optuna.storages.RDBStorage

class optuna.storages.RDBStorage(url, engine_kwargs=None, skip_compatibility_check=False, *, heartbeat_interval=None, grace_period=None, failed_trial_callback=None, skip_table_creation=False)[source]

Storage class for RDB 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

Create an RDBStorage instance with customized pool_size and timeout settings.

import optuna


def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    return x**2


storage = optuna.storages.RDBStorage(
    url="sqlite:///:memory:",
    engine_kwargs={"pool_size": 20, "connect_args": {"timeout": 10}},
)

study = optuna.create_study(storage=storage)
study.optimize(objective, n_trials=10)

Parameters

url (str) – URL of the storage.
engine_kwargs (Optional[Dict[str, Any]]) – A dictionary of keyword arguments that is passed to sqlalchemy.engine.create_engine function.
skip_compatibility_check (bool) – Flag to skip schema compatibility check if set to True.
heartbeat_interval (Optional[int]) –
Interval to record the heartbeat. It is recorded every interval seconds. heartbeat_interval must be None or a positive integer.

Note

The heartbeat is supposed to be used with optimize(). If you use ask() and tell() instead, it will not work.
grace_period (Optional[int]) – Grace period before a running trial is failed from the last heartbeat. grace_period must be None or a positive integer. If it is None, 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 and FrozenTrial.

Note

The procedure to fail existing stale trials is called just before asking the study for a new trial.
skip_table_creation (bool) – Flag to skip table creation if set to True.

Note

If you use MySQL, pool_pre_ping will be set to True by default to prevent connection timeout. You can turn it off with engine_kwargs['pool_pre_ping']=False, but it is recommended to keep the setting if execution time of your objective function is longer than the wait_timeout of your MySQL configuration.

Note

Mainly in a cluster environment, running trials are often killed unexpectedly. If you want to detect a failure of trials, please use the heartbeat mechanism. Set heartbeat_interval, grace_period, and failed_trial_callback appropriately according to your use case. For more details, please refer to the tutorial and Example page.

See also

You can use RetryFailedTrialCallback to automatically retry failed trials detected by heartbeat.

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.
`get_all_studies`()	Read a list of `FrozenStudy` objects.
`get_all_trials`(study_id[, deepcopy, states])	Read all trials in a study.
`get_all_versions`()	Return the schema version list.
`get_best_trial`(study_id)	Return the trial with the best value in a study.
`get_current_version`()	Return the schema version currently used by this storage.
`get_failed_trial_callback`()	Get the failed trial callback function.
`get_head_version`()	Return the latest schema version.
`get_heartbeat_interval`()	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.
`get_trial_id_from_study_id_trial_number`(...)	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.
`remove_session`()	Removes the current session.
`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.
`upgrade`()	Upgrade the storage schema.

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: int

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

int

delete_study(study_id)[source]

Delete a study.

Parameters: study_id (int) – ID of the study.
Raises: KeyError – If no study with the matching study_id exists.
Return type: None

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

study_id (int) – ID of the study.
deepcopy (bool) – Whether to copy the list of trials before returning. Set to True if you intend to update the list or elements of the list.
states (Optional[Container[TrialState]]) – Trial states to filter on. If None, include all states.

Returns

List of trials in the study.

Raises

KeyError – If no study with the matching study_id exists.

Return type

List[FrozenTrial]

get_all_versions()[source]

Return the schema version list.

Return type: List[str]

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

FrozenTrial

get_current_version()[source]

Return the schema version currently used by this storage.

Return type: str

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_head_version()[source]

Return the latest schema version.

Return type: str

get_heartbeat_interval()[source]

Get the heartbeat interval if it is set.

Returns: The heartbeat interval if it is set, otherwise None.
Return type: Optional[int]

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

int

get_study_directions(study_id)[source]

Read whether a study maximizes or minimizes an objective.

Parameters: study_id (int) – ID of a study.
Returns: Optimization directions list of the study.
Raises: KeyError – If no study with the matching study_id exists.
Return type: List[StudyDirection]

get_study_id_from_name(study_name)[source]

Read the ID of a study.

Parameters: study_name (str) – Name of the study.
Returns: ID of the study.
Raises: KeyError – If no study with the matching study_name exists.
Return type: int

get_study_name_from_id(study_id)[source]

Read the study name of a study.

Parameters: study_id (int) – ID of the study.
Returns: Name of the study.
Raises: KeyError – If no study with the matching study_id exists.
Return type: str

get_study_system_attrs(study_id)[source]

Read the optuna-internal attributes of a study.

