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Optuna is an open-source hyperparameter optimization framework to automate hyperparameter search. It is used with any machine learning or deep learning framework. Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importance, etc. in graphs and tables. With Optuna, you can define a search space of hyperparameters, specify an objective function to optimize, and let Optuna find the optimal values for the hyperparameters.

• Software Developers
• Data Engineers
• Data Scientists
• Data Analyst

Optuna offers a wide range of features that make it easy for data scientists and machine learning engineers. Here are some of the key features of Optuna:

1. Automatic hyperparameter optimization: Optuna automates the process of hyperparameter optimization by searching for the best set of hyperparameters for your machine-learning model.
2. Efficient optimization algorithms: Optuna uses state-of-the-art optimization algorithms, including TPE (Tree-structured Parzen Estimator), CMA-ES (Covariance Matrix Adaptation Evolution Strategy), and NSGA-II (Non-dominated Sorting Genetic Algorithm II).
3. Distributed and parallel computing: Optuna supports distributed and parallel computing, which can speed up hyperparameter optimization when working with large datasets or complex models.
4. Customizable search spaces: With Optuna, you can define a custom search space for your hyperparameters. This means you can specify constraints on the values each hyperparameter can take, and Optuna will only search within those constraints.
5. Visualization: Optuna provides a variety of visualization tools that help you understand the optimization process and results. These tools include plot_optimization_history(), plot_parallel_coordinate(), and plot_slice().
6. Integration with popular machine learning frameworks: Optuna can be integrated with popular machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn, making it easy to use with your existing models.
7. Customizable optimization strategies: Optuna allows you to customize the optimization strategy for your specific problem by defining your own pruning strategies, samplers, or trials.

• Minimum memory: 8 GB
• Minimum vCPU: 2 vCPUs
• Operating System: Ubuntu 20.04
• Storage: 6GB
• Python: Currently Python 3.7, 3.8, 3.9, 3.10, and 3.11 are supported. Some plugins do not yet support Python 3.11.
• Pip: version latest 23.0.1

1. Eager search spaces: Automated search for optimal hyperparameters using Python conditionals, loops, and syntax
2. State-of-the-art algorithms: Efficiently search large spaces and prune unpromising trials for faster results
3. Easy parallelization: Parallelize hyperparameter searches over multiple threads or processes without modifying code

For further accessing the web interface of Optuna Dashboard follow the steps:

1. Create a python file
   import optuna
   def objective(trial):
   x = trial.suggest_float("x", -100, 100)
   y = trial.suggest_categorical("y", [-1, 0, 1])
   return x**2 + y
   if __name__ == "__main__":
   study = optuna.create_study( storage="sqlite:///db.sqlite3", # Specify the storage URL here. study_name="quadratic-simple" )
   study.optimize(objective, n_trials=100)
   print(f"Best value: {study.best_value} (params: {study.best_params})")
2. Specify storage or database
3. Run this command to access the web interface of Optuna Dashboard on port 8080 optuna-dashboard sqlite:///db.sqlite3
   Navigate to the URL and access web-interface by http://ip-addr:8080