Open heth27 opened 2 months ago
as _update_n only calls bar.refresh() and not the update method of the progress bar
This change is needed to give us exact control in all use cases to enable exact updates. The progress bar is deeply tied to the loops so we need that precise control. So we can't remove the _update_n
change. But you can definitely override everything in TQDMProgressBar
you wish and make it your own if that smoothing option is important.
or it should be added to the documentation if smoothing having no effect is the desired behavior (overriding a default that has no effect is a bit misleading)
I'm fine with adding a note to the TQDMProgressBar.init_*_tqdm
methods documentation đź‘Ť
I've updated the documentation for TQDMProgressBar
to clarify the behavior of the smoothing
option. The new note highlights that the smoothing
option has no effect when using the default implementation of TQDMProgressBar
and suggests a potential workaround. You can review the updated documentation here. Additionally, I've submitted a pull request with these changes: PR link.
Bug description
The option smoothing when creating progress bars in TQDMProgressBar has no effect in the default implementation, as _update_n only calls bar.refresh() and not the update method of the progress bar. Thus only the global average is taken, as the update method of the tqdm class is responsible for calculating moving averages. Either the update method of the progress bar could be used or it should be added to the documentation if smoothing having no effect is the desired behavior (overriding a default that has no effect is a bit misleading)
What version are you seeing the problem on?
master
How to reproduce the bug
Error messages and logs
Environment
Current environment
``` #- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow): #- PyTorch Lightning Version (e.g., 1.5.0): #- Lightning App Version (e.g., 0.5.2): #- PyTorch Version (e.g., 2.0): #- Python version (e.g., 3.9): #- OS (e.g., Linux): #- CUDA/cuDNN version: #- GPU models and configuration: #- How you installed Lightning(`conda`, `pip`, source): #- Running environment of LightningApp (e.g. local, cloud): ```More info
No response
cc @borda