nadir 0.0.2

Nadir is a library of bleeding-edge DL optimisers built for speed and functionality in PyTorch for researchers

Nadir

Nadir (pronounced nay-di-ah) is derived from the arabic word nazir, and means "the lowest point of a space". In optimisation problems, it is equivalent to the point of minimum. If you are a machine learning enthusiast, a data scientist or an AI practitioner, you know how important it is to use the best optimization algorithms to train your models. The purpose of this library is to help optimize machine learning models and enable them to reach the point of nadir in the appropriate context.

PyTorch is a popular machine learning framework that provides a flexible and efficient way of building and training deep neural networks. This library, Nadir, is built on top of PyTorch to provide high-performing general-purpose optimisation algorithms.

:warning: Currently in Developement Beta version with every update having breaking changes; user discreation and caution advised! :warning:

Supported Optimisers

Optimiser	Paper
SGD	https://paperswithcode.com/method/sgd
Momentum	https://paperswithcode.com/method/sgd-with-momentum
Adagrad	https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf
RMSProp	https://paperswithcode.com/method/rmsprop
Adam	https://arxiv.org/abs/1412.6980v9
Adamax	https://arxiv.org/abs/1412.6980v9
Adadelta	https://arxiv.org/abs/1212.5701v1

Installation

Nadir is on the PyPi packaging Index! :partying_face:

Simply run the following command on your terminal and start using Nadir now!

$ pip install nadir

Usage

import nadir as nd

# some model setup here...
model = ...

# set up your Nadir optimiser
config = nd.SGDConfig(lr=learning_rate)
optimizer = nd.SGD(model.parameters(), config)