PipelineJob Class

Pipeline job.

You should not instantiate this class directly. Instead, you should use the @pipeline decorator to create a PipelineJob.

] :param compute: Compute target name of the built pipeline. Defaults to None :type compute: str :param tags: Tag dictionary. Tags can be added, removed, and updated. Defaults to None :type tags: dict[str, str] :param kwargs: A dictionary of additional configuration parameters. Defaults to None :type kwargs: dict

Inheritance

azure.ai.ml.entities._job.job.Job

PipelineJob

azure.ai.ml.entities._mixins.YamlTranslatableMixin

PipelineJob

azure.ai.ml.entities._job.pipeline._io.mixin.PipelineJobIOMixin

PipelineJob

azure.ai.ml.entities._validation.path_aware_schema.PathAwareSchemaValidatableMixin

PipelineJob

Constructor

PipelineJob(*, component: str | PipelineComponent | Component | None = None, inputs: Dict[str, Input | str | bool | int | float] | None = None, outputs: Dict[str, Output] | None = None, name: str | None = None, description: str | None = None, display_name: str | None = None, experiment_name: str | None = None, jobs: Dict[str, BaseNode] | None = None, settings: PipelineJobSettings | None = None, identity: ManagedIdentityConfiguration | AmlTokenConfiguration | UserIdentityConfiguration | None = None, compute: str | None = None, tags: Dict[str, str] | None = None, **kwargs: Any)

Parameters

Name	Description
component Required	Union[str, PipelineComponent] Pipeline component version. The field is mutually exclusive with 'jobs'.
inputs Required	dict[str, Union[<xref:azure.ai.ml.entities.Input>, str, bool, int, float]] Inputs to the pipeline job.
outputs Required	dict[str, <xref:azure.ai.ml.entities.Output>] Outputs of the pipeline job.
name Required	str Name of the PipelineJob. Defaults to None.
description Required	str Description of the pipeline job. Defaults to None
display_name Required	str Display name of the pipeline job. Defaults to None
experiment_name Required	str Name of the experiment the job will be created under. If None is provided, the experiment will be set to the current directory. Defaults to None
jobs Required	dict[str, <xref:azure.ai.ml.entities._builders.BaseNode>] Pipeline component node name to component object. Defaults to None
settings Required	PipelineJobSettings Setting of the pipeline job. Defaults to None
identity Required	Union[ ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration Identity that the training job will use while running on compute. Defaults to None

Keyword-Only Parameters

Name	Description
component Required
inputs Required
outputs Required
name Required
description Required
display_name Required
experiment_name Required
jobs Required
settings Required
identity Required
compute Required
tags Required

Examples

Shows how to create a pipeline using this class.

   from azure.ai.ml.entities import PipelineJob, PipelineJobSettings

   pipeline_job = PipelineJob(
       description="test pipeline job",
       tags={},
       display_name="test display name",
       experiment_name="pipeline_job_samples",
       properties={},
       settings=PipelineJobSettings(force_rerun=True, default_compute="cpu-cluster"),
       jobs={"component1": component_func(component_in_number=1.0, component_in_path=uri_file_input)},
   )

   ml_client.jobs.create_or_update(pipeline_job)

Methods

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

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.

Keyword-Only Parameters

Name	Description
kwargs	dict Additional arguments to pass to the YAML serializer.

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.

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.

id

The resource ID.

Returns

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

inputs

Inputs of the pipeline job.

Returns

Type	Description
dict[str, Union[Input, str, bool, int, float]]	Inputs of the pipeline job.

jobs

Return jobs of pipeline job.

Returns

Type	Description
dict	Jobs of pipeline job.

log_files

Job output files.

Returns

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

outputs

Outputs of the pipeline job.

Returns

Type	Description
dict[str, Union[str, Output]]	Outputs of the pipeline job.

settings

Settings of the pipeline job.

Returns

Type	Description
PipelineJobSettings	Settings of the pipeline 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.