Migrated from https://github.com/tensorflow/tensorflow/issues/7385.
so, any plans to support this ? :) it would be very welcome
I had a look at the code and I think it doesn't require too much knowledge of the inner workings. I think this part, where it takes a path_filter, is the relevant one. My confusion is that it is called from both event_accumulator.py and the plugin_event_accumulator.
So, I would be happy to contribute this small feature, provided that someone from the team can:
event_accumulator and its corresponding plugin_...To re-state the options:
This feature would help a lot.
Once you get to over 15 different models, the tensorboard takes too long to load and fails to load all the logs often.
Also the colors cycle start to get confusing
Totally agree. :) Wouldn't hurt if one could just use wildcards e.g.
tensorboard --logdir modelname_*/
Any plans to support this feature in 2019? It would be most welcome.
Also need this feature.
Here's a workaround for specifying path patterns with some bash-fu and a "discouraged" --logdir_spec option which supports loading a comma-separated list of multiple directories :
tensorboard --logdir_spec $(ls -m -d ./runs/2020-01-* | tr -d ' \n')
We use 2 ls flags here: -m sets delimiter to , (comma, space) and -d lists directories without their contents.
It assumes there are no commas or spaces in your log directory paths. You can use find for more complex cases.
More ways to concatenate list of paths with a comma
@shtratos: The main point of this feature request is that the globs be
resolved dynamically by TensorBoard, such that if you were to add a new
./runs/2020-01-* directory at runtime it would show up without having
to restart the process.
If you鈥檙e satisfied with invocation-time glob resolution, you can use
the multitb shell function from the following comment, which is safer,
does not have restrictions on the characters in your path name, does not
use --logdir_spec, and works even if you have runs of the same name in
different sub-logdirs:
https://github.com/tensorflow/tensorboard/issues/179#issuecomment-518729885
Most helpful comment
Totally agree. :) Wouldn't hurt if one could just use wildcards e.g.