Class State (1.10.3)

Version latest

• 1.10.3 (latest)
• 1.10.2
• 1.9.0
• 1.8.3
• 1.7.0
• 1.6.3
• 1.5.2
• 1.4.0
• 1.3.0
• 1.2.4
• 1.1.0
• 1.0.0
• 0.4.2
• 0.3.0
• 0.2.1

State(value)

State of the job.

Values: STATE_UNSPECIFIED (0): No description available. SCHEDULED (1): The job is scheduled to run at the [configured interval][google.cloud.datalabeling.v1beta1.EvaluationJob.schedule]. You can pause or delete the job.

    When the job is in this state, it samples prediction input
    and output from your model version into your BigQuery table
    as predictions occur.
RUNNING (2):
    The job is currently running. When the job runs, Data
    Labeling Service does several things:

    1. If you have configured your job to use Data Labeling
       Service for ground truth labeling, the service creates a
       <xref uid="google.cloud.datalabeling.v1beta1.Dataset">Dataset</xref> and
       a labeling task for all data sampled since the last time
       the job ran. Human labelers provide ground truth labels
       for your data. Human labeling may take hours, or even
       days, depending on how much data has been sampled. The
       job remains in the `RUNNING` state during this time,
       and it can even be running multiple times in parallel if
       it gets triggered again (for example 24 hours later)
       before the earlier run has completed. When human labelers
       have finished labeling the data, the next step occurs. If
       you have configured your job to provide your own ground
       truth labels, Data Labeling Service still creates a
       <xref uid="google.cloud.datalabeling.v1beta1.Dataset">Dataset</xref> for
       newly sampled data, but it expects that you have already
       added ground truth labels to the BigQuery table by this
       time. The next step occurs immediately.

    2. Data Labeling Service creates an
       <xref uid="google.cloud.datalabeling.v1beta1.Evaluation">Evaluation</xref>
       by comparing your model version's predictions with the
       ground truth labels.

    If the job remains in this state for a long time, it
    continues to sample prediction data into your BigQuery table
    and will run again at the next interval, even if it causes
    the job to run multiple times in parallel.
PAUSED (3):
    The job is not sampling prediction input and output into
    your BigQuery table and it will not run according to its
    schedule. You can
    <xref uid="google.cloud.datalabeling.v1beta1.DataLabelingService.ResumeEvaluationJob">resume</xref>
    the job.
STOPPED (4):
    The job has this state right before it is
    deleted.