azure.ai.ml.sweep package — Azure SDK for Python 2.0.0 documentation

class azure.ai.ml.sweep.BanditPolicy(*, delay_evaluation: Optional[int] = 0, evaluation_interval: Optional[int] = 0, slack_amount: Optional[float] = 0, slack_factor: Optional[float] = 0)[source]¶

Defines an early termination policy based on slack criteria and a frequency and delay interval for evaluation.

Parameters

delay_evaluation (int) – Number of intervals by which to delay the first evaluation. Defaults to 0.
evaluation_interval (int) – Interval (number of runs) between policy evaluations. Defaults to 0.
slack_amount (float) – Absolute distance allowed from the best performing run. Defaults to 0.
slack_factor (float) – Ratio of the allowed distance from the best performing run. Defaults to 0.

Example:

Configuring BanditPolicy early termination of a hyperparameter sweep on a Command job.¶

from azure.ai.ml import commandjob = command( 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",)# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsfrom azure.ai.ml.sweep import Uniformjob_for_sweep = job( kernel=Uniform(min_value=0.0005, max_value=0.005), penalty=Uniform(min_value=0.9, max_value=0.99),)from azure.ai.ml.sweep import BanditPolicysweep_job = job_for_sweep.sweep( sampling_algorithm="random", primary_metric="best_val_acc", goal="Maximize", max_total_trials=8, max_concurrent_trials=4, early_termination_policy=BanditPolicy(slack_factor=0.15, evaluation_interval=1, delay_evaluation=10),)

Defines an early termination policy based on slack criteria and a frequency and delay interval for evaluation.

Parameters

delay_evaluation (int) – Number of intervals by which to delay the first evaluation. Defaults to 0.
evaluation_interval (int) – Interval (number of runs) between policy evaluations. Defaults to 0.
slack_amount (float) – Absolute distance allowed from the best performing run. Defaults to 0.
slack_factor (float) – Ratio of the allowed distance from the best performing run. Defaults to 0.

class azure.ai.ml.sweep.BayesianSamplingAlgorithm[source]¶

Bayesian Sampling Algorithm.

Example:

Assigning a Bayesian sampling algorithm for a SweepJob¶

from azure.ai.ml.entities import CommandJobfrom azure.ai.ml.sweep import BayesianSamplingAlgorithm, Objective, SweepJob, SweepJobLimitscommand_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"}}, compute="top_level", limits=SweepJobLimits(trial_timeout=600), objective=Objective(goal="maximize", primary_metric="accuracy"),)

Base class for sampling algorithms

class azure.ai.ml.sweep.Choice(values: Optional[List[Union[float,str,dict]]] = None, **kwargs)[source]¶

Choice distribution configuration.

Parameters: values (List[Union[float, str, dict]]) – List of values to choose from.

Example:

Using Choice distribution to set values for a hyperparameter sweep¶

from azure.ai.ml import commandjob = command( 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",)from azure.ai.ml.sweep import Choice, LogUniform# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsjob_for_sweep = job( kernel=LogUniform(min_value=-6, max_value=-1), penalty=Choice([0.9, 0.18, 0.36, 0.72]),)

Parameters: values (List[Union[float, str, dict]]) – List of values to choose from.

class azure.ai.ml.sweep.GridSamplingAlgorithm[source]¶

Grid Sampling Algorithm.

Example:

Assigning a grid sampling algorithm for a SweepJob¶

from azure.ai.ml.entities import CommandJobfrom azure.ai.ml.sweep import GridSamplingAlgorithm, SweepJob, SweepJobLimitscommand_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=GridSamplingAlgorithm(), trial=command_job, search_space={"ss": Choice(type="choice", values=[{"space1": True}, {"space2": True}])}, inputs={"input1": {"file": "top_level.csv", "mode": "ro_mount"}}, compute="top_level", limits=SweepJobLimits(trial_timeout=600),)

Base class for sampling algorithms

class azure.ai.ml.sweep.LogNormal(mu: Optional[float] = None, sigma: Optional[float] = None, **kwargs)[source]¶

LogNormal distribution configuration.

