cancel(name, body=None, x__xgafv=None)

Cancels a DataLabelingJob. Success of cancellation is not guaranteed.

Args:
  name: string, Required. The name of the DataLabelingJob. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for JobService.CancelDataLabelingJob.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
}

close()

Close httplib2 connections.

create(parent, body=None, x__xgafv=None)

Creates a DataLabelingJob.

Args:
parent: string, Required. The parent of the DataLabelingJob. Format: `projects/{project}/locations/{location}` (required)
body: object, The request body.
The object takes the form of:

{ # DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:
"activeLearningConfig": { # Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy. # Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
"maxDataItemCount": "A String", # Max number of human labeled DataItems.
"maxDataItemPercentage": 42, # Max percent of total DataItems for human labeling.
"sampleConfig": { # Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy. # Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
"followingBatchSamplePercentage": 42, # The percentage of data needed to be labeled in each following batch (except the first batch).
"initialBatchSamplePercentage": 42, # The percentage of data needed to be labeled in the first batch.
"sampleStrategy": "A String", # Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
},
"trainingConfig": { # CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems. # CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
"timeoutTrainingMilliHours": "A String", # The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
},
},
"annotationLabels": { # Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
"a_key": "A String",
},
"createTime": "A String", # Output only. Timestamp when this DataLabelingJob was created.
"currentSpend": { # Represents an amount of money with its currency type. # Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
"currencyCode": "A String", # The three-letter currency code defined in ISO 4217.
"nanos": 42, # Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If `units` is positive, `nanos` must be positive or zero. If `units` is zero, `nanos` can be positive, zero, or negative. If `units` is negative, `nanos` must be negative or zero. For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.
"units": "A String", # The whole units of the amount. For example if `currencyCode` is `"USD"`, then 1 unit is one US dollar.
},
"datasets": [ # Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
"A String",
],
"displayName": "A String", # Required. The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
"kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
},
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. DataLabelingJob errors. It is only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"inputs": "", # Required. Input config parameters for the DataLabelingJob.
"inputsSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
"instructionUri": "A String", # Required. The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
"labelerCount": 42, # Required. Number of labelers to work on each DataItem.
"labelingProgress": 42, # Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
"labels": { # The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
"a_key": "A String",
},
"name": "A String", # Output only. Resource name of the DataLabelingJob.
"specialistPools": [ # The SpecialistPools' resource names associated with this job.
"A String",
],
"state": "A String", # Output only. The detailed state of the job.
"updateTime": "A String", # Output only. Timestamp when this DataLabelingJob was updated most recently.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:
"activeLearningConfig": { # Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy. # Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
"maxDataItemCount": "A String", # Max number of human labeled DataItems.
"maxDataItemPercentage": 42, # Max percent of total DataItems for human labeling.
"sampleConfig": { # Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy. # Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
"followingBatchSamplePercentage": 42, # The percentage of data needed to be labeled in each following batch (except the first batch).
"initialBatchSamplePercentage": 42, # The percentage of data needed to be labeled in the first batch.
"sampleStrategy": "A String", # Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
},
"trainingConfig": { # CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems. # CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
"timeoutTrainingMilliHours": "A String", # The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
},
},
"annotationLabels": { # Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
"a_key": "A String",
},
"createTime": "A String", # Output only. Timestamp when this DataLabelingJob was created.
"currentSpend": { # Represents an amount of money with its currency type. # Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
"currencyCode": "A String", # The three-letter currency code defined in ISO 4217.
"nanos": 42, # Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If `units` is positive, `nanos` must be positive or zero. If `units` is zero, `nanos` can be positive, zero, or negative. If `units` is negative, `nanos` must be negative or zero. For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.
"units": "A String", # The whole units of the amount. For example if `currencyCode` is `"USD"`, then 1 unit is one US dollar.
},
"datasets": [ # Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
"A String",
],
"displayName": "A String", # Required. The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
"kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
},
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. DataLabelingJob errors. It is only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"inputs": "", # Required. Input config parameters for the DataLabelingJob.
"inputsSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
"instructionUri": "A String", # Required. The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
"labelerCount": 42, # Required. Number of labelers to work on each DataItem.
"labelingProgress": 42, # Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
"labels": { # The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
"a_key": "A String",
},
"name": "A String", # Output only. Resource name of the DataLabelingJob.
"specialistPools": [ # The SpecialistPools' resource names associated with this job.
"A String",
],
"state": "A String", # Output only. The detailed state of the job.
"updateTime": "A String", # Output only. Timestamp when this DataLabelingJob was updated most recently.
}

delete(name, x__xgafv=None)

Deletes a DataLabelingJob.

Args:
name: string, Required. The name of the DataLabelingJob to be deleted. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

get(name, x__xgafv=None)

Gets a DataLabelingJob.

