A software framework for HyperParameters Optimization
Project description
Zellij is an open source Python framework for HyperParameter Optimization (HPO) which was orginally dedicated to Fractal Decomposition based algorithms [1] [2]. It includes tools to define mixed search space, manage objective functions, and a few algorithms. To implements metaheuristics and other optimization methods, Zellij uses DEAP[3] for the Evolutionary Algorithms part and BoTorch [4] for Bayesian Optimization. Zellij is defined as an easy to use and modular framework, based on Python object oriented paradigm.
See documentation.
Install Zellij
Original version
$ pip install zellij
Distributed Zellij
This version requires a MPI library, such as MPICH or Open MPI. It is based on mpi4py
$ pip install zellij[mpi]
User will then be able to use the MPI
option of the Loss
decorator.
@Loss(MPI=True)
Then the python script must be executed using mpiexec
:
$ mpiexec -machinefile <path/to/hostfile> -n <number of processes> python3 <path/to/python/script>
Dependencies
Original version
- Python >=3.6
- numpy=>1.21.4
- DEAP>=1.3.1
- botorch>=0.6.3.1
- gpytorch>=1.6.0
- pandas>=1.3.4
- enlighten>=1.10.2
MPI version
- Python >=3.6
- numpy=>1.21.4
- DEAP>=1.3.1
- botorch>=0.6.3.1
- gpytorch>=1.6.0
- pandas>=1.3.4
- enlighten>=1.10.2
- mpi4py>=3.1.2
Contributors
Design
- Thomas Firmin: thomas.firmin@univ-lille.fr
- El-Ghazali Talbi: el-ghazali.talbi@univ-lille.fr
References
[1] Nakib, A., Ouchraa, S., Shvai, N., Souquet, L. & Talbi, E.-G. Deterministic metaheuristic based on fractal decomposition for large-scale optimization. Applied Soft Computing 61, 468–485 (2017).
[2] Demirhan, M., Özdamar, L., Helvacıoğlu, L. & Birbil, Ş. I. FRACTOP: A Geometric Partitioning Metaheuristic for Global Optimization. Journal of Global Optimization 14, 415–436 (1999).
[3] Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau and Christian Gagné, "DEAP: Evolutionary Algorithms Made Easy", Journal of Machine Learning Research, vol. 13, pp. 2171-2175, jul 2012.
[4] M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in Neural Information Processing Systems 33, 2020.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.