kodu_optim 0.1.2

A distributed optimization package

Kodu-Optim

Kodu-Optim is a distributed system designed to leverage Optuna for hyperparameter optimization across multiple compute nodes. It enables efficient and scalable optimization for machine learning models and other computational tasks.

Features

• Distributed hyperparameter optimization using Optuna.
• Scalable architecture for large-scale experiments.
• Easy integration with existing machine learning workflows.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/kodu-optim.git
   cd kodu-optim
   

2. Install dependencies:

   pip install -r requirements.txt
   

Usage

1. Start the distributed system:

   python start_distributed.py
   

2. Define your Optuna study and objective function in your script:

   import optuna
   
   def objective(trial):
       x = trial.suggest_float("x", -10, 10)
       return x ** 2
   
   study = optuna.create_study(direction="minimize")
   study.optimize(objective, n_trials=100)
   

3. Run your optimization script across the distributed nodes.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.