Transfer Trials to other DB#

local_study = optuna.load_study(study_name="foo", storage="SQLITE URL")
remote_study = optuna.create_study(study_name="foo", storage="REMOTE SQL DB URL")

for trial in local_study.trials:

Save Plot to Disk#

fig = optuna.visualization.plot_slice(study)
fig.write_image("<filename>.png")  # save to image
fig.write_html("<filename>.html")  # save to html

Extend HP-space#

It might be that the HP space was selected to narrow and we want to extend it during a study to continue the HP search with an extended HP space. This is a bit difficult in Optuna.

This is known to the Optuna maintainers but needs someone to write a pull request. Details here: https://github.com/optuna/optuna/issues/4037

A workaround is as following:

Lets say we had a hyperparameterspace with num_epochs from 2 to 4. Now we did some trials for this study but want to extend num_epochs to 2 to 7. This is done like this:

# load old study
study_name = "my_study_01"
study = optuna.create_study(

# iterate trials from old study and modify (extend) the distributions
new_trials = []
for trial in study.trials:
    # this is only valid for int distributions
    # if it is a float distribution this needs to be changed
    trial.distributions["num_epochs"] = optuna.distributions.IntDistribution(2, 7)


# delete old study from memory
# for some reason I always do this because there was some trouble in the past
# but I do not remember why - maybe because there is a DB connection behind it
del study

# create new study
study_name_new = "my_study_02"
new_study = optuna.create_study(

# add old trials to new trial
for trial in new_trials:

# now also modify your training code
# this would be the following change for example
# change from
# num_epochs = trial.suggest_int("num_epochs", 2, 4)
# to
# num_epochs = trial.suggest_int("num_epochs", 2, 7)

# now you can continue training on "study_name_new" with the old knowledge but extended HP space

More Topics / To-do#

  • choose HP space

    • when to use category

    • when to use step

    • how many?

  • monitor HP space / slice plot

  • monitor training

  • only train for x non failed iteration

  • xval vs. not xval