Tensorboard Logging

Currently tensorboard_logger is used. I don’t think it’s actively supported anymore, they point to pytorch’s own tensorboard-module on their Github. Why bother? tensorboard_logger uses a singleton-default-logger and I cannot reset the path to write the tensorboard-eventfiles. That would be helpful though, to distinguish between different configurations when refitting configurations from the incumbent-trajectory 😃

[maxmarketit]

@shukon I might not understand the situation here but I do not see any problem because if the each training of NN starts and ends one summaryWriter, is there really any requirement to pass the writer?

I think we can set the root directory and every model trained can saved to subdirectory with appropriate name which convealing main architecture ‘resnet-100-20-30’ or ‘shapedmlpnet-diamond-…-…’ and as the optimization goes, it could change to resnet-100-20-30-i01’ ‘-i02’ ‘-i03’ etc

[maxmarketit]

In my opinion, it would be better if seperate logging is made for each model.

Something like,

from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(filename)

Different writer for different model. Each model will have its own logging directory and we can do tensorboard --log-dir root to see and compare all the models.

I think we can have another writer for monitoring loss for all the models…

But as the number of models gets large, it will be soon very crowded.

But you can filter some of the models.