tune.concepts.flow

tune.concepts.flow.judge

class Monitor[source]

Bases: object

finalize()[source]

Return type: None

initialize()[source]

Return type: None

on_get_budget(trial, rung, budget)[source]

Parameters

trial (tune.concepts.flow.trial.Trial) –
rung (int) –
budget (float) –

Return type

None

on_judge(decision)[source]

Parameters: decision (tune.concepts.flow.judge.TrialDecision) –
Return type: None

on_report(report)[source]

Parameters: report (tune.concepts.flow.report.TrialReport) –
Return type: None

class NoOpTrailJudge(monitor=None)[source]

Bases: tune.concepts.flow.judge.TrialJudge

Parameters: monitor (Optional[Monitor]) –

can_accept(trial)[source]

Parameters: trial (tune.concepts.flow.trial.Trial) –
Return type: bool

get_budget(trial, rung)[source]

Parameters

trial (tune.concepts.flow.trial.Trial) –
rung (int) –

Return type

float

judge(report)[source]

Parameters: report (tune.concepts.flow.report.TrialReport) –
Return type: tune.concepts.flow.judge.TrialDecision

class RemoteTrialJudge(entrypoint)[source]

Bases: tune.concepts.flow.judge.TrialJudge

Parameters: entrypoint (Callable[[str, Dict[str, Any]], Any]) –

can_accept(trial)[source]

Parameters: trial (tune.concepts.flow.trial.Trial) –
Return type: bool

get_budget(trial, rung)[source]

Parameters

trial (tune.concepts.flow.trial.Trial) –
rung (int) –

Return type

float

judge(report)[source]

Parameters: report (tune.concepts.flow.report.TrialReport) –
Return type: tune.concepts.flow.judge.TrialDecision

property report: Optional[tune.concepts.flow.report.TrialReport]

class TrialCallback(judge)[source]

Bases: object

Parameters: judge (tune.concepts.flow.judge.TrialJudge) –

entrypoint(name, kwargs)[source]

Parameters: kwargs (Dict[str, Any]) –
Return type: Any

class TrialDecision(report, budget, should_checkpoint, reason='', metadata=None)[source]

Bases: object

Parameters

report (tune.concepts.flow.report.TrialReport) –
budget (float) –
should_checkpoint (bool) –
reason (str) –
metadata (Optional[Dict[str, Any]]) –

property budget: float

property metadata: Dict[str, Any]

property reason: str

property report: tune.concepts.flow.report.TrialReport

property should_checkpoint: bool

property should_stop: bool

property trial: tune.concepts.flow.trial.Trial

property trial_id: str

class TrialJudge(monitor=None)[source]

Bases: object

Parameters: monitor (Optional[Monitor]) –

can_accept(trial)[source]

Parameters: trial (tune.concepts.flow.trial.Trial) –
Return type: bool

get_budget(trial, rung)[source]

Parameters

trial (tune.concepts.flow.trial.Trial) –
rung (int) –

Return type

float

judge(report)[source]

Parameters: report (tune.concepts.flow.report.TrialReport) –
Return type: tune.concepts.flow.judge.TrialDecision

property monitor: tune.concepts.flow.judge.Monitor

reset_monitor(monitor=None)[source]

Parameters: monitor (Optional[tune.concepts.flow.judge.Monitor]) –
Return type: None

tune.concepts.flow.report

class TrialReport(trial, metric, params=None, metadata=None, cost=1.0, rung=0, sort_metric=None, log_time=None)[source]

Bases: object

The result from running the objective. It is immutable.

Parameters

trial (tune.concepts.flow.trial.Trial) – the original trial sent to the objective
metric (Any) – the raw metric from the objective output
params (Any) – updated parameters based on the trial input, defaults to None. If none, it means the params from the trial was not updated, otherwise it is an object convertible to TuningParametersTemplate by to_template()
metadata (Optional[Dict[str, Any]]) – metadata from the objective output, defaults to None
cost (float) – cost to run the objective, defaults to 1.0
rung (int) – number of rungs in the current objective, defaults to 0. This is for iterative problems
sort_metric (Any) – the metric for comparison, defaults to None. It must be smaller better. If not set, it implies the metric is sort_metric and it is smaller better
log_time (Any) – the time generating this report, defaults to None. If None, current time will be used

Attention

This class is not for users to construct directly.

copy()[source]

Copy the current object.

