Azure GetJob

Azure GetJob
Certified
Get the state, metrics and outputs of an Azure Machine Learning job

Reads a job's current status and named outputs from the ARM control plane. Metrics are logged through MLflow rather than the ARM API, so they are fetched separately from the workspace's MLflow tracking server using the same Azure AD credentials; if that call fails (e.g. the service principal lacks the required scope), metrics is returned empty and a warning is logged instead of failing the task.

yaml
type: io.kestra.plugin.azure.ml.GetJob

Examples

yaml
id: azure_ml_get_job
namespace: company.team

inputs:
  - id: job_name
    type: STRING

tasks:
  - id: get_job
    type: io.kestra.plugin.azure.ml.GetJob
    tenantId: "{{ secret('AZURE_TENANT_ID') }}"
    clientId: "{{ secret('AZURE_CLIENT_ID') }}"
    clientSecret: "{{ secret('AZURE_CLIENT_SECRET') }}"
    subscriptionId: "{{ secret('AZURE_SUBSCRIPTION_ID') }}"
    resourceGroupName: ml-rg
    workspaceName: ml-workspace
    jobName: "{{ inputs.job_name }}"

Properties

jobName *string

Job name

Name of the Azure Machine Learning job to inspect

resourceGroupName *string

Resource group name

Resource group containing the Machine Learning workspace

subscriptionId *string

Subscription ID

Azure subscription GUID that owns the Machine Learning workspace

tenantId *string

Azure AD tenant ID (GUID)

workspaceName *string

Workspace name

Name of the Azure Machine Learning workspace

clientId string

Client ID of the Azure AD application

Application (client) ID used for service principal authentication.

clientSecret string

Client secret for the Azure AD application

Secret value associated with the client ID; store in a Kestra secret.

pemCertificate string

PEM-encoded certificate content for client authentication

PEM text for certificate-based auth; alternative to clientSecret.

Outputs

jobName string

Job name

metrics object

SubTypenumber

Metrics

Metrics logged by the run, keyed by metric name.

Azure Machine Learning logs job metrics through MLflow, not through the ARM control-plane API used for everything else in this task. This value is fetched best-effort by reading the workspace's MLflow tracking URI and calling its REST API directly with the same Azure AD bearer token used to authenticate this task. If that call fails (e.g. the service principal lacks the required scope, or the endpoint is unreachable), a warning is logged and this field is an empty map — the task does not fail because of it.

outputs object

SubTypestring

Outputs

Named job outputs, keyed by output name, pointing to their storage URI

status string

Possible Values

NOT_STARTEDSTARTINGPROVISIONINGPREPARINGQUEUEDRUNNINGFINALIZINGCANCEL_REQUESTEDCOMPLETEDFAILEDCANCELEDNOT_RESPONDINGPAUSEDUNKNOWN

Job status

studioUrl string

Studio URL

Deep link to the run in Azure ML Studio