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Trademarks: This software listing is packaged by Data Science Dojo. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

About the offer:

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.

Who benefits from this offer:

Software Developers
Data Engineers
Data Scientists
Data Analyst

What is included in this offer:

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:

Automatic hyperparameter optimization: Optuna automates the process of hyperparameter optimization by searching for the best set of hyperparameters for your machine-learning model.
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).
Distributed and parallel computing: Optuna supports distributed and parallel computing, which can speed up hyperparameter optimization when working with large datasets or complex models.
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.
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().
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.
Customizable optimization strategies: Optuna allows you to customize the optimization strategy for your specific problem by defining your own pruning strategies, samplers, or trials.

Technical Specifications:

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

Principles:

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

Getting Started:

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

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})")
Specify storage or database
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

Optuna packaged by Data Science DojoData Science Dojo

Optuna packaged by Data Science Dojo

Data Science Dojo

Optuna packaged by Data Science Dojo

Data Science Dojo

Optuna packaged by Data Science Dojo
Data Science Dojo