · Using a Function Call in submit() · Using a Function Call in map()...
Read more >Things I Wish They Told Me About Multiprocessing in Python
When there is only one shared structure, you can easily run into issues with blocking and contention. As such structures proliferate, however, ...
Read more >Why your multiprocessing Pool is stuck (it's full of sharks!)
On Linux, the default configuration of Python's multiprocessing library can lead to deadlocks and brokenness. Learn why, and how to fix it.
Read more >python problem with multiprocessing and for - Stack Overflow
In the code below, however, I am using a multiprocessing.Pipe on which the multiprocessing.Queue is built. It is not as flexible as a...
Read more >multiprocessing issues on Windows #1240 - GitHub
Windows causes certain multiprocessing workflows to fail due to pickling issues. To Reproduce Steps to reproduce the behavior: Run https:// ...
Read more >Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start FreeTop Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
Top GitHub Comments
I manage to solve the issue by using mpi4py
Yes, I am only using multithreading and not MPI on the EPFL cluster since I have access to only one node at a time. I found that the multithreading mode is more efficient if you are using only one node. But I never had this problem on the cluster, nor on my laptop…