Parameters

mu (float) – Mean of the log of the distribution.
sigma (float) – Standard deviation of the log of the distribution.

Example:

Configuring LogNormal distributions for a hyperparameter sweep on a Command job.¶

from azure.ai.ml import commandjob = command( 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",)from azure.ai.ml.sweep import LogNormal, QLogNormal# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsjob_for_sweep = job( kernel=LogNormal(mu=0.0, sigma=1.0), penalty=QLogNormal(mu=5.0, sigma=2.0),)

LogNormal distribution configuration.

Parameters

mu (float) – Mean of the log of the distribution.
sigma (float) – Standard deviation of the log of the distribution.

class azure.ai.ml.sweep.LogUniform(min_value: Optional[float] = None, max_value: Optional[float] = None, **kwargs)[source]¶

LogUniform distribution configuration.

Parameters

min_value (float) – Minimum value of the log of the distribution.
max_value (float) – Maximum value of the log of the distribution.

Example:

Configuring a LogUniform distribution for a hyperparameter sweep job learning rate¶

from azure.ai.ml import commandjob = command( 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",)from azure.ai.ml.sweep import Choice, LogUniform# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsjob_for_sweep = job( kernel=LogUniform(min_value=-6, max_value=-1), penalty=Choice([0.9, 0.18, 0.36, 0.72]),)

LogUniform distribution configuration.

Parameters

min_value (float) – Minimum value of the log of the distribution.
max_value (float) – Maximum value of the log of the distribution.

class azure.ai.ml.sweep.MedianStoppingPolicy(*, delay_evaluation: Optional[int] = 0, evaluation_interval: Optional[int] = 1)[source]¶

Defines an early termination policy based on a running average of the primary metric of all runs.

Parameters

delay_evaluation (int) – Number of intervals by which to delay the first evaluation. Defaults to 0.
evaluation_interval (int) – Interval (number of runs) between policy evaluations. Defaults to 1.

Example:

Configuring an early termination policy for a hyperparameter sweep job using MedianStoppingPolicy¶

from azure.ai.ml import commandjob = command( 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",)# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsfrom azure.ai.ml.sweep import MedianStoppingPolicy, Uniformjob_for_sweep = job( kernel=Uniform(min_value=0.0005, max_value=0.005), penalty=Uniform(min_value=0.9, max_value=0.99),)sweep_job = job_for_sweep.sweep( sampling_algorithm="random", primary_metric="best_val_acc", goal="Maximize", max_total_trials=8, max_concurrent_trials=4, early_termination_policy=MedianStoppingPolicy(delay_evaluation=5, evaluation_interval=2),)

Defines an early termination policy based on a running average of the primary metric of all runs.

Parameters

delay_evaluation (int) – Number of intervals by which to delay the first evaluation. Defaults to 0.
evaluation_interval (int) – Interval (number of runs) between policy evaluations. Defaults to 1.

class azure.ai.ml.sweep.Normal(mu: Optional[float] = None, sigma: Optional[float] = None, **kwargs)[source]¶

Normal distribution configuration.

Parameters

mu (float) – Mean of the distribution.
sigma (float) – Standard deviation of the distribution.

Example:

Configuring Normal distributions for a hyperparameter sweep on a Command job.¶

from azure.ai.ml import commandjob = command( 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",)from azure.ai.ml.sweep import Normal, Randint# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsjob_for_sweep = job( penalty=Randint(upper=5), kernel=Normal(mu=2.0, sigma=1.0),)

Normal distribution configuration.

Parameters

mu (float) – Mean of the distribution.
sigma (float) – Standard deviation of the distribution.

class azure.ai.ml.sweep.Objective(goal: str, primary_metric: Optional[str] = None)[source]¶

Optimization objective.

Parameters

goal (str) – Required. Defines supported metric goals for hyperparameter tuning. Acceptable values are: “minimize”, “maximize”.
primary_metric (str) – Required. Name of the metric to optimize.

Example:

Assigning an objective to a SweepJob¶

from azure.ai.ml.entities import CommandJobfrom azure.ai.ml.sweep import BayesianSamplingAlgorithm, Objective, SweepJob, SweepJobLimitscommand_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"}}, compute="top_level", limits=SweepJobLimits(trial_timeout=600), objective=Objective(goal="maximize", primary_metric="accuracy"),)

Optimization objective.

