The EVer Evolving Optimizer
EVE: The EVer Evolving Deep Learning Optimizer
EVE is a new optimizer library built on top of PyTorch that combines the best of multiple state-of-the-art optimizer algorithms into one flexible, infinitely customizable super-optimizer.
The goal of EVE is not to provide one final, static optimizer, but rather an interface to a PyTorch optimizer that will continue to implement the latest, well-tested methods from modern research.
In preliminary testing, the current implementation of EVE was able to beat Adam and other near state-of-the-art optimizers without a significant increase in compute time. Here are some inital results from training a ResNet18 on the ImageNette (subset of ImageNet that encompasses a few hard to classify classes) 5 epoch challenge.
Adam (Final Accuracy = 40.00%)
EVE (Final Accuracy = 70.62%)
Here are a few animations demonstrating EVE's convergence properties on simple functions:
|2D Convex Surface||2D Non-Convex Surface||3D Surface with Saddle Point|
Installation and Getting Started
The simplest way to use EVE in your PyTorch models is to install it using pip:
pip install eve-optimizer
Then, the main EVE optimizer can be imported as follows:
from eve.optimizers import eveo3
This will import a function that returns a
torch.optim.Optimizer object, which can be used in the usual way.
The EVE library also provides a direct interface to other optimizers (like Ranger, RAdam, etc.) that were used in part or were built upon to create the main EVE optimizer. These can also be accessed from
eve.optimizers in the same way.
What Exactly is EVE?
At present, EVE implements (and combines) the following algorithms:
We are currently working on adding in the following variants as well:
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