Tensorboard is a service for tracking experimental results during or after training. It is part of the larger Tensorflow project but can be used independently of it.
tensorboard package can either be installed directly with
pip install tensorboard
or with PyKEEN by using the
tensorboard extra in
pip install pykeen[tensorboard].
Tensorboard logs can created without actually installing tensorboard itself. However, if you want to view and interact with the data created via the tracker, it must be installed.
tensorboard web application can be started from the command line with
$ tensorboard --logdir=~/.data/pykeen/logs/tensorboard/
where the value passed to the
--logdir is location of log directory. By default, PyKEEN logs to
~/.data/pykeen/logs/tensorboard/, but this is configurable.
The Tensorboard can then be accessed via a browser at: http://localhost:6006/
It is not required for the Tensorboard process to be running while the training is happening. Indeed, it only needs to be started once you want to interact with and view the logs. It can be stopped at any time and the logs will persist in the filesystem.
Minimal Pipeline Example
The tensorboard tracker can be used during training with the
pykeen.pipeline.pipeline() as follows:
from pykeen.pipeline import pipeline pipeline_result = pipeline( model='RotatE', dataset='Kinships', result_tracker='tensorboard', )
It is placed in a subdirectory of
pystow default data directory of PyKEEN called
which will likely be at
~/.data/pykeen/logs/tensorboard on your system. The file is named based on the
current time if no alternative is provided.
Specifying a Log Name
If you want to specify the name of the log file in the default directory, use the
from pykeen.pipeline import pipeline pipeline_result = pipeline( model='RotatE', dataset='Kinships', result_tracker='tensorboard', result_tracker_kwargs=dict( experiment_name='rotate-kinships', ), )
Specifying a Custom Log Directory
If you want to specify a custom directory to store the tensorboard logs, use the
from pykeen.pipeline import pipeline pipeline_result = pipeline( model='RotatE', dataset='Kinships', result_tracker='tensorboard', result_tracker_kwargs=dict( experiment_path='tb-logs/rotate-kinships', ), )
Please be aware that if you re-run an experiment using the same directory, then the logs will be combined. It is advisable to use a unique sub-directory for each experiment to allow for easy comparison.
Minimal HPO Pipeline Example
Tensorboard tracking can also be used in conjunction with a HPO pipeline as follows:
from pykeen.pipeline import pipeline hpo_pipeline_result = hpo_pipeline( n_trials=30, dataset='Nations', model='TransE', result_tracker='tensorboard', )
This provides a way to compare directly between different trails and parameter configurations. Please not that it is recommended to leave the experiment name as the default value here to allow for a directory to be created per trail.