Source code for ax.core.base_trial
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from abc import ABC, abstractmethod, abstractproperty
from datetime import datetime
from enum import Enum
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional
from ax.core.arm import Arm
from ax.core.base import Base
from ax.core.data import Data
from ax.core.metric import Metric
from ax.core.runner import Runner
from ax.utils.common.typeutils import not_none
if TYPE_CHECKING:
# import as module to make sphinx-autodoc-typehints happy
from ax import core # noqa F401 # pragma: no cover
[docs]class TrialStatus(Enum):
"""Enum of trial status.
General lifecycle of a trial is:::
CANDIDATE --> STAGED --> RUNNING --> COMPLETED
-------------> --> FAILED (machine failure)
-------------------------> ABANDONED (human-initiated action)
--> DISPATCHED ---------->
Trials may be abandoned at any time prior to completion or failure
via human intervention. The difference between abandonment and failure
is that the former is human-directed, while the latter is an internal
failure state.
Additionally, when trials are deployed, they may be in an intermediate
staged state (e.g. scheduled but waiting for resources) or immediately
transition to running.
When used though the service API, Ax proposes trials and expects the client
application to compelete them with evaluation data when available. In this
case, a trial is set to 'dispatched' right after the trial is created, and
when user completes the trial with data, its status is set to 'completed'.
"""
CANDIDATE = 0
STAGED = 1
FAILED = 2
COMPLETED = 3
RUNNING = 4
ABANDONED = 5
DISPATCHED = 6
@property
def is_terminal(self) -> bool:
"""True if trial is completed."""
return (
self == TrialStatus.ABANDONED
or self == TrialStatus.COMPLETED
or self == TrialStatus.FAILED
)
@property
def expecting_data(self) -> bool:
"""True if trial is expecting data."""
return self == TrialStatus.RUNNING or self == TrialStatus.COMPLETED
@property
def is_deployed(self) -> bool:
"""True if trial has been deployed but not completed."""
return (
self == TrialStatus.STAGED
or self == TrialStatus.RUNNING
or self == TrialStatus.DISPATCHED
)
@property
def is_failed(self) -> bool:
"""True if this trial is a failed one."""
return self == TrialStatus.FAILED
[docs]def immutable_once_run(func: Callable) -> Callable:
"""Decorator for methods that should throw Error when
trial is running or has ever run and immutable.
"""
# no type annotation for now; breaks sphinx-autodoc-typehints
def _immutable_once_run(self, *args, **kwargs):
if self._status != TrialStatus.CANDIDATE:
raise ValueError(
"Cannot modify a trial that is running or has ever run.",
"Create a new trial using `experiment.new_trial()` "
"or clone an existing trial using `trial.clone()`.",
)
return func(self, *args, **kwargs)
return _immutable_once_run
[docs]class BaseTrial(ABC, Base):
"""Base class for representing trials.
Trials are containers for arms that are deployed together. There are
two types of trials: regular Trial, which only contains a single arm,
and BatchTrial, which contains an arbitrary number of arms.
"""
def __init__(
self, experiment: "core.experiment.Experiment", trial_type: Optional[str] = None
) -> None:
"""Initialize trial.
Args:
experiment: The experiment this trial belongs to.
"""
self._experiment = experiment
self._index = self._experiment._attach_trial(self)
if trial_type is not None:
if not self._experiment.supports_trial_type(trial_type):
raise ValueError(
f"Experiment does not support trial_type {trial_type}."
)
else:
trial_type = self._experiment.default_trial_type
self._trial_type: Optional[str] = trial_type
self._status: TrialStatus = TrialStatus.CANDIDATE
self._time_created: datetime = datetime.now()
# Initialize fields to be used later in lifecycle
self._time_completed: Optional[datetime] = None
self._time_staged: Optional[datetime] = None
self._time_run_started: Optional[datetime] = None
self._abandoned_reason: Optional[str] = None
self._run_metadata: Dict[str, Any] = {}
self._runner: Optional[Runner] = None
# Counter to maintain how many arms have been named by this BatchTrial
self._num_arms_created = 0
@property
def experiment(self) -> "core.experiment.Experiment":
"""The experiment this trial belongs to."""
return self._experiment
@property
def index(self) -> int:
"""The index of this trial within the experiment's trial list."""
return self._index
@property
def status(self) -> TrialStatus:
"""The status of the trial in the experimentation lifecycle."""
return self._status
@status.setter
def status(self, status: TrialStatus) -> None:
raise NotImplementedError("Use `trial.mark_*` methods to set trial status.")
