Hyperparameter tuning on numerai data with PyTorch Lightning and weights & biases

To compare the previously described approach of hyperparameter tuning using fastai and wandb, today we’ll see how to tackle the same approach, but using PyTorch Lightning instead of fastai. The goal is to have an automated hyperparameter tuning pipeline running on the Numerai data set. What is Numerai? Numerai is a hedge fund which trades stocks in a market neutral fashion. That means that they try to make money without having a lot of risk for their customers.
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Hyperparameter tuning on numerai data with fastai and weights & biases

Today we will try to tackle the Numerai tournament using the fastai deep learning library. However, as the results likely depend on many different hyperparameters, let’s take advantage of the weights and biases library and their sweeps API. Sweeps are hyperparameter runs which test out different combinations of your model’s hyperparameters. What is Numerai? Numerai is a hedge fund which trades stocks in a market neutral fashion. That means that they try to make money without having a lot of risk for their customers.
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