2. interesting error, maybe we can revert to "thread mode" if running under a daemon. (I have to admit, I'm not sure why python has this limitation, let me check it...)
Yes, I'm not sure either. I have banged my head against the wall in trying to have multiple level of subprocesses, but it gets too complicated with python. Let me know what you find out
Wait but that will skip all the assertion checks that I have in my code?!
Thanks for the tip with the config file. I have reverted back to 0.17.4 but will try this.
Yes, I am using multiprocessing.Pool to launch each job
clearml launches a subprocess
correct, this subprocess is used fgor resource monitoring and sending logs in the background (i.e metrics console etc.)
Where does the "training" part coming from? I'm assuming the training is your main code?
Follow up, is this happening when running manually or when executed via the agent ?
Yes it does. I'm assuming each job is launched using a multiprocessing.Pool (which translates into a sub process). Let me see if I can reproduce this behavior.
Okay, I was able to reproduce, this will only happen if you are running from a daemon process (like in the case of a process pool), Python is sometimes very picky when it comes to multi-threading/processes I'll check what we can do 🙂
This is happening manually. I am not using agent yet
I'll check what we can do on running in a daemon subprocess
The second subprocess is by design. It becomes the primary process when clearml does not use multiprocessing. I hope I'm not confusing you further
Yes the 'training' is my main code. You can think of it has launching a job (training or inference). My main code launches multiple jobs using multiprocessing. Each job is a seprate task for clearml that gets logged. Does that make sense?
SarcasticSparrow10 LOL there is a hack around it 🙂
Run your code with python -O
Which basically skips over all assertion checks
SarcasticSparrow10 how do I reproduce it?
I tried launching from a sub process that is a daemon and it worked. Are you using ProcessPool ?
Hi SarcasticSparrow10 , so yes it does, this is more efficient when using pytorch loaders, and in some other situations.
To disable it add to your clearml.conf:sdk.development.report_use_subprocess = false2. interesting error, maybe we can revert to "thread mode" if running under a daemon. (I have to admit, I'm not sure why python has this limitation, let me check it...)
Yes, I am using Pool. Here is what I think is happening. clearml launches a subprocess which I assume is a daemonic process. That process in-turn launches a subprocess for training which causes the error I mentioned
Sure, it will revert to the old behavior and run in threads
Yep, but a funny hack nonetheless.
No idea why they have it there...