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CustomModelFineTuningJob Class

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Constructor

CustomModelFineTuningJob(**kwargs: Any)

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.

hyperparameters

Get hyperparameters.

Returns

Type Description
hyperparameters: Dict[str,str]

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.

model

The model to be fine-tuned. :return: Input object representing the mlflow model to be fine-tuned. :rtype: Input

model_provider

The model provider. :return: The model provider. :rtype: str

outputs

queue_settings

Queue settings for job execution. :return: QueueSettings object. :rtype: QueueSettings

resources

Job resources to use during job execution. :return: Job Resources object. :rtype: JobResources

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.

task

Get finetuning task.

Returns

Type Description
str

The type of task to run. Possible values include: "ChatCompletion" "TextCompletion", "TextClassification", "QuestionAnswering","TextSummarization", "TokenClassification", "TextTranslation", "ImageClassification", "ImageInstanceSegmentation", "ImageObjectDetection","VideoMultiObjectTracking".

training_data

Get training data.

Returns

Type Description
Input

Training data input

type

The type of the job.

Returns

Type Description
Optional[str]

The type of the job.

validation_data

Get validation data.

Returns

Type Description
Input

Validation data input