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In the tutorial notebook, it is recommended that "For experimentation purposes, you can reduce the number of epochs in the tadgan.json file such that you reduce the number of training iterations."
This operation is risky in the sense that tadgan.json is in some sense considered the default pipeline file. If the users forget to change it back (which is very likely if they are only doing an experimental run in their first trial), it might lead to undertraining in later runs. Also, this operation is inconvenient, especially when the users want to try a couple hyper-parameter(s) to find out the best fit for their data sets.
One solution might be to remind the users in the tutorial notebook that they can first load the json files as dictionaries, change the hyper-parameter(s) inside and then pass it into the Orion factory method.
Alternatively, like many other classes (e.g. sklearn models), we can give the users the option to pass in additional keyword arguments (or as dictionaries) to overwrite the ones in the json files. That is, creating objects in these ways:
In pipeline jsons file, specify the global name for the hyperparameter, for example epochs and which primitive it maps to. A new section in the json file is proposed where we map keyword arguments to primitives and their respective hyperparameters
In Orion API, we can search for any primitive that has the specified hyperparameter name, and change that for all hyperparameters. In some cases, this will lead to an undesired behavior.
Description
In the tutorial notebook, it is recommended that "For experimentation purposes, you can reduce the number of
epochs
in thetadgan.json
file such that you reduce the number of training iterations."This operation is risky in the sense that
tadgan.json
is in some sense considered the default pipeline file. If the users forget to change it back (which is very likely if they are only doing an experimental run in their first trial), it might lead to undertraining in later runs. Also, this operation is inconvenient, especially when the users want to try a couple hyper-parameter(s) to find out the best fit for their data sets.One solution might be to remind the users in the tutorial notebook that they can first load the
json
files as dictionaries, change the hyper-parameter(s) inside and then pass it into theOrion
factory method.Alternatively, like many other classes (e.g.
sklearn
models), we can give the users the option to pass in additional keyword arguments (or as dictionaries) to overwrite the ones in thejson
files. That is, creating objects in these ways:or
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