Returns: the copied object
Return type: tune.concepts.flow.report.TrialReport

Note

This is shallow copy, but it is also used by __deepcopy__ of this object. This is because we disable deepcopy of TrialReport.

property cost: float: The cost to run the objective

fill_dict(data)[source]

Fill a row of StudyResult with the report information

Parameters: data (Dict[str, Any]) – a row (as dict) from StudyResult
Returns: the updated data
Return type: Dict[str, Any]

generate_sort_metric(min_better, digits)[source]

Construct a new report object with the new derived``sort_metric``

Parameters

min_better (bool) – whether the current metric() is smaller better
digits (int) – number of digits to keep in sort_metric

Returns

a new object with the updated value

Return type

tune.concepts.flow.report.TrialReport

property log_time: datetime.datetime: The time generating this report

property metadata: Dict[str, Any]: The metadata from the objective output

property metric: float: The raw metric from the objective output

property params: tune.concepts.space.parameters.TuningParametersTemplate: The parameters used by the objective to generate the metric()

reset_log_time()[source]

Reset log_time() to now

Return type: tune.concepts.flow.report.TrialReport

property rung: int: The number of rungs in the current objective, defaults to 0. This is for iterative problems

property sort_metric: float: The metric for comparison

property trial: tune.concepts.flow.trial.Trial: The original trial sent to the objective

property trial_id: str: tune.concepts.flow.trial.Trial.trial_id()

with_cost(cost)[source]

Construct a new report object with the new cost

Parameters: cost (float) – new cost
Returns: a new object with the updated value
Return type: tune.concepts.flow.report.TrialReport

with_rung(rung)[source]

Construct a new report object with the new rung

Parameters: rung (int) – new rung
Returns: a new object with the updated value
Return type: tune.concepts.flow.report.TrialReport

with_sort_metric(sort_metric)[source]

Construct a new report object with the new sort_metric

Parameters: sort_metric (Any) – new sort_metric
Returns: a new object with the updated value
Return type: tune.concepts.flow.report.TrialReport

class TrialReportHeap(min_heap)[source]

Bases: object

Parameters: min_heap (bool) –

pop()[source]

Return type: tune.concepts.flow.report.TrialReport

push(report)[source]

Parameters: report (tune.concepts.flow.report.TrialReport) –
Return type: None

values()[source]

Return type: Iterable[tune.concepts.flow.report.TrialReport]

class TrialReportLogger(new_best_only=False)[source]

Bases: object

Parameters: new_best_only (bool) –

property best: Optional[tune.concepts.flow.report.TrialReport]

log(report)[source]

Parameters: report (tune.concepts.flow.report.TrialReport) –
Return type: None

on_report(report)[source]

Parameters: report (tune.concepts.flow.report.TrialReport) –
Return type: bool

tune.concepts.flow.trial

class Trial(trial_id, params, metadata=None, keys=None, dfs=None)[source]

Bases: object

The input data collection for running an objective. It is immutable.

Parameters

trial_id (str) – the unique id for a trial
params (Any) – parameters for tuning, an object convertible to TuningParametersTemplate by to_template()
metadata (Optional[Dict[str, Any]]) – metadata for tuning, defaults to None. It is set during the construction of TuneDataset
keys (Optional[List[str]]) – partitions keys of the TuneDataset, defaults to None
dfs (Optional[Dict[str, Any]]) – dataframes extracted from TuneDataset, defaults to None

Attention

This class is not for users to construct directly. Use Space instead.

copy()[source]

Copy the current object.

Returns: the copied object
Return type: tune.concepts.flow.trial.Trial

Note

This is shallow copy, but it is also used by __deepcopy__ of this object. This is because we disable deepcopy of Trial.

property dfs: Dict[str, Any]: Dataframes extracted from TuneDataset

property keys: List[str]: Partitions keys of the TuneDataset

property metadata: Dict[str, Any]: Metadata of the trial

property params: tune.concepts.space.parameters.TuningParametersTemplate: Parameters for tuning

property trial_id: str: The unique id of this trial

with_dfs(dfs)[source]

Set dataframes for the trial, a new Trial object will be constructed and with the new dfs

Parameters: dfs (Dict[str, Any]) – dataframes to attach to the trial
Return type: tune.concepts.flow.trial.Trial

with_params(params)[source]

Set parameters for the trial, a new Trial object will be constructed and with the new params

Parameters: params (Any) – parameters for tuning
Return type: tune.concepts.flow.trial.Trial