@property
def runner(self) -> Optional[Runner]:
"""The runner object defining how to deploy the trial."""
return self._runner
@runner.setter
@immutable_once_run
def runner(self, runner: Optional[Runner]) -> None:
if self.experiment.is_simple_experiment:
raise NotImplementedError(
"SimpleExperiment does not support addition of runners."
)
self._runner = runner
@property
def deployed_name(self) -> Optional[str]:
"""Name of the experiment created in external framework.
This property is derived from the name field in run_metadata.
"""
return self._run_metadata.get("name") if self._run_metadata else None
@property
def run_metadata(self) -> Dict[str, Any]:
"""Dict containing metadata from the deployment process.
This is set implicitly during `trial.run()`.
"""
return self._run_metadata
@property
def trial_type(self) -> Optional[str]:
"""The type of the trial.
Relevant for experiments containing different kinds of trials
(e.g. different deployment types).
"""
return self._trial_type
@trial_type.setter
@immutable_once_run
def trial_type(self, trial_type: Optional[str]) -> None:
"""Identifier used to distinguish trial types in experiments
with multiple trial types.
"""
if self._experiment is not None:
if not self._experiment.supports_trial_type(trial_type):
raise ValueError(f"{trial_type} is not supported by the experiment.")
self._trial_type = trial_type
[docs] def assign_runner(self) -> "BaseTrial":
"""Assigns default experiment runner if trial doesn't already have one."""
self._runner = self._runner or self.experiment.runner_for_trial(self)
return self
[docs] def run(self) -> "BaseTrial":
"""Deploys the trial according to the behavior on the runner.
The runner returns a `run_metadata` dict containining metadata
of the deployment process. It also returns a `deployed_name` of the trial
within the system to which it was deployed. Both these fields are set on
the trial.
Returns:
The trial instance.
"""
if self.status != TrialStatus.CANDIDATE:
raise ValueError("Can only run a candidate trial.")
# Default to experiment runner if trial doesn't have one
self.assign_runner()
if self._runner is None:
raise ValueError("No runner set on trial or experiment.")
# pyre-fixme[16]: `Optional` has no attribute `run`.
self._run_metadata = self._runner.run(self)
# pyre-fixme[16]: `Optional` has no attribute `staging_required`.
if self._runner.staging_required:
self.mark_staged()
else:
self.mark_running()
return self
[docs] def complete(self) -> "BaseTrial":
"""Stops the trial if functionality is defined on runner
and marks trial completed.
Returns:
The trial instance.
"""
if self.status != TrialStatus.RUNNING:
raise ValueError("Can only stop a running trial.")
not_none(self._runner).stop(self)
self.mark_completed()
return self
[docs] def fetch_data(self, metrics: Optional[List[Metric]] = None, **kwargs: Any) -> Data:
"""Fetch data for this trial for all metrics on experiment.
Args:
trial_index: The index of the trial to fetch data for.
metrics: If provided, fetch data for these metrics instead of the ones
defined on the experiment.
kwargs: keyword args to pass to underlying metrics' fetch data functions.
Returns:
Data for this trial.
"""
return self.experiment._fetch_trial_data(
trial_index=self.index, metrics=metrics, **kwargs
)
def _check_existing_and_name_arm(self, arm: Arm) -> None:
"""Sets name for given arm; if this arm is already in the
experiment, uses the existing arm name.
