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@JMGaljaard JMGaljaard released this 12 May 09:42
· 126 commits to main since this release
v0.2.0
50ab397

This release introduces the following changes:

  1. FederatedLearning datasets other than FashionMNIST failed to load without raising an Exception. Thereby resulting in a failed execution of experiments. This required a revision of the experiment configuration objects that were used by the learners, see also point 4.
    • To help detect issues like these from getting introduced undetected, a series of smoke tests were introduced that can be run locally.
  2. Distributed (DistributedDataParallel) experiments are now compatible again with KFLTK 🎉. See also #26.
  3. Configuration files with small floating-point numbers without a decimal (such as 10e-5) will no longer be parsed as a string by FedLearningConfig and DistLearningConfig objects. This was mainly an issue for the min_lr configuration parameter, which would result in an exception being raised after 50 epochs.
  4. The (flat) configuration objects for DistributedDataParallel and Federated learning experiments have been unified partially. These objects are now renamed to FedLearningConfig and DistLearningConfig respectively. ⚠️ Make sure to update your imports.
    • Moreover both classes now allow being directly parsed from a json file. Note that the FromYamlFile function has been renamed to the more pythonic name from_yaml
    • These objects now both make use of the Definitions Enum classes, allowing for more readable parsing errors when providing an incorrect type.
  5. A typo has been resolved in the jinja templates such that theaggregation is now properly set.
  6. Datasets for Federated experiments now make use of the test_batch_size, rather than using a default of 16. This parameter has also been introduced in the jinja template.
  7. the fltk.util.env module has been renamed to fltk.util.environment to add it to the repository. This also resolves #30
  8. A series of linter issues have been resolved (mostly warnings and some typos).

Issues closed

This version resolves the following issues. For more information read the changelog above.

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