Tensorboard HParams Metric creates unwanted runs

Hello,

I’ve been using Tensorboard for a while to plot different scalars during training. Now I want to do hyperparameter tuning in an organised way, so I’m trying to use HParams correctly.

I plot scalars like this and it works well:

writer = SummaryWriter('runs/' + run_name)
...
...
# I do this every epoch
writer.add_scalar('Training/CE_Loss', loss_CE, epoch)
writer.add_scalar('Validation/Accuracy', validation_acc, epoch)

However, I’m seeing some weird behaviour after adding the hyperparameters. I have a dictionary with all the parameters of my experiment, plus an extra key containing the best validation accuracy. I use that dictionary to create the HParams table and looks fine on TensorBoard:

writer.add_hparams(experiment_data, {'Validation/Accuracy': experiment_data['VALIDATION_ACCURACY_BEST']})

It also adds a scalar (the green dot) to the existing plot:

hparams1

The problem is that it also creates something that looks like a new run, which is very annoying because I get many repeated colours very quickly and makes it difficult to compare between experiments:

hparams2

I see a LOT of tutorials on TB+Pytorch, but none of them (not even the official one!) talks about HParams.

Am I doing something wrong? How could I avoid this?

Thanks

1 Like

Hi @Ignacio_Hernandez, I’m also getting the exact same issue.
Have you managed to solve it, yet?

1 Like

Hi, I haven’t managed to solve it yet… I’m sorry.

1 Like

I just want to report that I have exactly the same issue. I hoped that the 1.7.0 option run_name="" would solve it. It doesnt. :frowning: