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Neuraxio / Neuraxle

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.
https://www.neuraxle.org/
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Feature: Have a step store using metaclass-based registration and DLS-based declaration. #513

Closed guillaume-chevalier closed 1 year ago

guillaume-chevalier commented 3 years ago

Is your feature request related to a problem? Please describe. Meta-describing a pipeline from, say, a loaded configuration json would require to compose the objects back together. To do so, from object names, we'd need an object store to save the objects to upon creating them, including custom objects.

Describe the solution you'd like BaseStep to register objects to the global store using a descriptor.

Describe alternatives you've considered The "Pattern: language integrated registration" of the article found below as well. But it adds too much boilerplate.

Additional context See this article, especially the sections:

Metaclass-based registration could be used as well for specifying hyperparameter distributions and more objects. This could be like Orion strings in the Orion framework.

stale[bot] commented 1 year ago

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