Sweep Class

Reference

Base class for sweep node.

This class should not be instantiated directly. Instead, it should be created via the builder function: sweep.

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Inheritance: azure.ai.ml.entities._job.sweep.parameterized_sweep.ParameterizedSweep

Sweep

azure.ai.ml.entities._builders.base_node.BaseNode

Sweep

Constructor

Sweep(*, trial: CommandComponent | str | None = None, compute: str | None = None, limits: SweepJobLimits | None = None, sampling_algorithm: str | SamplingAlgorithm | None = None, objective: Objective | None = None, early_termination: BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy | EarlyTerminationPolicy | str | None = None, search_space: Dict[str, Choice | LogNormal | LogUniform | Normal | QLogNormal | QLogUniform | QNormal | QUniform | Randint | Uniform] | None = None, inputs: Dict[str, Input | str | bool | int | float] | None = None, outputs: Dict[str, str | Output] | None = None, identity: Dict | ManagedIdentityConfiguration | AmlTokenConfiguration | UserIdentityConfiguration | None = None, queue_settings: QueueSettings | None = None, resources: dict | JobResourceConfiguration | None = None, **kwargs: Any)

Parameters

Name	Description
trial Required	Union[CommandComponent, str] The ID or instance of the command component or job to be run for the step.
compute Required	str The compute definition containing the compute information for the step.
limits Required	SweepJobLimits The limits for the sweep node.
sampling_algorithm Required	str The sampling algorithm to use to sample inside the search space. Accepted values are: "random", "grid", or "bayesian".
objective Required	Objective The objective used to determine the target run with the local optimal hyperparameter in search space.
early_termination_policy Required	The early termination policy of the sweep node.
search_space Required	The hyperparameter search space to run trials in.
inputs Required	Mapping of input data bindings used in the job.
outputs Required	Dict[str, Union[str, Output]] Mapping of output data bindings used in the job.
identity Required	The identity that the training job will use while running on compute.
queue_settings Required	QueueSettings The queue settings for the job.
resources Required	Optional[Union[dict, ResourceConfiguration]] Compute Resource configuration for the job.

Keyword-Only Parameters

Name	Description
trial Required
compute Required
limits Required
sampling_algorithm Required
objective Required
early_termination Required
search_space Required
inputs Required
outputs Required
identity Required
queue_settings Required
resources Required

Methods

clear
copy
dump	Dumps the job content into a file in YAML format.
fromkeys	Create a new dictionary with keys from iterable and values set to value.
get	Return the value for key if key is in the dictionary, else default.
items
keys
pop	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem	Remove and return a (key, value) pair as a 2-tuple. Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.
set_limits	Set limits for Sweep node. Leave parameters as None if you don't want to update corresponding values.
set_objective	Set the sweep object.. Leave parameters as None if you don't want to update corresponding values.
set_resources	Set resources for Sweep.
setdefault	Insert key with a value of default if key is not in the dictionary. Return the value for key if key is in the dictionary, else default.
update	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values

clear

clear() -> None.  Remove all items from D.

copy

copy() -> a shallow copy of D

dump

Dumps the job content into a file in YAML format.

dump(dest: str | PathLike | IO, **kwargs: Any) -> None

Parameters

Name	Description
dest Required	Union[<xref:PathLike>, str, IO[AnyStr]] The local path or file stream to write the YAML content to. If dest is a file path, a new file will be created. If dest is an open file, the file will be written to directly.

Exceptions

Type	Description
FileExistsError	Raised if dest is a file path and the file already exists.
IOError	Raised if dest is an open file and the file is not writable.

fromkeys

Create a new dictionary with keys from iterable and values set to value.

fromkeys(value=None, /)

Positional-Only Parameters

Name	Description
iterable Required
value	Default value: None

Parameters

Name	Description
type Required

get

Return the value for key if key is in the dictionary, else default.

get(key, default=None, /)

Positional-Only Parameters

Name	Description
key Required
default	Default value: None

items

items() -> a set-like object providing a view on D's items

keys

keys() -> a set-like object providing a view on D's keys

pop

If the key is not found, return the default if given; otherwise, raise a KeyError.

pop(k, [d]) -> v, remove specified key and return the corresponding value.

popitem

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

popitem()

set_limits

Set limits for Sweep node. Leave parameters as None if you don't want to update corresponding values.

set_limits(*, max_concurrent_trials: int | None = None, max_total_trials: int | None = None, timeout: int | None = None, trial_timeout: int | None = None) -> None

Keyword-Only Parameters

Name	Description
max_concurrent_trials	int maximum concurrent trial number.
max_total_trials	int maximum total trial number.
timeout	int total timeout in seconds for sweep node
trial_timeout	int timeout in seconds for each trial

set_objective

Set the sweep object.. Leave parameters as None if you don't want to update corresponding values.

