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Hi, I Am Switching From Wandb To Clearml In My Pytorch Ddp Training Script. With Wandb I Used To Have Worker Nr 1 Handle Logging To Wandb And Initiating The Connection. If I Simply Exchange Wandb Calls With Clearml Calls, Worker Nr 1, Which Handles The Co

Hi, I am switching from WandB to clearml in my pytorch DDP training script. With WandB i used to have worker nr 1 handle logging to WandB and initiating the connection. If i simply exchange WandB calls with clearml calls, worker nr 1, which handles the connection, start straggling behind in the validation and training loop, causing a massive slowdown of the training session, up to a factor 5. Is this normal, and if not, how should clearml be used when logging in pytorch multigpu DDP type training environment?

Posted one month ago
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Answers 5

Hi @<1671689458606411776:profile|StormySeaturtle98> ! Do you have a sample snippet that could help us diagnose this problem?

Posted one month ago

It just puzzles me that if a single subprocess spawns a task, meaning that only one task is active, and that task is only accessed by the worker creating it, the massive slowdown happens

Posted one month ago

It seems that running Task.init() outside in the main process before spawning multiple processes, and calling Task.current_task() inside the rank = 0 process works with no slowdown

Posted one month ago

Hi Eugen
Thanks for the response!
let me know if you need a running example, that will require some effort to produce.

Posted one month ago

That makes sense. You should generally have only 1 task (initialized in the master process). The other subprocesses will inherit this task which should speed up the process

Posted one month ago
5 Answers
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one month ago