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Answered
Another Problem Is That When Using Mp.Spawn To Init Distributed Training In Pytorch,

Another problem is that when using mp.spawn to init distributed training in pytorch, Task.current_task().get_logger() on worker process will throw
'NoneType' object has no attribute 'get_logger' error

  
  
Posted 3 years ago
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Answers 8


Yes, when i put the task init into the spawn function, it can run without error, but it seems that each of the child process has their own experiments
ClearML Task: created new task id=54ce0761934c42dbacb02a5c059314da ClearML Task: created new task id=fe66f8ec29a1476c8e6176989a4c67e9 ClearML results page: ClearML results page: ClearML Task: overwriting (reusing) task id=de46ccdfb6c047f689db6e50e6fb8291 ClearML Task: created new task id=91f891a272364713a4c3019d0afa058e ClearML results page: ClearML results page:and it shows some errors at init

  
  
Posted 3 years ago

so basically, the spawn will run a function in several separate processes, so i followed the link you gave above and put task.init into that function.
i guess in this way, there will be multiple task.init running.

  
  
Posted 3 years ago

Hi PompousHawk82 . sorry for the delay, I missed the last message. Can you try adding in the spawn process to have task = Task.get_task(task_id=<Your main task Id>) instead of the Task.init call?

  
  
Posted 3 years ago

what i want to do is to init one task and multiple works can log with this one task in parallel. TimelyPenguin76

  
  
Posted 3 years ago

Hi PompousHawk82 . Are you running in parallel the several instances of the same code on the same task?

  
  
Posted 3 years ago

I think this is not related to pytorch, because it shows the same problem with mp spawn

  
  
Posted 3 years ago

trying to understand what reset the task

  
  
Posted 3 years ago
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