网页
综合
更新时间 2026-11-14 16:52:35
Optuna - A hyperparameter optimization framework
An open source hyperparameter optimization framework to automate hyperparameter search
Automated search for optimal hyperparameters using Python conditionals, loops, and syntax
Efficiently search large spaces and prune unpromising trials for faster results
Parallelize hyperparameter searches over multiple threads or processes without modifying code
Optuna can be installed with pip. Python 3.10 or newer is supported.
Optuna is framework agnostic. You can use it with any machine learning or deep learning framework.
A simple optimization problem:
colab.research.google Open in Colab Open in Colab
You can optimize PyTorch hyperparameters, such as the number of layers and the number of hidden nodes in each layer, in three steps:
You can optimize TensorFlow hyperparameters, such as the number of layers and the number of hidden nodes in each layer, in three steps:
You can optimize Keras hyperparameters, such as the number of filters and kernel size, in three steps:
You can optimize Scikit-Learn hyperparameters, such as the C parameter of SVC and the max_depth of the RandomForestClassifier, in three steps:
You can optimize XGBoost hyperparameters, such as the booster type and alpha, in three steps:
You can optimize LightGBM hyperparameters, such as boosting type and the number of leaves, in three steps:
Check more examples including PyTorch Ignite, Dask-ML and MLFlow at our GitHub repository. It also provides the visualization demo as follows:
Rustuna is a faster implementation of Optuna written in Rust. It keeps the API you already know, and rewrites the parts that start to hurt at scale: sampling speed and memory efficiency.
Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importances, etc. in graphs and tables.
Optuna Dashboard is also available as extensions for Jupyter Lab and Visual Studio Code.
OptunaHub is a feature-sharing platform for Optuna. Users can freely use registered features, and contributors can register the features they implement. The following example uses AutoSampler on OptunaHub, which automatically selects a proper sampler from those implemented in Optuna.
If you use Optuna in a scientific publication, please use the following citation:
If you use OptunaHub in a scientific publication, please use the following citation:
Automated search for optimal hyperparameters using Python conditionals, loops, and syntax
Efficiently search large spaces and prune unpromising trials for faster results
Parallelize hyperparameter searches over multiple threads or processes without modifying code
Optuna can be installed with pip. Python 3.10 or newer is supported.
Optuna is framework agnostic. You can use it with any machine learning or deep learning framework.
A simple optimization problem:
colab.research.google Open in Colab Open in Colab
You can optimize PyTorch hyperparameters, such as the number of layers and the number of hidden nodes in each layer, in three steps:
You can optimize TensorFlow hyperparameters, such as the number of layers and the number of hidden nodes in each layer, in three steps:
You can optimize Keras hyperparameters, such as the number of filters and kernel size, in three steps:
You can optimize Scikit-Learn hyperparameters, such as the C parameter of SVC and the max_depth of the RandomForestClassifier, in three steps:
You can optimize XGBoost hyperparameters, such as the booster type and alpha, in three steps:
You can optimize LightGBM hyperparameters, such as boosting type and the number of leaves, in three steps:
Check more examples including PyTorch Ignite, Dask-ML and MLFlow at our GitHub repository. It also provides the visualization demo as follows:
Rustuna is a faster implementation of Optuna written in Rust. It keeps the API you already know, and rewrites the parts that start to hurt at scale: sampling speed and memory efficiency.
Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importances, etc. in graphs and tables.
Optuna Dashboard is also available as extensions for Jupyter Lab and Visual Studio Code.
OptunaHub is a feature-sharing platform for Optuna. Users can freely use registered features, and contributors can register the features they implement. The following example uses AutoSampler on OptunaHub, which automatically selects a proper sampler from those implemented in Optuna.
If you use Optuna in a scientific publication, please use the following citation:
If you use OptunaHub in a scientific publication, please use the following citation:
本页为82搜索生成的网页快照，内容来自站内索引；演示数据的原文链接仅作示例。