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[oguiza (Ignacio Oguiza)]

40% faster training with a scikit-learn-like API for numpy arrays

If you use numpy arrays as input to your model, you may be interested in a new package I’ve created to speed up training.

The API looks like this:

dsets = NumpyDatasets(X, y=None, tfms=tfms, splits=splits, inplace=True)

You have more details on this link:

40+% faster training with a scikit-learn-like API for numpy arrays

TLDR: fastai2 is great, but if you use numpy arrays as input it may be slower than fastai v1. However, if you use NumpyDatasets/ NumpyDataLoader your training will be 33% faster than v1 and 40+% faster than v2. During the last couple of weeks, I’ve been porting timeseriesAI to fastai v2. timeseriesAI is a Practical Deep Learning for Time Series / Sequential Data package I built on top fastai v1, that takes X (and y) numpy arrays as input. The new package can be easily installed from pip (`pip…