Parameters: goal – Required. Defines supported metric goals for hyperparameter tuning. Acceptable values

are: “minimize”, “maximize”. :type goal: str :param primary_metric: Required. The name of the metric to optimize. :type primary_metric: str

class azure.ai.ml.sweep.QLogNormal(mu: Optional[float] = None, sigma: Optional[float] = None, q: Optional[int] = None, **kwargs)[source]¶

QLogNormal distribution configuration.

Parameters

mu (float) – Mean of the log of the distribution.
sigma (float) – Standard deviation of the log of the distribution.

Example:

Configuring QLogNormal distributions for a hyperparameter sweep on a Command job.¶

from azure.ai.ml import commandjob = command( 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",)from azure.ai.ml.sweep import LogNormal, QLogNormal# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsjob_for_sweep = job( kernel=LogNormal(mu=0.0, sigma=1.0), penalty=QLogNormal(mu=5.0, sigma=2.0),)

QLogNormal distribution configuration.

Parameters

mu (float) – Mean of the log of the distribution.
sigma (float) – Standard deviation of the log of the distribution.

class azure.ai.ml.sweep.QLogUniform(min_value: Optional[float] = None, max_value: Optional[float] = None, q: Optional[int] = None, **kwargs)[source]¶

QLogUniform distribution configuration.

Parameters

min_value (float) – Minimum value of the log of the distribution.
max_value (float) – Maximum value of the log of the distribution.

Example:

Configuring QLogUniform distributions for a hyperparameter sweep on a Command job.¶

from azure.ai.ml import commandjob = command( 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",)from azure.ai.ml.sweep import QLogUniform, QNormal# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsjob_for_sweep = job( penalty=QNormal(mu=2.0, sigma=1.0, q=1), kernel=QLogUniform(min_value=1.0, max_value=5.0),)

QLogUniform distribution configuration.

Parameters

min_value (float) – Minimum value of the log of the distribution.
max_value (float) – Maximum value of the log of the distribution.

class azure.ai.ml.sweep.QNormal(mu: Optional[float] = None, sigma: Optional[float] = None, q: Optional[int] = None, **kwargs)[source]¶

QNormal distribution configuration.

Parameters

mu (float) – Mean of the distribution.
sigma (float) – Standard deviation of the distribution.
q (int) – Quantization factor.

Example:

Configuring QNormal distributions for a hyperparameter sweep on a Command job.¶

from azure.ai.ml import commandjob = command( 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",)from azure.ai.ml.sweep import QLogUniform, QNormal# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsjob_for_sweep = job( penalty=QNormal(mu=2.0, sigma=1.0, q=1), kernel=QLogUniform(min_value=1.0, max_value=5.0),)

QNormal distribution configuration.

Parameters

mu (float) – Mean of the distribution.
sigma (float) – Standard deviation of the distribution.
q (int) – Quantization factor.

class azure.ai.ml.sweep.QUniform(min_value: Optional[Union[int,float]] = None, max_value: Optional[Union[int,float]] = None, q: Optional[int] = None, **kwargs)[source]¶

QUniform distribution configuration.

Parameters

min_value (float) – Minimum value of the distribution.
max_value (float) – Maximum value of the distribution.
q (int) – Quantization factor.

Example:

Configuring QUniform distributions for a hyperparameter sweep on a Command job.¶

from azure.ai.ml import commandjob = command( 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",)# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsfrom azure.ai.ml.sweep import QUniform, TruncationSelectionPolicy, Uniformjob_for_sweep = job( kernel=Uniform(min_value=0.0005, max_value=0.005), penalty=QUniform(min_value=0.05, max_value=0.75, q=1),)sweep_job = job_for_sweep.sweep( sampling_algorithm="random", primary_metric="best_val_acc", goal="Maximize", max_total_trials=8, max_concurrent_trials=4, early_termination_policy=TruncationSelectionPolicy(delay_evaluation=5, evaluation_interval=2),)

QUniform distribution configuration.

