optuna.storages.RedisStorage
- class optuna.storages.RedisStorage(url, *, heartbeat_interval=None, grace_period=None, failed_trial_callback=None)[source]
Storage class for Redis backend.
Note that library users can instantiate this class, but the attributes provided by this class are not supposed to be directly accessed by them.
Example
We create an
RedisStorage
instance using the given redis database URL.import optuna def objective(trial): ... storage = optuna.storages.RedisStorage( url="redis://passwd@localhost:port/db", ) study = optuna.create_study(storage=storage) study.optimize(objective)
- Parameters
url (str) – URL of the redis storage, password and db are optional. (ie: redis://localhost:6379)
heartbeat_interval (Optional[int]) –
Interval to record the heartbeat. It is recorded every
interval
seconds.heartbeat_interval
must beNone
or a positive integer.Note
The heartbeat is supposed to be used with
optimize()
. If you useask()
andtell()
instead, it will not work.grace_period (Optional[int]) – Grace period before a running trial is failed from the last heartbeat.
grace_period
must beNone
or a positive integer. If it isNone
, the grace period will be 2 * heartbeat_interval.failed_trial_callback (Optional[Callable[[optuna.Study, FrozenTrial], None]]) –
A callback function that is invoked after failing each stale trial. The function must accept two parameters with the following types in this order:
Study
andFrozenTrial
.Note
The procedure to fail existing stale trials is called just before asking the study for a new trial.
Note
If you use plan to use Redis as a storage mechanism for optuna, make sure Redis in installed and running. Please execute
$ pip install -U redis
to install redis python library.Note
Added in v1.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.4.0.
Methods
check_trial_is_updatable
(trial_id, trial_state)Check whether a trial state is updatable.
create_new_study
([study_name])Create a new study from a name.
create_new_trial
(study_id[, template_trial])Create and add a new trial to a study.
delete_study
(study_id)Delete a study.
Read a list of
FrozenStudy
objects.get_all_trials
(study_id[, deepcopy, states])Read all trials in a study.
get_best_trial
(study_id)Return the trial with the best value in a study.
Get the failed trial callback function.
Get the heartbeat interval if it is set.
get_n_trials
(study_id[, state])Count the number of trials in a study.
get_study_directions
(study_id)Read whether a study maximizes or minimizes an objective.
get_study_id_from_name
(study_name)Read the ID of a study.
get_study_name_from_id
(study_id)Read the study name of a study.
get_study_system_attrs
(study_id)Read the optuna-internal attributes of a study.
get_study_user_attrs
(study_id)Read the user-defined attributes of a study.
get_trial
(trial_id)Read a trial.
Read the trial ID of a trial.
get_trial_number_from_id
(trial_id)Read the trial number of a trial.
get_trial_param
(trial_id, param_name)Read the parameter of a trial.
get_trial_params
(trial_id)Read the parameter dictionary of a trial.
get_trial_system_attrs
(trial_id)Read the optuna-internal attributes of a trial.
get_trial_user_attrs
(trial_id)Read the user-defined attributes of a trial.
record_heartbeat
(trial_id)Record the heartbeat of the trial.
Clean up all connections to a database.
set_study_directions
(study_id, directions)Register optimization problem directions to a study.
set_study_system_attr
(study_id, key, value)Register an optuna-internal attribute to a study.
set_study_user_attr
(study_id, key, value)Register a user-defined attribute to a study.
set_trial_intermediate_value
(trial_id, step, ...)Report an intermediate value of an objective function.
set_trial_param
(trial_id, param_name, ...)Set a parameter to a trial.
set_trial_state_values
(trial_id, state[, values])Update the state and values of a trial.
set_trial_system_attr
(trial_id, key, value)Set an optuna-internal attribute to a trial.
set_trial_user_attr
(trial_id, key, value)Set a user-defined attribute to a trial.
- check_trial_is_updatable(trial_id, trial_state)
Check whether a trial state is updatable.
- Parameters
trial_id (int) – ID of the trial. Only used for an error message.
trial_state (TrialState) – Trial state to check.
- Raises
RuntimeError – If the trial is already finished.
- Return type
None
- create_new_study(study_name=None)[source]
Create a new study from a name.
If no name is specified, the storage class generates a name. The returned study ID is unique among all current and deleted studies.
- Parameters
study_name (Optional[str]) – Name of the new study to create.
- Returns
ID of the created study.
- Raises
optuna.exceptions.DuplicatedStudyError – If a study with the same
study_name
already exists.- Return type
- create_new_trial(study_id, template_trial=None)[source]
Create and add a new trial to a study.
The returned trial ID is unique among all current and deleted trials.
- Parameters
study_id (int) – ID of the study.
template_trial (Optional[FrozenTrial]) – Template
FronzenTrial
with default user-attributes, system-attributes, intermediate-values, and a state.
