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CommandJob Class

  • Reference

Command job.

Note

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

AZUREML_PARAMETER_. For example, if you have a parameter named "input_data", you can access it as

AZUREML_PARAMETER_input_data.

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.

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.