Parameters

min_value (float) – Minimum value of the distribution.
max_value (float) – Maximum value of the distribution.
q (int) – Quantization factor.

class azure.ai.ml.sweep.Randint(upper: Optional[int] = None, **kwargs)[source]¶

Randint distribution configuration.

Parameters: upper (int) – Upper bound of the distribution.

Example:

Configuring Randint distributions for a hyperparameter sweep on a Command job.¶

from azure.ai.ml import commandjob = command( 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",)from azure.ai.ml.sweep import Normal, Randint# we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurationsjob_for_sweep = job( penalty=Randint(upper=5), kernel=Normal(mu=2.0, sigma=1.0),)

Randint distribution configuration.

Parameters: upper (int) – Upper bound of the distribution.

class azure.ai.ml.sweep.RandomSamplingAlgorithm(*, rule: Optional[str] = None, seed: Optional[int] = None, logbase: Optional[Union[float,str]] = None)[source]¶

Random Sampling Algorithm.

Parameters

rule (str) – The specific type of random algorithm. Acceptable values are: “random”, “sobol”.
seed (int) – The seed for random number generation.
logbase (Union[float, str]) – A positive number or the number “e” in string format to be used as the base for log based random sampling.

Example:

Assigning a random sampling algorithm for a SweepJob¶

from azure.ai.ml.entities import CommandJobfrom azure.ai.ml.sweep import RandomSamplingAlgorithm, SweepJob, SweepJobLimitscommand_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=RandomSamplingAlgorithm(seed=999, rule="sobol", logbase="e"), trial=command_job, search_space={"ss": Choice(type="choice", values=[{"space1": True}, {"space2": True}])}, inputs={"input1": {"file": "top_level.csv", "mode": "ro_mount"}}, compute="top_level", limits=SweepJobLimits(trial_timeout=600),)

Random Sampling Algorithm.

Parameters

rule (str) – The specific type of random algorithm. Acceptable values are: “random”, “sobol”.
seed (int) – The seed for random number generation.
logbase (Union[float, str]) – A positive number or the number “e” in string format to be used as the base for log based random sampling.

class azure.ai.ml.sweep.SamplingAlgorithm[source]¶

Base class for sampling algorithms.

You should not instantiate this class directly. Instead, use one of its subclasses.

Base class for sampling algorithms

class azure.ai.ml.sweep.SweepJob(*, name: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, display_name: Optional[str] = None, experiment_name: Optional[str] = None, identity: Optional[Union[azure.ai.ml.entities._credentials.ManagedIdentityConfiguration,azure.ai.ml.entities._credentials.AmlTokenConfiguration,azure.ai.ml.entities._credentials.UserIdentityConfiguration]] = None, inputs: Optional[Dict[str,Union[int,str,float,bool,azure.ai.ml.entities._inputs_outputs.input.Input]]] = None, outputs: Optional[Dict[str,azure.ai.ml.entities._inputs_outputs.output.Output]] = None, compute: Optional[str] = None, limits: Optional[azure.ai.ml.entities._job.job_limits.SweepJobLimits] = None, sampling_algorithm: Optional[Union[str,azure.ai.ml.entities._job.sweep.sampling_algorithm.SamplingAlgorithm]] = None, search_space: Optional[Dict[str,Union[azure.ai.ml.entities._job.sweep.search_space.Choice,azure.ai.ml.entities._job.sweep.search_space.LogNormal,azure.ai.ml.entities._job.sweep.search_space.LogUniform,azure.ai.ml.entities._job.sweep.search_space.Normal,azure.ai.ml.entities._job.sweep.search_space.QLogNormal,azure.ai.ml.entities._job.sweep.search_space.QLogUniform,azure.ai.ml.entities._job.sweep.search_space.QNormal,azure.ai.ml.entities._job.sweep.search_space.QUniform,azure.ai.ml.entities._job.sweep.search_space.Randint,azure.ai.ml.entities._job.sweep.search_space.Uniform]]] = None, objective: Optional[azure.ai.ml.entities._job.sweep.objective.Objective] = None, trial: Optional[Union[azure.ai.ml.entities._job.command_job.CommandJob,azure.ai.ml.entities._component.command_component.CommandComponent]] = None, early_termination: Optional[Union[azure.ai.ml.entities._job.sweep.early_termination_policy.BanditPolicy,azure.ai.ml.entities._job.sweep.early_termination_policy.MedianStoppingPolicy,azure.ai.ml.entities._job.sweep.early_termination_policy.TruncationSelectionPolicy]] = None, queue_settings: Optional[azure.ai.ml.entities._job.queue_settings.QueueSettings] = None, **kwargs: Any)[source]¶

