I'm using Optuna to optimize LightGBM hyperparameters, and I'm running into an issue with the variability of best_iteration across different random seeds. Current Setup I train multiple models with ...

I’m running Optuna to tune hyperparameters for a TabM regression model (10 trials) on Kaggle (GPU: Tesla P100) to minimize RMSE. The optimization runs fine — all trials complete — but right after ...

I have done a XGBoost model, and used Optuna to get the best parameters. I saved the trained model and the features used. My question is, when I got to load the model and use it when making ...

I'm running into a segmentation fault when training a Transformer model with PyTorch 2.6.0 and Optuna on CUDA (12.4). The exact same code used to work fine the issue appeared only after using Optuna. ...

I am using Optuna for hyperparameter tuning. I get messages as shown below: Trial 15 finished with value: 6.226334123011727 and parameters: {'iterations': 1100, 'learning_rate': 0.04262148853587423, '...

I'm trying to use optuna to find good hyperparameters for a fine-tuning task I'm doing with some different language models. My actual code is more complex, but here's a MWE: import torch import optuna ...

I used TPESampler and set it as follows while optimizing with optuna: sampler=optuna.samplers.TPESampler(multivariate=True, n_startup_trials=10, seed=None). But in the 10 startup_trials process, it ...

I created a 5 GPU Cluster using three nodes/machines locally using the tensorflow.distributed.MultiWorkerMirrored Strategy. One machine has the Apple M1 Pro Metals GPU, the other two nodes has NVIDIA ...

My impression is that every trial is run for one step. Then some trials are pruned and the remaining continue for another step and so on. However, the logs show: Trial 0 completed Trial 1 completed ...

I have some code to train a RL agent in jax. The code runs fine. To tune the hyperparameters I would like to use the optuna plugin of hydra since my project is based on the latter. To this end, I ...

I am using Optuna to optimize the parameters of a non-ML task. Now, each trial consists in processing several files in sequence, each of which gets a score. The scores are summed cumulatively in order ...

I'm trying to use FLAML for hyperparameter tuning of my model, and I would like to see how each hyperparameter contributes to the objective value. Similar to Optuna's get_param_importances or ...

I'm trying to set up Optuna for hyperparam optimization. I have 2 main doubt/troubles I don't know if are correlated. When lunching the script with 20 or 100 trials no matter, its run but in some ...

This problem has been bothering me for a long time. I am using optuna for automatic parameter tuning of deep learning models, and the objective function returns the average AUC of five folds. Unable ...

This comment is from this issue: https://github.com/optuna/optuna/issues/5397 I found out what the issue was, If you load a study using "optuna.study.load_study" the settings for the study ...