optuna.integrationΒΆ
Chainer extension to prune unpromising trials. |
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A wrapper of |
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Catalyst callback to prune unpromising trials. |
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A Sampler using cma library as the backend. |
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Wrapper class of PyCmaSampler for backward compatibility. |
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FastAI callback to prune unpromising trials for fastai. |
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Keras callback to prune unpromising trials. |
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Callback for LightGBM to prune unpromising trials. |
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Wrapper of LightGBM Training API to tune hyperparameters. |
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Hyperparameter tuner for LightGBM. |
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Hyperparameter tuner for LightGBM with cross-validation. |
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Callback to track Optuna trials with MLflow. |
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MXNet callback to prune unpromising trials. |
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PyTorch Ignite handler to prune unpromising trials. |
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PyTorch Lightning callback to prune unpromising trials. |
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Sampler using Scikit-Optimize as the backend. |
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Callback to track Optuna trials with TensorBoard. |
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TensorFlow SessionRunHook to prune unpromising trials. |
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tf.keras callback to prune unpromising trials. |
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Callback for XGBoost to prune unpromising trials. |
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Hyperparameter search with cross-validation. |
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AllenNLP extension to use optuna with Jsonnet config file. |
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Save JSON config file after updating with parameters from the best trial in the study. |
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AllenNLP callback to prune unpromising trials. |