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SweepJob Class

Sweep job for hyperparameter tuning.

Note

For sweep jobs, inputs, outputs, and parameters are accessible as environment variables using the prefix

AZUREML_SWEEP_. For example, if you have a parameter named "learning_rate", you can access it as

AZUREML_SWEEP_learning_rate.

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Constructor

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

Keyword-Only Parameters

Name Description
name
str

Name of the job.

Default value: None
display_name
str

Display name of the job.

Default value: None
description
str

Description of the job.

Default value: None
tags
dict[str, str]

Tag dictionary. Tags can be added, removed, and updated.

Default value: None
properties
dict[str, str]

The asset property dictionary.
experiment_name
str

Name of the experiment the job will be created under. If None is provided, job will be created under experiment 'Default'.

Default value: None
identity
Union[ <xref:azure.ai.ml.ManagedIdentityConfiguration>, <xref:azure.ai.ml.AmlTokenConfiguration>, <xref:azure.ai.ml.UserIdentityConfiguration>

Identity that the training job will use while running on compute.

Default value: None
inputs
dict

Inputs to the command.

Default value: None
outputs
dict[str, Output]

Mapping of output data bindings used in the job.

Default value: None
sampling_algorithm
str

The hyperparameter sampling algorithm to use over the search_space. Defaults to "random".

Default value: None
search_space
Dict

Dictionary of the hyperparameter search space. The key is the name of the hyperparameter and the value is the parameter expression.

Default value: None
objective
Objective

Metric to optimize for.

Default value: None
compute
str

The compute target the job runs on.

Default value: None
trial
Union[ CommandJob, CommandComponent

The job configuration for each trial. Each trial will be provided with a different combination of hyperparameter values that the system samples from the search_space.

Default value: None
early_termination
Union[ BanditPolicy, MedianStoppingPolicy, TruncationSelectionPolicy

The early termination policy to use. A trial job is canceled when the criteria of the specified policy are met. If omitted, no early termination policy will be applied.

Default value: None
limits
<xref:azure.ai.ml.entities.SweepJobLimits>

Limits for the sweep job.

Default value: None
queue_settings
QueueSettings

Queue settings for the job.

Default value: None
resources
Optional[Union[ResourceConfiguration]

Compute Resource configuration for the job.

Default value: None

Examples

Creating a SweepJob


   from azure.ai.ml.entities import CommandJob
   from azure.ai.ml.sweep import BayesianSamplingAlgorithm, Objective, SweepJob, SweepJobLimits

   command_job = CommandJob(
       inputs=dict(kernel="linear", penalty=1.0),
       compute=cpu_cluster,
       environment=f"{job_env.name}:{job_env.version}",
       code="./scripts",
       command="python scripts/train.py --kernel $kernel --penalty $penalty",
       experiment_name="sklearn-iris-flowers",
   )

   sweep = SweepJob(
       sampling_algorithm=BayesianSamplingAlgorithm(),
       trial=command_job,
       search_space={"ss": Choice(type="choice", values=[{"space1": True}, {"space2": True}])},
       inputs={"input1": {"file": "top_level.csv", "mode": "ro_mount"}},  # type:ignore
       compute="top_level",
       limits=SweepJobLimits(trial_timeout=600),
       objective=Objective(goal="maximize", primary_metric="accuracy"),
   )

Methods

dump

Dumps the job content into a file in YAML format.
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.

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.

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.

Default value: None
max_total_trials
int

maximum total trial number.

Default value: None
timeout
int

total timeout in seconds for sweep node

Default value: None
trial_timeout
int

timeout in seconds for each trial

Default value: None

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".

Default value: None
primary_metric
str

Name of the metric to optimize.

Default value: None

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.

Default value: None
instance_count
Optional[int]

The number of instances to use for the job.

Default value: None
locations
Optional[List[str]]

The locations to use for the job.

Default value: None
properties
Optional[Dict]

The properties for the job.

Default value: None
docker_args
Optional[str]

The docker arguments for the job.

Default value: None
shm_size
Optional[str]

The shared memory size for the job.

Default value: None

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

Early termination policy for sweep job.

Returns

Type Description
<xref:azure.ai.ml.entities._job.sweep.early_termination_policy.EarlyTerminationPolicy>

Early termination policy for sweep job.

id

The resource ID.

Returns

Type Description
Optional[str]

The global ID of the resource, an Azure Resource Manager (ARM) ID.

inputs

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.

outputs

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.

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.

type

The type of the job.

Returns

Type Description
Optional[str]

The type of the job.