configs/hparams_search/focus150_optuna.yaml

# @package _global_

# example hyperparameter optimization of some experiment with Optuna:
# python train.py -m hparams_search=mnist_optuna experiment=example

defaults:
 - override /datamodule: focus150.yaml
 - override /model: focus150.yaml
 - 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/mae_best"

name: "focus150_optuna"

# 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:
 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: focus150_hyperparameter_search

 # number of parallel workers
 n_jobs: 1

 # 'minimize' or 'maximize' the objective
 direction: minimize

 # total number of runs that will be executed
 n_trials: 25

 # choose Optuna hyperparameter sampler
 # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html
 sampler:
 _target_: optuna.samplers.TPESampler
 seed: 12345
 n_startup_trials: 10 # number of random sampling runs before optimization starts

 # define range of hyperparameters
 search_space:
 datamodule.batch_size:
 type: categorical
 choices: [32, 64, 128]
 model.lr:
 type: float
 low: 0.0001
 high: 0.2
 model.lin1_size:
 type: categorical
 choices: [32, 64, 128, 256, 512, 1024, 2048]
 model.lin2_size:
 type: categorical
 choices: [32, 64, 128, 256, 512, 1024, 2048]
 model.lin3_size:
 type: categorical
 choices: [32, 64, 128, 256, 512, 1024, 2048]