Args:
name: string, Required. The name of the DataLabelingJob. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:
"activeLearningConfig": { # Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy. # Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
"maxDataItemCount": "A String", # Max number of human labeled DataItems.
"maxDataItemPercentage": 42, # Max percent of total DataItems for human labeling.
"sampleConfig": { # Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy. # Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
"followingBatchSamplePercentage": 42, # The percentage of data needed to be labeled in each following batch (except the first batch).
"initialBatchSamplePercentage": 42, # The percentage of data needed to be labeled in the first batch.
"sampleStrategy": "A String", # Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
},
"trainingConfig": { # CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems. # CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
"timeoutTrainingMilliHours": "A String", # The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
},
},
"annotationLabels": { # Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
"a_key": "A String",
},
"createTime": "A String", # Output only. Timestamp when this DataLabelingJob was created.
"currentSpend": { # Represents an amount of money with its currency type. # Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
"currencyCode": "A String", # The three-letter currency code defined in ISO 4217.
"nanos": 42, # Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If `units` is positive, `nanos` must be positive or zero. If `units` is zero, `nanos` can be positive, zero, or negative. If `units` is negative, `nanos` must be negative or zero. For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.
"units": "A String", # The whole units of the amount. For example if `currencyCode` is `"USD"`, then 1 unit is one US dollar.
},
"datasets": [ # Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
"A String",
],
"displayName": "A String", # Required. The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
"kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
},
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. DataLabelingJob errors. It is only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"inputs": "", # Required. Input config parameters for the DataLabelingJob.
"inputsSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
"instructionUri": "A String", # Required. The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
"labelerCount": 42, # Required. Number of labelers to work on each DataItem.
"labelingProgress": 42, # Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
"labels": { # The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
"a_key": "A String",
},
"name": "A String", # Output only. Resource name of the DataLabelingJob.
"specialistPools": [ # The SpecialistPools' resource names associated with this job.
"A String",
],
"state": "A String", # Output only. The detailed state of the job.
"updateTime": "A String", # Output only. Timestamp when this DataLabelingJob was updated most recently.
}

list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, readMask=None, x__xgafv=None)

Lists DataLabelingJobs in a Location.

Args:
parent: string, Required. The parent of the DataLabelingJob. Format: `projects/{project}/locations/{location}` (required)
filter: string, The standard list filter. Supported fields: * `display_name` supports `=`, `!=` comparisons, and `:` wildcard. * `state` supports `=`, `!=` comparisons. * `create_time` supports `=`, `!=`,`<`, `<=`,`>`, `>=` comparisons. `create_time` must be in RFC 3339 format. * `labels` supports general map functions that is: `labels.key=value` - key:value equality `labels.key:* - key existence Some examples of using the filter are: * `state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"` * `state!="JOB_STATE_FAILED" OR display_name="my_job"` * `NOT display_name="my_job"` * `create_time>"2021-05-18T00:00:00Z"` * `labels.keyA=valueA` * `labels.keyB:*`
orderBy: string, A comma-separated list of fields to order by, sorted in ascending order by default. Use `desc` after a field name for descending.
pageSize: integer, The standard list page size.
pageToken: string, The standard list page token.
readMask: string, Mask specifying which fields to read. FieldMask represents a set of symbolic field paths. For example, the mask can be `paths: "name"`. The "name" here is a field in DataLabelingJob. If this field is not set, all fields of the DataLabelingJob are returned.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Response message for JobService.ListDataLabelingJobs.
"dataLabelingJobs": [ # A list of DataLabelingJobs that matches the specified filter in the request.
{ # DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:
"activeLearningConfig": { # Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy. # Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
"maxDataItemCount": "A String", # Max number of human labeled DataItems.
"maxDataItemPercentage": 42, # Max percent of total DataItems for human labeling.
"sampleConfig": { # Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy. # Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
"followingBatchSamplePercentage": 42, # The percentage of data needed to be labeled in each following batch (except the first batch).
"initialBatchSamplePercentage": 42, # The percentage of data needed to be labeled in the first batch.
"sampleStrategy": "A String", # Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
},
"trainingConfig": { # CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems. # CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
"timeoutTrainingMilliHours": "A String", # The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
},
},
"annotationLabels": { # Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
"a_key": "A String",
},
"createTime": "A String", # Output only. Timestamp when this DataLabelingJob was created.
"currentSpend": { # Represents an amount of money with its currency type. # Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
"currencyCode": "A String", # The three-letter currency code defined in ISO 4217.
"nanos": 42, # Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If `units` is positive, `nanos` must be positive or zero. If `units` is zero, `nanos` can be positive, zero, or negative. If `units` is negative, `nanos` must be negative or zero. For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.
"units": "A String", # The whole units of the amount. For example if `currencyCode` is `"USD"`, then 1 unit is one US dollar.
},
"datasets": [ # Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
"A String",
],
"displayName": "A String", # Required. The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
"kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
},
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. DataLabelingJob errors. It is only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"inputs": "", # Required. Input config parameters for the DataLabelingJob.
"inputsSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
"instructionUri": "A String", # Required. The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
"labelerCount": 42, # Required. Number of labelers to work on each DataItem.
"labelingProgress": 42, # Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
"labels": { # The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
"a_key": "A String",
},
"name": "A String", # Output only. Resource name of the DataLabelingJob.
"specialistPools": [ # The SpecialistPools' resource names associated with this job.
"A String",
],
"state": "A String", # Output only. The detailed state of the job.
"updateTime": "A String", # Output only. Timestamp when this DataLabelingJob was updated most recently.
},
],
"nextPageToken": "A String", # The standard List next-page token.
}

list_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.

Vertex AI API . projects . locations . dataLabelingJobs

Instance Methods

Method Details