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Plugin support
This is a very early and evolving feature, but users who have a need to extend behavior can now do so with plugin loading.
Plugin creation
To create a plugin, extend the BasePlugin class and override the methods you want to change.
For example, let's say we made the ExampleLoggingPlugin class, and placed it in a file called plugins/example_plugin.py:
To activate a plugin, edit your train.json as follows:
"plugins": ["plugins.example_plugin.ExampleLoggingPlugin"],
The entries are in 'module-path-like' form to your class. For this example, plugins/example_plugin.py contains a class ExampleLoggingPlugin which extends BasePlugin. See this example for more details.
You can add as many plugins as you want, and each plugin only needs to implement the functions you wish.
Data is passed to the plugin functions as a list of variables via kwargs, but it's still an open question which data should be passed in. In any case it should be relevant to the function, e.g. on_epoch_start() should not receive batch data or anything specific to a step, except for epoch and global_step.
Quick example
A basic implementation of a plugin that only uses the "end of an epoch" function will look something like this:
from plugins.plugins import BasePlugin
class MyPlugin(BasePlugin):
def on_epoch_end(self, **kwargs):
print(f"hello world, this is a plugin at the end of epoch number: {kwargs['epoch']}")