I know in TF if we use a Supervisor, it helps to save all results and restore the visualizations from the point we stop the training, using a global step variable. In pytorch there’s a pytorch-tensorboard library available, but there doesn’t seem to be any support for something like the Supervisor, so I’m wondering what do pytorch users generally do when you want to visualize the accuracy curves etc from the start of the training until the end using the tensorboard library, after training has stopped (e.g. in the event of a system crash). – how do you “resume” the visualizations?
Alternatively, do you guys find tensorboard useful at all? At this point I’m quite inclined to get away from the usual TF stuff and simply parse the scalar JSON data manually to get the visualizations I want (matplotlib looks very pretty with seaborn too). There’s more control this way, although there’s more work to do.
I think it would be interesting if there’s a “best practices standard” available or if you guys could share what worked the best for you. Thank you! 
