tuning_jobs

Creates, updates, deletes, gets or lists a tuning_jobs resource.

Overview

Name	tuning_jobs
Type	Resource
Id	google.aiplatform.tuning_jobs

Fields

Name	Datatype	Description
name	string	Output only. Identifier. Resource name of a TuningJob. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}
description	string	Optional. The description of the TuningJob.
baseModel	string	The base model that is being tuned, e.g., "gemini-1.0-pro-002".
createTime	string	Output only. Time when the TuningJob was created.
encryptionSpec	object	Represents a customer-managed encryption key spec that can be applied to a top-level resource.
endTime	string	Output only. Time when the TuningJob entered any of the following JobStates: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED, JOB_STATE_EXPIRED.
error	object	The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide.
experiment	string	Output only. The Experiment associated with this TuningJob.
labels	object	Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
startTime	string	Output only. Time when the TuningJob for the first time entered the JOB_STATE_RUNNING state.
state	string	Output only. The detailed state of the job.
supervisedTuningSpec	object	Tuning Spec for Supervised Tuning.
tunedModel	object	The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob.
tunedModelDisplayName	string	Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.
tuningDataStats	object	The tuning data statistic values for TuningJob.
updateTime	string	Output only. Time when the TuningJob was most recently updated.

Methods

Name	Accessible by	Required Params	Description
get	SELECT	locationsId, projectsId, tuningJobsId	Gets a TuningJob.
list	SELECT	locationsId, projectsId	Lists TuningJobs in a Location.
create	INSERT	locationsId, projectsId	Creates a TuningJob. A created TuningJob right away will be attempted to be run.
cancel	EXEC	locationsId, projectsId, tuningJobsId	Cancels a TuningJob. Starts asynchronous cancellation on the TuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use GenAiTuningService.GetTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the TuningJob is not deleted; instead it becomes a job with a TuningJob.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED, and TuningJob.state is set to CANCELLED.

SELECT examples

Lists TuningJobs in a Location.

SELECT
name,
description,
baseModel,
createTime,
encryptionSpec,
endTime,
error,
experiment,
labels,
startTime,
state,
supervisedTuningSpec,
tunedModel,
tunedModelDisplayName,
tuningDataStats,
updateTime
FROM google.aiplatform.tuning_jobs
WHERE locationsId = '{{ locationsId }}'
AND projectsId = '{{ projectsId }}'; 

INSERT example

Use the following StackQL query and manifest file to create a new tuning_jobs resource.

All Properties

/*+ create */
INSERT INTO google.aiplatform.tuning_jobs (
locationsId,
projectsId,
supervisedTuningSpec,
encryptionSpec,
tunedModelDisplayName,
description,
baseModel,
labels
)
SELECT 
'{{ locationsId }}',
'{{ projectsId }}',
'{{ supervisedTuningSpec }}',
'{{ encryptionSpec }}',
'{{ tunedModelDisplayName }}',
'{{ description }}',
'{{ baseModel }}',
'{{ labels }}'
;

Manifest

- name: your_resource_model_name
  props:
    - name: supervisedTuningSpec
      value:
        - name: validationDatasetUri
          value: '{{ validationDatasetUri }}'
        - name: trainingDatasetUri
          value: '{{ trainingDatasetUri }}'
        - name: hyperParameters
          value:
            - name: adapterSize
              value: '{{ adapterSize }}'
            - name: learningRateMultiplier
              value: '{{ learningRateMultiplier }}'
            - name: epochCount
              value: '{{ epochCount }}'
    - name: encryptionSpec
      value:
        - name: kmsKeyName
          value: '{{ kmsKeyName }}'
    - name: tunedModelDisplayName
      value: '{{ tunedModelDisplayName }}'
    - name: description
      value: '{{ description }}'
    - name: baseModel
      value: '{{ baseModel }}'
    - name: labels
      value: '{{ labels }}'