| # @package _global_ | |
| # example hyperparameter optimization of some experiment with Optuna: | |
| # python train.py -m hparams_search=mnist_optuna experiment=example | |
| defaults: | |
| - override /hydra/sweeper: optuna | |
| # choose metric which will be optimized by Optuna | |
| # make sure this is the correct name of some metric logged in lightning module! | |
| optimized_metric: "val/acc_best" | |
| # here we define Optuna hyperparameter search | |
| # it optimizes for value returned from function with @hydra.main decorator | |
| # docs: https://hydra.cc/docs/next/plugins/optuna_sweeper | |
| hydra: | |
| mode: "MULTIRUN" # set hydra to multirun by default if this config is attached | |
| sweeper: | |
| _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper | |
| # storage URL to persist optimization results | |
| # for example, you can use SQLite if you set 'sqlite:///example.db' | |
| storage: null | |
| # name of the study to persist optimization results | |
| study_name: null | |
| # number of parallel workers | |
| n_jobs: 1 | |
| # 'minimize' or 'maximize' the objective | |
| direction: maximize | |
| # total number of runs that will be executed | |
| n_trials: 20 | |
| # choose Optuna hyperparameter sampler | |
| # you can choose bayesian sampler (tpe), random search (without optimization), grid sampler, and others | |
| # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html | |
| sampler: | |
| _target_: optuna.samplers.TPESampler | |
| seed: 1234 | |
| n_startup_trials: 10 # number of random sampling runs before optimization starts | |
| # define hyperparameter search space | |
| params: | |
| model.optimizer.lr: interval(0.0001, 0.1) | |
| data.batch_size: choice(32, 64, 128, 256) | |
| model.net.lin1_size: choice(64, 128, 256) | |
| model.net.lin2_size: choice(64, 128, 256) | |
| model.net.lin3_size: choice(32, 64, 128, 256) | |