Sweep job for hyperparameter tuning.

Parameters

name (str) – Name of the job.
display_name (str) – Display name of the job.
description (str) – Description of the job.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
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’.
identity (Union[ManagedIdentityConfiguration,AmlTokenConfiguration,UserIdentityConfiguration]) – Identity that the training job will use while running on compute.
inputs (dict) – Inputs to the command.
outputs (dict[str, Output]) – Mapping of output data bindings used in the job.
sampling_algorithm (str) – The hyperparameter sampling algorithm to use over the search_space. Defaults to “random”.
search_space (Dict) – Dictionary of the hyperparameter search space. The key is the name of the hyperparameter and the value is the parameter expression.
objective (Objective) – Metric to optimize for.
compute (str) – The compute target the job runs on.
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.
early_termination (Union[) – 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.

~azure.mgmt.machinelearningservices.models.BanditPolicy, ~azure.mgmt.machinelearningservices.models.MedianStoppingPolicy, ~azure.mgmt.machinelearningservices.models.TruncationSelectionPolicy] :param limits: Limits for the sweep job. :type limits: ~azure.ai.ml.entities.SweepJobLimits :param queue_settings: Queue settings for the job. :type queue_settings: ~azure.ai.ml.entities.QueueSettings :param kwargs: A dictionary of additional configuration parameters. :type kwargs: dict

Example:

Creating a SweepJob¶

from azure.ai.ml.entities import CommandJobfrom azure.ai.ml.sweep import BayesianSamplingAlgorithm, Objective, SweepJob, SweepJobLimitscommand_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"}}, compute="top_level", limits=SweepJobLimits(trial_timeout=600), objective=Objective(goal="maximize", primary_metric="accuracy"),)

Sweep job for hyperparameter tuning.

Parameters

name (str) – Name of the job.
display_name (str) – Display name of the job.
description (str) – Description of the job.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
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’.
identity (Union[ManagedIdentityConfiguration,AmlTokenConfiguration,UserIdentityConfiguration]) – Identity that the training job will use while running on compute.
inputs (dict) – Inputs to the command.
outputs (dict[str, Output]) – Mapping of output data bindings used in the job.
sampling_algorithm (str) – The hyperparameter sampling algorithm to use over the search_space. Defaults to “random”.
search_space (Dict) – Dictionary of the hyperparameter search space. The key is the name of the hyperparameter and the value is the parameter expression.
objective (Objective) – Metric to optimize for.
compute (str) – The compute target the job runs on.
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.
early_termination (Union[) – 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.

dump(dest: Union[str,os.PathLike,IO], **kwargs) → None ¶

Dump the job content into a file in yaml format.

Parameters: dest (Union[PathLike, str, IO[AnyStr]]) – The destination to receive this job’s content. Must be either a path to a local file, or an already-open file stream. If dest is a file path, a new file will be created, and an exception is raised if the file exists. If dest is an open file, the file will be written to directly, and an exception will be raised if the file is not writable.

set_limits(*, max_concurrent_trials: Optional[int] = None, max_total_trials: Optional[int] = None, timeout: Optional[int] = None, trial_timeout: Optional[int] = None) → None ¶

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

Parameters

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(*, goal: Optional[str] = None, primary_metric: Optional[str] = None) → None ¶

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

Parameters: goal – Defines supported metric goals for hyperparameter tuning. Acceptable values are:

“minimize”, “maximize”. :type goal: str :param primary_metric: Name of the metric to optimize. :type primary_metric: str

property base_path¶

Base path of the resource.

