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.
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To install latest version of CORDS package using PyPI:
pip install -i https://test.pypi.org/simple/ cords
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To install using source:
git clone https://github.com/decile-team/cords.git cd cords pip install -r requirements/requirements.txt
NOTE: Please make sure to enter the space between simple/ and cords in the above command while installing CORDS package using PyPI
The below link contains the jupyter notebook link for cifar10 timing analysis experiments
- "numpy >= 1.14.2",
- "scipy >= 1.0.0",
- "numba >= 0.43.0",
- "tqdm >= 4.24.0",
- "torch >= 1.4.0",
- "apricot-select >= 0.6.0"