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I Am Experiencing Performance Issues With Using Clearml Together With Pytorch Lightning Cli For Experiment Tracking. Essentially What We'Re Doing Is Fetching The Logger Object Through Task.Get_Logger() And Then Using The Reporting Methods. However, It Ad

I am experiencing performance issues with using ClearML together with Pytorch Lightning CLI for experiment tracking. Essentially what we're doing is fetching the logger object through task.get_logger() and then using the reporting methods. However, it adds a huge overhead to our model training in terms of time spent.

Attached is a picture comparing time-per-epoch for training with ClearML logging enabled or disabled.

I'm assuming this is because the logging is synchronous and thread-blocking. Is there any way to configure the logger to work in a background thread, or batch x number of messages before sending, etc.?

Posted one year ago
Votes Newest


Hi SoreHorse95 ,

Does ClearML not automatically log all outputs?

Regarding logging maybe try the following setting in ~/clearml.conf sdk.network.metrics.file_upload_threads: 16

Posted one year ago
1 Answer
one year ago
one year ago