mira.topics.Redis#

class mira.topics.Redis(url='redis://localhost:6379', heartbeat_interval=30, grace_period=None, failed_trial_callback=None)#

Connects the optuna hyperparameter optimization instance to a REDIS server backend.

Parameters

urlstr, default = “redis://localhost:6379”

The URL which connects to the REDIS database. The standard format is:

redis://<serverlocation>:<port>

If the REDIS server is running on the same machine as MIRA, then its address will be “localhost”. The provided port is the default used by REDIS.

heartbeat_intervalint > 0, default = 30

How often to check that database server is still running during tuning, in seconds.

grace_periodint > 0 or None, default = None

If connection to database is lost, how long to wait for the connection to be restored before terminating tuning.

failed_trial_callbackfunction or None, default = None

Run this function if commiting to the database fails.

Examples

..code-block :: python

>>> tuner = mira.topics.SpeedyTuner(
    ...    model = model,
    ...    min_topics = 5,
    ...    max_topics = 55,
    ...    n_jobs = 1,
    ...    save_name = 'tuning/rna/0',
    ...    storage = mira.topics.Redis(),
    ... )

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.
`fail_stale_trials`(study_id)	Fail stale trials.
`get_all_study_summaries`()	Read a list of `StudySummary` 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_failed_trial_callback`()	Get the failed trial callback function.
`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_id_from_trial_id`(trial_id)	Read the ID of a study to which a trial belongs.
`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.
`is_heartbeat_enabled`()	Check whether the storage enables the heartbeat.
`read_trials_from_remote_storage`(study_id)	Make an internal cache of trials up-to-date.
`record_heartbeat`(trial_id)	Record the heartbeat of the trial.
`remove_session`()	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`(trial_id, state)	Update the state 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.
`set_trial_values`(trial_id, values)	Set return values of an objective function.

mira.topics.gradient_tune

mira.rp.LITE_Model