Returns: Base path of the resource
Return type: str

property creation_context¶

Creation context.

Returns: Creation metadata of the resource.
Return type: typing.Optional[SystemData]

property early_termination¶

Early termination policy for sweep job.

Returns: Early termination policy for sweep job.
Return type: EarlyTerminationPolicy

property id¶

Resource ID.

Returns: Global id of the resource, Azure Resource Manager ID
Return type: typing.Optional[str]

property inputs¶

property limits¶

Limits for sweep job.

Returns: Limits for sweep job.
Return type: SweepJobLimits

property log_files¶

Job output files.

Returns: Dictionary of log names to url.
Return type: Optional[Dict[str, str]]

property outputs¶

property sampling_algorithm¶

Sampling algorithm for sweep job.

Returns: Sampling algorithm for sweep job.
Return type: SamplingAlgorithm

property status¶

Status of the job.

Common values returned include “Running”, “Completed”, and “Failed”.

Note

NotStarted - This is a temporary state 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 - Returned when on-demand compute is being created for a given job submission.
Preparing - The run environment is being prepared:
- docker image build
- conda environment setup
Queued - The job is queued in the compute target. For example, in BatchAI the job is in queued state
while waiting for all the requested nodes to be ready.
Running - The job started to run in the compute target.
Finalizing - User code has completed and the run is in post-processing stages.
CancelRequested - Cancellation has been requested for the job.
Completed - The run completed successfully. This includes both the user code 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: Status of the job.
Return type: str

property studio_url¶

Azure ML studio endpoint.

Returns: URL to the job detail page.
Return type: Optional[str]

property type¶

Type of the job, supported are ‘command’ and ‘sweep’.

Returns: Type of the job.
Return type: str

class azure.ai.ml.sweep.SweepJobLimits(*, max_concurrent_trials: Optional[int] = None, max_total_trials: Optional[int] = None, timeout: Optional[int] = None, trial_timeout: Optional[int] = None)[source]¶

Sweep Job limit class.

Variables are only populated by the server, and will be ignored when sending a request.

Parameters

max_concurrent_trials (int) – Sweep Job max concurrent trials.
max_total_trials (int) – Sweep Job max total trials.
timeout (int) – The max run duration in seconds , after which the job will be cancelled. Only supports duration with precision as low as Seconds.
trial_timeout (int) – Sweep Job Trial timeout value in seconds.

Example:

Assigning limits to a SweepJob¶

from azure.ai.ml.entities import CommandJobfrom azure.ai.ml.sweep import BayesianSamplingAlgorithm, Objective, SweepJob, SweepJobLimitscommand_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"}}, compute="top_level", limits=SweepJobLimits(trial_timeout=600), objective=Objective(goal="maximize", primary_metric="accuracy"),)

Sweep Job limit class.

Parameters

max_concurrent_trials (int) – Sweep Job max concurrent trials.
max_total_trials (int) – Sweep Job max total trials.
timeout – The max run duration in seconds , after which the job will be cancelled.

Only supports duration with precision as low as Seconds. :type timeout: int :param trial_timeout: Sweep Job Trial timeout value in seconds. :type trial_timeout: int

property timeout¶

property trial_timeout¶

class azure.ai.ml.sweep.TruncationSelectionPolicy(*, delay_evaluation: Optional[int] = 0, evaluation_interval: Optional[int] = 0, truncation_percentage: Optional[int] = 0)[source]¶

Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.

Parameters

delay_evaluation (int) – Number of intervals by which to delay the first evaluation. Defaults to 0.
evaluation_interval (int) – Interval (number of runs) between policy evaluations. Defaults to 0.
truncation_percentage (int) – The percentage of runs to cancel at each evaluation interval. Defaults to 0.

Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.

Parameters

delay_evaluation (int) – Number of intervals by which to delay the first evaluation.
evaluation_interval (int) – Interval (number of runs) between policy evaluations.
truncation_percentage (int) – The percentage of runs to cancel at each evaluation interval.

class azure.ai.ml.sweep.Uniform(min_value: Optional[float] = None, max_value: Optional[float] = None, **kwargs)[source]¶