Azure GetJob
CertifiedGet the state, metrics and outputs of an Azure Machine Learning job
Azure GetJob
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.
type: io.kestra.plugin.azure.ml.GetJobExamples
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
assets
Assets this task consumes as inputs or produces as outputs, for lineage tracking and the asset graph (Enterprise Edition). A flow declaring this property on a task is rejected in the open-source edition.
io.kestra.core.models.assets.AssetsDeclaration
IGNOREFAILWARNAsset failure behavior
Behavior applied to the task state when a declared asset fails to render, emit, or be persisted (e.g. a lock conflict): FAIL escalates it to FAILED, WARN (default) warns it if it would otherwise succeed, IGNORE leaves the state untouched.
Whether to auto-register assets referenced dynamically at runtime that are not statically declared in inputs or outputs.
The assets consumed as inputs.
io.kestra.core.models.assets.AssetIdentifier
1The assets produced as outputs.
io.kestra.plugin.ee.assets.Dataset
1150{}1150io.kestra.plugin.ee.assets.File
1150{}1150io.kestra.plugin.ee.assets.Table
1150{}1150io.kestra.plugin.ee.assets.VM
1150{}1150io.kestra.core.models.assets.External
1150{}1150io.kestra.core.models.assets.Custom
11501Custom asset type
{}1150clientId 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
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
Outputs
Named job outputs, keyed by output name, pointing to their storage URI
status string
NOT_STARTEDSTARTINGPROVISIONINGPREPARINGQUEUEDRUNNINGFINALIZINGCANCEL_REQUESTEDCOMPLETEDFAILEDCANCELEDNOT_RESPONDINGPAUSEDUNKNOWNJob status
studioUrl string
Studio URL
Deep link to the run in Azure ML Studio
