Describe Usage of Avalanche updaters and their Limitations #202
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610v4nn1
previously approved these changes
Apr 24, 2023
| @@ -4,7 +4,7 @@ Getting Started | |||
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| This section covers the usage of Renate from the installation to the training | |||
| of a model. The content is intended to explain the basic steps to be taken when creating a new | |||
| training pipeline based on Rente. | |||
| training pipeline based on Renate. | |||
| :doc:`how_to_run_training`. You can select an Avalanche updater by passing the respective string to the ``updater`` | ||
| argument of :py:func:`~renate.training.training.run_training_job`. The available Avalanche options are | ||
| ``"Avalanche-ER"``, ``"Avalanche-EWC"``, ``"Avalanche-LwF"``, and ``"Avalanche-iCaRL"``. | ||
| More details about these algorithms are given in :doc:`supported_algorithms`. |
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More updaters can be created if needed.
| No Multi-Fidelity HPO | ||
| --------------------- | ||
| Currently, we do not support multi-fidelity hyperparameter optimization with Avalanche updaters. For that reason, | ||
| please do not use ``asha`` as a scheduler but use ``random`` or ``bo`` instead. |
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It would be great to link the HPO page here
610v4nn1
approved these changes
Apr 24, 2023
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