How to add graphs to hparams in tensorboard?

I’m trying to use tensorboard with pytorch and cannot find any clear documentation as to how to add graphs to the hparams dashboard. The image below shows what I want


however I can only add singular values for the metrics:

Thanks in advance.

Having dug a little deeper into torch.utils.tensorboard, I can’t seem to find how to use this functionality. However I can achieve it by modifying the SummaryWriter.add_hparams() function as such:

			def add_hparams(self, hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None):
				torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
				if type(hparam_dict) is not dict or type(metric_dict) is not dict:
					raise TypeError('hparam_dict and metric_dict should be dictionary.')
				exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)

				self.file_writer.add_summary(exp)
				self.file_writer.add_summary(ssi)
				self.file_writer.add_summary(sei)
				for k, v in metric_dict.items():
					if v is not None:
						self.add_scalar(k, v)

To use this, when you create the SummaryWriter you should set log_dir=log_dir+'/[run_name]'. I have some example code and pictures to show how to use this below.

s = SummaryWriter(log_dir=f'{log_dir}/run1')
h_params = {'This': 1, 'is': 2, 'a': 3, 'test': 4}
metrics = {'accuracy/accuracy': None, 'loss/loss': None}

accuracy_values = [(1, 0.6), (2, 0.8), (3, 0.9), (4, 0.95)]
loss_values = [(1, 3), (2, 1), (3, 0.5), (4, 0.11)]
for step, v in accuracy_values:
	s.add_scalar('accuracy/accuracy', v, step)
for step, v in loss_values:
	s.add_scalar('loss/loss', v, step)

s.add_hparams(h_params, metrics)
s.close()

Something that I’m not sure if it’s a bug or not is that in the original code, any scalars are logged to the log_dir whilst hyper parameters are logged in their own directory and given a run_name.

(I’ve just posted this as hopefully this is useful to someone)

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Thanks, this is a massive lifesaver. I think there would definitely be a benefit to rewriting the PyTorch Tensorboard interface to make it more intuitive and increasing the detail of documentation. It shouldn’t be that hard to be able to log metrics on a per-epoch basis with hyperparameters.

“Something that I’m not sure if it’s a bug or not is that in the original code, any scalars are logged to the log_dir whilst hyper parameters are logged in their own directory and given a run_name.” As for this part, this also seems to be a weird default behavior of the add_hyperparameters option to use the log_dir along with the current timestamp (time.time()). See pytorch/writer.py at bf88a4dad5b3c06f43a9ce07a51ec0ab0dbfe2d3 · pytorch/pytorch · GitHub. A quick fix is to just delete those lines and just use the original log_dir.

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