"""
proposed_name = f"{self.index}_{self._num_arms_created}"
self.experiment._name_and_store_arm_if_not_exists(
arm=arm, proposed_name=proposed_name
)
# If arm was named using given name, incremement the count
if arm.name == proposed_name:
self._num_arms_created += 1
@abstractproperty
def arms(self) -> List[Arm]:
pass # pragma: no cover
@abstractproperty
def arms_by_name(self) -> Dict[str, Arm]:
pass # pragma: no cover
@abstractmethod
def __repr__(self) -> str:
pass # pragma: no cover
@abstractproperty
def abandoned_arms(self) -> List[Arm]:
"""All abandoned arms, associated with this trial."""
pass # pragma: no cover
# --- Batch lifecycle management functions ---
@property
def time_created(self) -> datetime:
"""Creation time of the trial."""
return self._time_created
@property
def time_completed(self) -> Optional[datetime]:
"""Completion time of the trial."""
return self._time_completed
@property
def time_staged(self) -> Optional[datetime]:
"""Staged time of the trial."""
return self._time_staged
@property
def time_run_started(self) -> Optional[datetime]:
"""Time the trial was started running (i.e. collecting data)."""
return self._time_run_started
@property
def is_abandoned(self) -> bool:
"""Whether this trial is abandoned."""
return self._status == TrialStatus.ABANDONED
@property
def abandoned_reason(self) -> Optional[str]:
return self._abandoned_reason
[docs] def mark_staged(self) -> "BaseTrial":
"""Mark the trial as being staged for running.
Returns:
The trial instance.
"""
if self._status != TrialStatus.CANDIDATE:
raise ValueError("Can only stage a candidate trial.")
self._status = TrialStatus.STAGED
self._time_staged = datetime.now()
return self
[docs] def mark_running(self) -> "BaseTrial":
"""Mark trial has started running.
Returns:
The trial instance.
"""
if self.experiment.is_simple_experiment:
self._status = TrialStatus.RUNNING
self._time_run_started = datetime.now()
return self
if self._runner is None:
raise ValueError("Cannot mark trial running without setting runner.")
prev_step = (
TrialStatus.STAGED
# pyre-fixme[16]: `Optional` has no attribute `staging_required`.
if self._runner.staging_required
else TrialStatus.CANDIDATE
)
prev_step_str = "staged." if prev_step == TrialStatus.STAGED else "candidate."
if self._status != prev_step:
raise ValueError(
f"Can only mark this trial as running when {prev_step_str}"
)
self._status = TrialStatus.RUNNING
self._time_run_started = datetime.now()
return self
[docs] def mark_completed(self) -> "BaseTrial":
"""Mark trial as completed.
Returns:
The trial instance.
"""
if (
self._status != TrialStatus.RUNNING
and self._status != TrialStatus.DISPATCHED
):
raise ValueError(
"Can only complete trial that is currently running or dispatched."
)
self._status = TrialStatus.COMPLETED
self._time_completed = datetime.now()
return self
[docs] def mark_abandoned(self, reason: Optional[str] = None) -> "BaseTrial":
"""Mark trial as abandoned.
Args:
abandoned_reason: The reason the trial was abandoned.
Returns:
The trial instance.
"""
if self._status.is_terminal:
raise ValueError("Cannot abandon a trial in a terminal state.")
self._abandoned_reason = reason
self._status = TrialStatus.ABANDONED
self._time_completed = datetime.now()
return self
[docs] def mark_failed(self) -> "BaseTrial":
"""Mark trial as failed.
Returns:
The trial instance.
"""
if (
self._status != TrialStatus.RUNNING
and self._status != TrialStatus.DISPATCHED
):
raise ValueError(
"Can only mark failed a trial that is currently running or dispatched."
)
self._status = TrialStatus.FAILED
self._time_completed = datetime.now()
return self
[docs] def mark_dispatched(self) -> "BaseTrial":
"""Mark trial as dispatched through the service API to await completion.
Returns:
The trial instance.
"""
if self._status != TrialStatus.CANDIDATE:
raise ValueError("Can only mark a candidate trial as dispatched.")
self._status = TrialStatus.DISPATCHED
return self
