gradboard 0.1.2

Easily snowboard down gnarly loss gradients

gradboard

Easily snowboard down gnarly loss gradients

Getting started

You can install gradboard with

pip install gradboard

PyTorch is a peer dependency of gradboard, which means

• You will need to make sure you have PyTorch installed in order to use gradboard
• PyTorch will not be installed automatically when you install gradboard

We take this approach because PyTorch versioning is environment-specific and we don't know where you will want to use gradboard. If we automatically install PyTorch for you, there's a good chance we would get it wrong!

Therefore, please also make sure you install PyTorch.

Usage examples

Decent model training outcomes without tuning hyperparameters

gradboard includes

• An implementation of AdamS as proposed in Xie et al. (2023) "On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective" (https://openreview.net/pdf?id=vnGcubtzR1), which in practice makes model training more robust to the weight decay setting.
• Utilities for implementing popular learning rate schedules
• An implementation of an automatic max/min learning rate finder based on Smith (2017) "Cyclical Learning Rates for Training Neural Networks" (https://arxiv.org/abs/1506.01186)
• Sensible defaults

In practice this means that you can train a neural network and get decent performance right out of the box, just by using the PASS (point-and-shoot scheduler), even for unfamiliar architectures or problem domains.