alteryx / alteryx/evalml

Performance optimization: memoize/cache fitted components and predictions during automl

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#466 4 comentarios 1 reacción 1 asignado Reclamado por @asniyaz Ver en GitHub
needs design new feature performance spike
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Python
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Descripción

A feature evalml could support down the road is the ability to cache the output of each combination of components our pipelines have trained, so that if that component string is used again during automl, its fetched from the cache rather than recomputed.

Sklearn supports this functionality: [see the `memory` parameter on `Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), and also [this issue in their repo](https://github.com/scikit-learn/scikit-learn/issues/9007).

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