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The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

0.5.4

3 years ago
  • Fix update_hyperparams for SKLearnWrapper (critical)

0.5.3

3 years ago
  • Tree Parzen Estimators
  • Scipy Distributions Support

0.5.2

3 years ago
  • Partial fit in SKLearnWrapper
  • Setup with context
  • All the changes from 0.5.1 (was not uploaded to pypi)

0.5.1

3 years ago
  • Fix warnings.warn for python 3.6 & python 3.7
  • Add with_context method to BaseStep to force the use of a given context in the pipeline
  • Use dependency injection by setting the execution context service locator : ExecutionContext().set_service_locator({ BaseType: instance })

0.5.0

3 years ago
  • Added streaming pipelines for parallel processing.
  • Implemented neat .apply() logic in the steps to recursively apply any function if it exists.
  • Refactored the BaseStep so as to make it inherit from multiple smaller classes to separate the logic and allow for creating various steps such as the BaseTransformer that doesn't need any fitting: the BaseStep is thus a BaseTransformer with aditionnal fitting behavior that is added by inheriting another mixin.
  • Some bug were fixed.

0.4.1

3 years ago

0.4.0

3 years ago

0.3.4

3 years ago

0.3.3

3 years ago

0.3.2

3 years ago