Class: Google::Cloud::DataLabeling::V1beta1::EvaluationJob

Inherits:
Object
  • Object
show all
Extended by:
Protobuf::MessageExts::ClassMethods
Includes:
Protobuf::MessageExts
Defined in:
proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb

Overview

Defines an evaluation job that runs periodically to generate Evaluations. Creating an evaluation job is the starting point for using continuous evaluation.

Defined Under Namespace

Modules: State

Instance Attribute Summary collapse

Instance Attribute Details

#annotation_spec_set ⇒ ::String

Returns Required. Name of the AnnotationSpecSet describing all the labels that your machine learning model outputs. You must create this resource before you create an evaluation job and provide its name in the following format:

"projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}".

Returns:

  • (::String) —

    Required. Name of the AnnotationSpecSet describing all the labels that your machine learning model outputs. You must create this resource before you create an evaluation job and provide its name in the following format:

    "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#attempts ⇒ ::Array<::Google::Cloud::DataLabeling::V1beta1::Attempt>

Returns Output only. Every time the evaluation job runs and an error occurs, the failed attempt is appended to this array.

Returns:



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#create_time ⇒ ::Google::Protobuf::Timestamp

Returns Output only. Timestamp of when this evaluation job was created.

Returns:



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#description ⇒ ::String

Returns Required. Description of the job. The description can be up to 25,000 characters long.

Returns:

  • (::String) —

    Required. Description of the job. The description can be up to 25,000 characters long.



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#evaluation_job_config ⇒ ::Google::Cloud::DataLabeling::V1beta1::EvaluationJobConfig

Returns Required. Configuration details for the evaluation job.

Returns:



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#label_missing_ground_truth ⇒ ::Boolean

Returns Required. Whether you want Data Labeling Service to provide ground truth labels for prediction input. If you want the service to assign human labelers to annotate your data, set this to true. If you want to provide your own ground truth labels in the evaluation job's BigQuery table, set this to false.

Returns:

  • (::Boolean) —

    Required. Whether you want Data Labeling Service to provide ground truth labels for prediction input. If you want the service to assign human labelers to annotate your data, set this to true. If you want to provide your own ground truth labels in the evaluation job's BigQuery table, set this to false.



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#model_version ⇒ ::String

Returns Required. The AI Platform Prediction model version to be evaluated. Prediction input and output is sampled from this model version. When creating an evaluation job, specify the model version in the following format:

"projects/{project_id}/models/{model_name}/versions/{version_name}"

There can only be one evaluation job per model version.

Returns:

  • (::String) —

    Required. The AI Platform Prediction model version to be evaluated. Prediction input and output is sampled from this model version. When creating an evaluation job, specify the model version in the following format:

    "projects/{project_id}/models/{model_name}/versions/{version_name}"

    There can only be one evaluation job per model version.



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#name ⇒ ::String

Returns Output only. After you create a job, Data Labeling Service assigns a name to the job with the following format:

"projects/{project_id}/evaluationJobs/{evaluation_job_id}".

Returns:

  • (::String) —

    Output only. After you create a job, Data Labeling Service assigns a name to the job with the following format:

    "projects/{project_id}/evaluationJobs/{evaluation_job_id}"



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#schedule ⇒ ::String

Returns Required. Describes the interval at which the job runs. This interval must be at least 1 day, and it is rounded to the nearest day. For example, if you specify a 50-hour interval, the job runs every 2 days.

You can provide the schedule in crontab format or in an English-like format.

Regardless of what you specify, the job will run at 10:00 AM UTC. Only the interval from this schedule is used, not the specific time of day.

Returns:

  • (::String) —

    Required. Describes the interval at which the job runs. This interval must be at least 1 day, and it is rounded to the nearest day. For example, if you specify a 50-hour interval, the job runs every 2 days.

    You can provide the schedule in crontab format or in an English-like format.

    Regardless of what you specify, the job will run at 10:00 AM UTC. Only the interval from this schedule is used, not the specific time of day.



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end

#state ⇒ ::Google::Cloud::DataLabeling::V1beta1::EvaluationJob::State

Returns Output only. Describes the current state of the job.

Returns:



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# File 'proto_docs/google/cloud/datalabeling/v1beta1/evaluation_job.rb', line 89

class EvaluationJob
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # State of the job.
  module State
    STATE_UNSPECIFIED = 0

    # The job is scheduled to run at the {::Google::Cloud::DataLabeling::V1beta1::EvaluationJob#schedule configured interval}. You
    # can {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#pause_evaluation_job pause} or
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#delete_evaluation_job 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

    # 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
    #    {::Google::Cloud::DataLabeling::V1beta1::Dataset 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 {::Google::Cloud::DataLabeling::V1beta1::Dataset 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.
    #
    # 2. Data Labeling Service creates an {::Google::Cloud::DataLabeling::V1beta1::Evaluation 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

    # The job is not sampling prediction input and output into your BigQuery
    # table and it will not run according to its schedule. You can
    # {::Google::Cloud::DataLabeling::V1beta1::DataLabelingService::Client#resume_evaluation_job resume} the job.
    PAUSED = 3

    # The job has this state right before it is deleted.
    STOPPED = 4
  end
end