tune.api

tune.api.factory

class TuneObjectFactory[source]

Bases: object

get_path_or_temp(path)[source]
Parameters

path (str) –

Return type

str

make_dataset(dag, dataset, df=None, df_name='__tune__df_', test_df=None, test_df_name='__tune__df__validation_', partition_keys=None, shuffle=True, temp_path='')[source]
Parameters

  • dag (fugue.workflow.workflow.FugueWorkflow) –

  • dataset (Any) –

  • df (Optional[Any]) –

  • df_name (str) –

  • test_df (Optional[Any]) –

  • test_df_name (str) –

  • partition_keys (Optional[List[str]]) –

  • shuffle (bool) –

  • temp_path (str) –

Return type

tune.concepts.dataset.TuneDataset

set_temp_path(path)[source]
Parameters

path (str) –

Return type

None

tune.api.optimize

optimize_by_continuous_asha(objective, dataset, plan, checkpoint_path='', always_checkpoint=False, study_early_stop=None, trial_early_stop=None, monitor=None)[source]
Parameters
Return type

tune.concepts.dataset.StudyResult

optimize_by_hyperband(objective, dataset, plans, checkpoint_path='', distributed=None, monitor=None)[source]
Parameters

  • objective (Any) –

  • dataset (tune.concepts.dataset.TuneDataset) –

  • plans (List[List[Tuple[float, int]]]) –

  • checkpoint_path (str) –

  • distributed (Optional[bool]) –

  • monitor (Optional[Any]) –

Return type

tune.concepts.dataset.StudyResult

optimize_by_sha(objective, dataset, plan, checkpoint_path='', distributed=None, monitor=None)[source]
Parameters

  • objective (Any) –

  • dataset (tune.concepts.dataset.TuneDataset) –

  • plan (List[Tuple[float, int]]) –

  • checkpoint_path (str) –

  • distributed (Optional[bool]) –

  • monitor (Optional[Any]) –

Return type

tune.concepts.dataset.StudyResult

optimize_noniterative(objective, dataset, optimizer=None, distributed=None, logger=None, monitor=None, stopper=None, stop_check_interval=None)[source]
Parameters

  • objective (Any) –

  • dataset (tune.concepts.dataset.TuneDataset) –

  • optimizer (Optional[Any]) –

  • distributed (Optional[bool]) –

  • logger (Optional[Any]) –

  • monitor (Optional[Any]) –

  • stopper (Optional[Any]) –

  • stop_check_interval (Optional[Any]) –

Return type

tune.concepts.dataset.StudyResult

tune.api.suggest

suggest_by_continuous_asha(objective, space, plan, train_df=None, temp_path='', partition_keys=None, top_n=1, monitor=None, execution_engine=None, execution_engine_conf=None)[source]
Parameters

  • objective (Any) –

  • space (tune.concepts.space.spaces.Space) –

  • plan (List[Tuple[float, int]]) –

  • train_df (Optional[Any]) –

  • temp_path (str) –

  • partition_keys (Optional[List[str]]) –

  • top_n (int) –

  • monitor (Optional[Any]) –

  • execution_engine (Optional[Any]) –

  • execution_engine_conf (Optional[Any]) –

Return type

List[tune.concepts.flow.report.TrialReport]

suggest_by_hyperband(objective, space, plans, train_df=None, temp_path='', partition_keys=None, top_n=1, monitor=None, distributed=None, execution_engine=None, execution_engine_conf=None)[source]
Parameters

  • objective (Any) –

  • space (tune.concepts.space.spaces.Space) –

  • plans (List[List[Tuple[float, int]]]) –

  • train_df (Optional[Any]) –

  • temp_path (str) –

  • partition_keys (Optional[List[str]]) –

  • top_n (int) –

  • monitor (Optional[Any]) –

  • distributed (Optional[bool]) –

  • execution_engine (Optional[Any]) –

  • execution_engine_conf (Optional[Any]) –

Return type

List[tune.concepts.flow.report.TrialReport]

suggest_by_sha(objective, space, plan, train_df=None, temp_path='', partition_keys=None, top_n=1, monitor=None, distributed=None, execution_engine=None, execution_engine_conf=None)[source]
Parameters

  • objective (Any) –

  • space (tune.concepts.space.spaces.Space) –

  • plan (List[Tuple[float, int]]) –

  • train_df (Optional[Any]) –

  • temp_path (str) –

  • partition_keys (Optional[List[str]]) –

  • top_n (int) –

  • monitor (Optional[Any]) –

  • distributed (Optional[bool]) –

  • execution_engine (Optional[Any]) –

  • execution_engine_conf (Optional[Any]) –

Return type

List[tune.concepts.flow.report.TrialReport]

suggest_for_noniterative_objective(objective, space, df=None, df_name='__tune__df_', temp_path='', partition_keys=None, top_n=1, local_optimizer=None, logger=None, monitor=None, stopper=None, stop_check_interval=None, distributed=None, shuffle_candidates=True, execution_engine=None, execution_engine_conf=None)[source]

Given non-iterative objective, space and (optional) dataframe, suggest the best parameter combinations.

Important

Please read Non-Iterative Tuning Guide

Parameters

  • objective (Any) – a simple python function or NonIterativeObjectiveFunc compatible object, please read Non-Iterative Objective Explained

  • space (tune.concepts.space.spaces.Space) – search space, please read Space Tutorial

  • df (Optional[Any]) – Pandas, Spark, Dask or any dataframe that can be converted to Fugue DataFrame, defaults to None

  • df_name (str) – dataframe name, defaults to the value of TUNE_DATASET_DF_DEFAULT_NAME

  • temp_path (str) – temp path for serialized dataframe partitions. It can be empty if you preset using TUNE_OBJECT_FACTORY.set_temp_path(). For details, read TuneDataset Tutorial, defaults to “”

  • partition_keys (Optional[List[str]]) – partition keys for df, defaults to None. For details, please read TuneDataset Tutorial

  • top_n (int) – number of best results to return, defaults to 1. If <=0 all results will be returned

  • local_optimizer (Optional[Any]) – an object that can be converted to NonIterativeObjectiveLocalOptimizer, please read Non-Iterative Optimizers, defaults to None

  • logger (Optional[Any]) – |LoggerLikeObject|, defaults to None

  • monitor (Optional[Any]) – realtime monitor, defaults to None. Read Monitoring Guide

  • stopper (Optional[Any]) – early stopper, defaults to None. Read Early Stopping Guide

  • stop_check_interval (Optional[Any]) – an object that can be converted to timedelta, defaults to None. For details, read to_timedelta()

  • distributed (Optional[bool]) – whether to use the exeuction engine to run different trials distributedly, defaults to None. If None, it’s equal to True.

  • shuffle_candidates (bool) – whether to shuffle the candidate configurations, defaults to True. This is no effect on final result.

  • execution_engine (Optional[Any]) – Fugue ExecutionEngine like object, defaults to None. If None, NativeExecutionEngine will be used, the task will be running on local machine.

  • execution_engine_conf (Optional[Any]) – Parameters like object, defaults to None

Returns

a list of best results

Return type

List[tune.concepts.flow.report.TrialReport]