Enum EvaluationJob.State (0.131.0)

Version 0.131.0

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public enum EvaluationJob.State extends Enum<EvaluationJob.State> implements ProtocolMessageEnum

State of the job.

Protobuf enum google.cloud.datalabeling.v1beta1.EvaluationJob.State

Implements

ProtocolMessageEnum

Inherited Members

Enum.<T>valueOf(Class<T>,String)

Enum.clone()

Enum.compareTo(E)

Enum.equals(Object)

Enum.finalize()

Enum.getDeclaringClass()

Enum.hashCode()

Enum.name()

Enum.ordinal()

Enum.toString()

Object.getClass()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Fields

Name	Description
PAUSED	The job is not sampling prediction input and output into your BigQuery table and it will not run according to its schedule. You can resume the job. PAUSED = 3;
PAUSED_VALUE	The job is not sampling prediction input and output into your BigQuery table and it will not run according to its schedule. You can resume the job. PAUSED = 3;
RUNNING	The job is currently running. When the job runs, Data Labeling Service does several things: If you have configured your job to use Data Labeling Service for ground truth labeling, the service creates a Dataset 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. <br><br> If you have configured your job to provide your own ground truth labels, Data Labeling Service still creates a Dataset 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. Data Labeling Service creates an Evaluation 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. RUNNING = 2;
RUNNING_VALUE	The job is currently running. When the job runs, Data Labeling Service does several things: If you have configured your job to use Data Labeling Service for ground truth labeling, the service creates a Dataset 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. <br><br> If you have configured your job to provide your own ground truth labels, Data Labeling Service still creates a Dataset 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. Data Labeling Service creates an Evaluation 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. RUNNING = 2;
SCHEDULED	The job is scheduled to run at the configured interval. 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. SCHEDULED = 1;
SCHEDULED_VALUE	The job is scheduled to run at the configured interval. 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. SCHEDULED = 1;
STATE_UNSPECIFIED	STATE_UNSPECIFIED = 0;
STATE_UNSPECIFIED_VALUE	STATE_UNSPECIFIED = 0;
STOPPED	The job has this state right before it is deleted. STOPPED = 4;
STOPPED_VALUE	The job has this state right before it is deleted. STOPPED = 4;
UNRECOGNIZED

Static Methods

Name	Description
forNumber(int value)
getDescriptor()
internalGetValueMap()
valueOf(Descriptors.EnumValueDescriptor desc)
valueOf(int value)	Deprecated. Use #forNumber(int) instead.
valueOf(String name)
values()

Methods

Name	Description
getDescriptorForType()
getNumber()
getValueDescriptor()