set_objective(*, goal: str | None = None, primary_metric: str | None = None) -> None

Keyword-Only Parameters

Name	Description
goal	str Defines supported metric goals for hyperparameter tuning. Acceptable values are: "minimize" and "maximize".
primary_metric	str Name of the metric to optimize.

set_resources

Set resources for Sweep.

set_resources(*, instance_type: str | List[str] | None = None, instance_count: int | None = None, locations: List[str] | None = None, properties: Dict | None = None, docker_args: str | None = None, shm_size: str | None = None) -> None

Keyword-Only Parameters

Name	Description
instance_type	Optional[Union[str, List[str]]] The instance type to use for the job.
instance_count	Optional[int] The number of instances to use for the job.
locations	Optional[List[str]] The locations to use for the job.
properties	Optional[Dict] The properties for the job.
docker_args	Optional[str] The docker arguments for the job.
shm_size	Optional[str] The shared memory size for the job.

setdefault

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

setdefault(key, default=None, /)

Positional-Only Parameters

Name	Description
key Required
default	Default value: None

update

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

update([E], **F) -> None.  Update D from dict/iterable E and F.

values

values() -> an object providing a view on D's values

Attributes

base_path

The base path of the resource.

Returns

Type	Description
str	The base path of the resource.

creation_context

The creation context of the resource.

Returns

Type	Description
Optional[SystemData]	The creation metadata for the resource.

early_termination

The early termination policy for the sweep job.

Returns

Type	Description
Union[str, BanditPolicy, MedianStoppingPolicy, TruncationSelectionPolicy]

id

The resource ID.

Returns

Type	Description
Optional[str]	The global ID of the resource, an Azure Resource Manager (ARM) ID.

inputs

Get the inputs for the object.

Returns

Type	Description
Dict[str, Union[Input, str, bool, int, float]]	A dictionary containing the inputs for the object.

limits

Limits for sweep job.

Returns

Type	Description
SweepJobLimits	Limits for sweep job.

log_files

Job output files.

Returns

Type	Description
Optional[Dict[str, str]]	The dictionary of log names and URLs.

name

Get the name of the node.

Returns

Type	Description
str	The name of the node.

outputs

Get the outputs of the object.

Returns

Type	Description
Dict[str, Union[str, Output]]	A dictionary containing the outputs for the object.

resources

Resources for sweep job.

Returns

Type	Description
ResourceConfiguration	Resources for sweep job.

sampling_algorithm

Sampling algorithm for sweep job.

Returns

Type	Description
SamplingAlgorithm	Sampling algorithm for sweep job.

search_space

Dictionary of the hyperparameter search space.

Each key is the name of a hyperparameter and its value is the parameter expression.

Returns

Type	Description
Dict[str, Union[Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform]]

status

The status of the job.

Common values returned include "Running", "Completed", and "Failed". All possible values are:

NotStarted - This is a temporary state that client-side Run objects are in before cloud submission.
Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.
Provisioning - On-demand compute is being created for a given job submission.
Preparing - The run environment is being prepared and is in one of two stages:
- Docker image build
- conda environment setup
Queued - The job is queued on the compute target. For example, in BatchAI, the job is in a queued state

while waiting for all the requested nodes to be ready.
Running - The job has started to run on the compute target.
Finalizing - User code execution has completed, and the run is in post-processing stages.
CancelRequested - Cancellation has been requested for the job.
Completed - The run has completed successfully. This includes both the user code execution and run

post-processing stages.
Failed - The run failed. Usually the Error property on a run will provide details as to why.
Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.
NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.

Returns

Type	Description
Optional[str]	Status of the job.

studio_url

Azure ML studio endpoint.

Returns

Type	Description
Optional[str]	The URL to the job details page.

trial

The ID or instance of the command component or job to be run for the step.

Returns

Type	Description
CommandComponent

type

The type of the job.

Returns

Type	Description
Optional[str]	The type of the job.

Share via

Sweep Class

Constructor

Parameters

Keyword-Only Parameters

Methods

clear

copy

dump

Parameters

Exceptions

fromkeys

Positional-Only Parameters

Parameters

get

Positional-Only Parameters

items

keys

pop

popitem

set_limits

Keyword-Only Parameters

set_objective

Keyword-Only Parameters

set_resources

Keyword-Only Parameters

setdefault

Positional-Only Parameters

update

values

Attributes

base_path

Returns

creation_context

Returns

early_termination

Returns

id

Returns

inputs

Returns

limits

Returns

log_files

Returns

name

Returns

outputs

Returns

resources

Returns

sampling_algorithm

Returns

search_space

Returns

status

Returns

studio_url

Returns

trial

Returns

type

Returns

Additional resources