CommandJob Class

Command job.

Inheritance

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

CommandJob

azure.ai.ml.entities._job.parameterized_command.ParameterizedCommand

CommandJob

azure.ai.ml.entities._job.job_io_mixin.JobIOMixin

CommandJob

Constructor

CommandJob(*, inputs: Dict[str, Input | str | bool | int | float] | None = None, outputs: Dict[str, Output] | None = None, limits: CommandJobLimits | None = None, identity: Dict | ManagedIdentityConfiguration | AmlTokenConfiguration | UserIdentityConfiguration | None = None, services: Dict[str, JobService | JupyterLabJobService | SshJobService | TensorBoardJobService | VsCodeJobService] | None = None, **kwargs: Any)

Keyword-Only Parameters

Name	Description
services	Optional[dict[str, JobService]] Read-only information on services associated with the job.
inputs	Optional[dict[str, Union[Input, str, bool, int, float]]] Mapping of output data bindings used in the command.
outputs	Optional[dict[str, Output]] Mapping of output data bindings used in the job.
identity	Optional[Union[<xref:azure.ai.ml.ManagedIdentityConfiguration>, <xref:azure.ai.ml.AmlTokenConfiguration>, <xref:azure.ai.ml.UserIdentityConfiguration>]] The identity that the job will use while running on compute.
limits	Optional[CommandJobLimits] The limits for the job.
kwargs	dict A dictionary of additional configuration parameters.

Examples

Configuring a CommandJob.

   command_job = CommandJob(
       code="./src",
       command="python train.py --ss {search_space.ss}",
       inputs={"input1": Input(path="trial.csv")},
       outputs={"default": Output(path="./foo")},
       compute="trial",
       environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
       limits=CommandJobLimits(timeout=120),
   )

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.

distribution

The configuration for the distributed command component or job.

Returns

Type	Description
Union[PyTorchDistribution, MpiDistribution, TensorFlowDistribution, RayDistribution]	The distribution configuration.

id

The resource ID.

Returns

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

inputs

log_files

Job output files.

Returns

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

outputs

parameters

MLFlow parameters.

Returns

Type	Description
dict[str, str]	MLFlow parameters logged in job.

resources

The compute resource configuration for the command component or job.

Returns

Type	Description
JobResourceConfiguration	The compute resource configuration for the command component or 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.