Make TensorBoard aware of hyperparameters

Migrated from https://github.com/tensorflow/tensorflow/issues/7708.

@mmuneebs says:

I am wondering if it's possible to store the arguments that we pass to the training script to be stored as part of other summaries, and have a way to see those arguments/parameters as notes in a separate tab in Tensorboard.
Reason: It's often hard to track individual training runs and relate them to the training/network config if you change training and network parameters frequently which always happens during hyperparameter search. We can do it manually too but if included in Tensorboard, it would make it one go-to visualizer and comparison tool for everything.
One method of doing this that comes to mind is using the tf.app.flags.FLAGS arguments. This would keep everything standardized. Or we could also support argparse directly.
Is this something in line with Tensorboard's philosophy or is it too straightforward to be a special feature?

No files, tests, or implementation entry points are named. Start by reviewing the migrated issue and its discussion, including the proposed tf.app.flags.FLAGS and argparse inputs; done should mean training arguments are stored with summaries and viewable as notes in a separate TensorBoard tab.