Neptune is a graphical tool for tracking the results of machine learning. PyKEEN integrates Neptune into the pipeline and HPO pipeline.
To use it, you’ll first have to install Neptune’s client with
pip install neptune-clientor install PyKEEN with the
pip install pykeen[neptune].
Create an account at Neptune.
Get an API token following this tutorial.
[Optional] Set the
NEPTUNE_API_TOKENenvironment variable to your API token.
[Optional] Create a new project by following this tutorial for project and user management. Neptune automatically creates a project for all new users called
sandboxwhich you can directly use.
This example shows using Neptune with the
experiment_name must be set.
from pykeen.pipeline import pipeline pipeline_result = pipeline( model='RotatE', dataset='Kinships', result_tracker='neptune', result_tracker_kwargs=dict( project_qualified_name='cthoyt/sandbox', experiment_name='Tutorial Training of RotatE on Kinships', ), )
If you haven’t set the
NEPTUNE_API_TOKEN environment variable, the
a mandatory key.
In the Neptune web application, you’ll see that experiments are assigned an ID. This means you can re-use the same
ID to group different sub-experiments together using the
experiment_id keyword argument instead of
from pykeen.pipeline import pipeline experiment_id = 4 # if doesn't already exist, will throw an error! pipeline_result = pipeline( model='RotatE', dataset='Kinships', result_tracker='neptune' result_tracker_kwargs=dict( project_qualified_name='cthoyt/sandbox', experiment_id=4, ), )
Don’t worry - you can keep using the
experiment_name argument and the experiment’s identifier will
be automatically looked up eah time.