Parameters: study_id (int) – ID of the study.
Returns: Dictionary with the optuna-internal attributes of the study.
Raises: KeyError – If no study with the matching study_id exists.
Return type: Dict[str, Any]

get_study_user_attrs(study_id)[source]

Read the user-defined attributes of a study.

Parameters: study_id (int) – ID of the study.
Returns: Dictionary with the user attributes of the study.
Raises: KeyError – If no study with the matching study_id exists.
Return type: Dict[str, Any]

get_trial(trial_id)[source]

Read a trial.

Parameters: trial_id (int) – ID of the trial.
Returns: Trial with a matching trial ID.
Raises: KeyError – If no trial with the matching trial_id exists.
Return type: FrozenTrial

get_trial_id_from_study_id_trial_number(study_id, trial_number)[source]

Read the trial ID of a trial.

Parameters

study_id (int) – ID of the study.
trial_number (int) – Number of the trial.

Returns

ID of the trial.

Raises

KeyError – If no trial with the matching study_id and trial_number exists.

Return type

int

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.

Parameters: trial_id (int) – ID of the trial.
Returns: Number of the trial.
Raises: KeyError – If no trial with the matching trial_id exists.
Return type: int

get_trial_param(trial_id, param_name)[source]

Read the parameter of a trial.

Parameters

trial_id (int) – ID of the trial.
param_name (str) – Name of the parameter.

Returns

Internal representation of the parameter.

Raises

KeyError – If no trial with the matching trial_id exists. If no such parameter exists.

Return type

float

get_trial_params(trial_id)

Read the parameter dictionary of a trial.

Parameters: trial_id (int) – ID of the trial.
Returns: Dictionary of a parameters. Keys are parameter names and values are internal representations of the parameter values.
Raises: KeyError – If no trial with the matching trial_id exists.
Return type: Dict[str, Any]

get_trial_system_attrs(trial_id)[source]

Read the optuna-internal attributes of a trial.

Parameters: trial_id (int) – ID of the trial.
Returns: Dictionary with the optuna-internal attributes of the trial.
Raises: KeyError – If no trial with the matching trial_id exists.
Return type: Dict[str, Any]

get_trial_user_attrs(trial_id)[source]

Read the user-defined attributes of a trial.

Parameters: trial_id (int) – ID of the trial.
Returns: Dictionary with the user-defined attributes of the trial.
Raises: KeyError – If no trial with the matching trial_id exists.
Return type: Dict[str, Any]

record_heartbeat(trial_id)[source]

Record the heartbeat of the trial.

Parameters: trial_id (int) – ID of the trial.
Return type: None

remove_session()[source]

Removes the current session.

A session is stored in SQLAlchemy’s ThreadLocalRegistry for each thread. This method closes and removes the session which is associated to the current thread. Particularly, under multi-thread use cases, it is important to call this method from each thread. Otherwise, all sessions and their associated DB connections are destructed by a thread that occasionally invoked the garbage collector. By default, it is not allowed to touch a SQLite connection from threads other than the thread that created the connection. Therefore, we need to explicitly close the connection from each thread.

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 or MINIMIZE.

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 or NOT_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.

Parameters

study_id (int) – ID of the study.
key (str) – Attribute key.
value (Any) – Attribute value. It should be JSON serializable.

Raises

KeyError – If no study with the matching study_id exists.

Return type

None

set_study_user_attr(study_id, key, value)[source]

Register a user-defined attribute to a study.

This method overwrites any existing attribute.

Parameters

study_id (int) – ID of the study.
key (str) – Attribute key.
value (Any) – Attribute value. It should be JSON serializable.

Raises

KeyError – If no study with the matching study_id exists.

Return type

None

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

trial_id (int) – ID of the trial.
step (int) – Step of the trial (e.g., the epoch when training a neural network).
intermediate_value (float) – Intermediate value corresponding to the step.

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

trial_id (int) – ID of the trial.
param_name (str) – Name of the parameter.
param_value_internal (float) – Internal representation of the parameter value.
distribution (BaseDistribution) – Sampled distribution of the parameter.

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 of RUNNING trial to RUNNING.

Raises

KeyError – If no trial with the matching trial_id exists.
RuntimeError – If the trial is already finished.

Return type

bool

set_trial_system_attr(trial_id, key, value)[source]

Set an optuna-internal attribute to a trial.

This method overwrites any existing attribute.

Parameters

trial_id (int) – ID of the trial.
key (str) – Attribute key.
value (Any) – Attribute value. It should be JSON serializable.

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

trial_id (int) – ID of the trial.
key (str) – Attribute key.
value (Any) – Attribute value. It should be JSON serializable.

Raises

KeyError – If no trial with the matching trial_id exists.
RuntimeError – If the trial is already finished.

Return type

None

upgrade()[source]

Upgrade the storage schema.

Return type: None