Reduce end to end training time from days to hours and hours to minutes using coresets and data selection. CORDS implements a number of state of the art data subset selection algorithms and coreset algorithms. Some of the algorithms currently implemented with CORDS include:

• GLISTER [1]
• GradMatchOMP [2]
• GradMatchFixed [2]
• CRAIG [2,3]
• SubmodularSelection [4,5,6]
  • Facility Location
  • Feature Based Functions
  • Coverage
  • Diversity
• RandomSelection

[1] Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, and Rishabh Iyer, GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning, 35th AAAI Conference on Artificial Intelligence, AAAI 2021

[2] S Durga, Krishnateja Killamsetty, Abir De, Ganesh Ramakrishnan, Baharan Mirzasoleiman, Rishabh Iyer, Grad-Match: A Gradient Matching based Data Selection Framework for Efficient Learning

[3] Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec. Coresets for Data-efficient Training of Machine Learning Models. In International Conference on Machine Learning (ICML), July 2020.

[4] Kai Wei, Rishabh Iyer, Jeff Bilmes, Submodularity in data subset selection and active learning, International Conference on Machine Learning (ICML) 2015:

[5] Vishal Kaushal, Rishabh Iyer, Suraj Kothiwade, Rohan Mahadev, Khoshrav Doctor, and Ganesh Ramakrishnan, Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision, 7th IEEE Winter Conference on Applications of Computer Vision (WACV), 2019 Hawaii, USA

[6] Wei, Kai, et al. "Submodular subset selection for large-scale speech training data." 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014.

1. To install latest version of CORDS package using PyPI:

   pip install -i https://test.pypi.org/simple/ cords

2. To install using source:

   git clone https://github.com/decile-team/cords.git
   cd cords
   pip install -r requirements/requirements.txt

The below link contains the jupyter notebook link for cifar10 timing analysis experiments

CIFAR10 Notebook

1. "numpy >= 1.14.2",
2. "scipy >= 1.0.0",
3. "numba >= 0.43.0",
4. "tqdm >= 4.24.0",
5. "torch >= 1.4.0",
6. "apricot-select >= 0.6.0"