Deep learning project seed

Use this seed to start new deep learning / ML projects.

Built in setup.py
Built in requirements
Examples with MNIST
Badges
Bibtex

Goals

The goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.

DELETE EVERYTHING ABOVE FOR YOUR PROJECT

Your Project Name

Description

What it does

How to run

First, install dependencies

# clone project   
git clone https://github.com/YourGithubName/deep-learning-project-template

# install project   
cd deep-learning-project-template 
pip install -e .   
pip install -r requirements.txt

Next, navigate to any file and run it.

# module folder
cd project

# run module (example: mnist as your main contribution)   
python lit_classifier_main.py

Imports

This project is setup as a package which means you can now easily import any file into any other file like so:

from project.datasets.mnist import mnist
from project.lit_classifier_main import LitClassifier
from pytorch_lightning import Trainer

# model
model = LitClassifier()

# data
train, val, test = mnist()

# train
trainer = Trainer()
trainer.fit(model, train, val)

# test using the best model!
trainer.test(test_dataloaders=test)

Fairness in Machine Learning

This project demonstrates how make fair machine learning models.

Notebooks

fairness-in-ml.ipynb: keras & TensorFlow implementation of Towards fairness in ML with adversarial networks.
fairness-in-torch.ipynb: PyTorch implementation of Fairness in Machine Learning with PyTorch.
playground/*: Various experiments.

Getting started

This repo uses conda's virtual environment for Python 3.

Install (mini)conda if not yet installed.

For MacOS:

$ wget http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh -O miniconda.sh
$ chmod +x miniconda.sh
$ ./miniconda.sh -b

cd into this directory and create the conda virtual environment for Python 3 from environment.yml:

$ conda env create -f environment.yml

Activate the virtual environment:

$ source activate fairness-in-ml

Install the fairness library:

$ python setup.py develop

Contributing

If you have applied these models to a different dataset or implemented any other fair models, consider submitting a Pull Request!

Citation

@article{YourName,
  title={Your Title},
  author={Your team},
  journal={Location},
  year={Year}
}

das-projects/fairDLRM