- Returns
ID of the created trial.
- Raises
KeyError – If no study with the matching
study_id
exists.- Return type
- get_all_studies()[source]
Read a list of
FrozenStudy
objects.- Returns
A list of
FrozenStudy
objects.- Return type
List[FrozenStudy]
- get_all_trials(study_id, deepcopy=True, states=None)[source]
Read all trials in a study.
- Parameters
- Returns
List of trials in the study.
- Raises
KeyError – If no study with the matching
study_id
exists.- Return type
- get_best_trial(study_id)[source]
Return the trial with the best value in a study.
This method is valid only during single-objective optimization.
- Parameters
study_id (int) – ID of the study.
- Returns
The trial with the best objective value among all finished trials in the study.
- Raises
KeyError – If no study with the matching
study_id
exists.RuntimeError – If the study has more than one direction.
ValueError – If no trials have been completed.
- Return type
- get_failed_trial_callback()[source]
Get the failed trial callback function.
- Returns
The failed trial callback function if it is set, otherwise
None
.- Return type
Optional[Callable[[Study, FrozenTrial], None]]
- get_n_trials(study_id, state=None)
Count the number of trials in a study.
- Parameters
study_id (int) – ID of the study.
state (Optional[Union[Tuple[TrialState, ...], TrialState]]) – Trial states to filter on. If
None
, include all states.
- Returns
Number of trials in the study.
- Raises
KeyError – If no study with the matching
study_id
exists.- Return type
- get_study_directions(study_id)[source]
Read whether a study maximizes or minimizes an objective.
- get_trial(trial_id)[source]
Read a trial.
- get_trial_id_from_study_id_trial_number(study_id, trial_number)[source]
Read the trial ID of a trial.
- get_trial_number_from_id(trial_id)
Read the trial number of a trial.
Note
The trial number is only unique within a study, and is sequential.
- get_trial_param(trial_id, param_name)
Read the parameter of a trial.
- get_trial_params(trial_id)
Read the parameter dictionary of a trial.
- get_trial_system_attrs(trial_id)
Read the optuna-internal attributes of a trial.
- get_trial_user_attrs(trial_id)
Read the user-defined attributes of a trial.
- record_heartbeat(trial_id)[source]
Record the heartbeat of the trial.
- Parameters
trial_id (int) – ID of the trial.
- Return type
None
- remove_session()
Clean up all connections to a database.
- Return type
None
- set_study_directions(study_id, directions)[source]
Register optimization problem directions to a study.
- Parameters
study_id (int) – ID of the study.
directions (Sequence[StudyDirection]) – A sequence of direction whose element is either
MAXIMIZE
orMINIMIZE
.
- Raises
KeyError – If no study with the matching
study_id
exists.ValueError – If the directions are already set and the each coordinate of passed
directions
is the opposite direction orNOT_SET
.
- Return type
None
- set_study_system_attr(study_id, key, value)[source]
Register an optuna-internal attribute to a study.
This method overwrites any existing attribute.
- set_study_user_attr(study_id, key, value)[source]
Register a user-defined attribute to a study.
This method overwrites any existing attribute.
- set_trial_intermediate_value(trial_id, step, intermediate_value)[source]
Report an intermediate value of an objective function.
This method overwrites any existing intermediate value associated with the given step.
- Parameters
- Raises
KeyError – If no trial with the matching
trial_id
exists.RuntimeError – If the trial is already finished.
- Return type
None
- set_trial_param(trial_id, param_name, param_value_internal, distribution)[source]
Set a parameter to a trial.
- Parameters
- Raises
KeyError – If no trial with the matching
trial_id
exists.RuntimeError – If the trial is already finished.
- Return type
None
- set_trial_state_values(trial_id, state, values=None)[source]
Update the state and values of a trial.
Set return values of an objective function to values argument. If values argument is not
None
, this method overwrites any existing trial values.- Parameters
trial_id (int) – ID of the trial.
state (TrialState) – New state of the trial.
values (Optional[Sequence[float]]) – Values of the objective function.
- Returns
True
if the state is successfully updated.False
if the state is kept the same. The latter happens when this method tries to update the state ofRUNNING
trial toRUNNING
.- Raises
KeyError – If no trial with the matching
trial_id
exists.RuntimeError – If the trial is already finished.
- Return type
- set_trial_system_attr(trial_id, key, value)[source]
Set an optuna-internal attribute to a trial.
This method overwrites any existing attribute.
- Parameters
- Raises
KeyError – If no trial with the matching
trial_id
exists.RuntimeError – If the trial is already finished.
- Return type
None
- set_trial_user_attr(trial_id, key, value)[source]
Set a user-defined attribute to a trial.
This method overwrites any existing attribute.
- Parameters
- Raises
KeyError – If no trial with the matching
trial_id
exists.RuntimeError – If the trial is already finished.
- Return type
None