ScheduleCreatePipelineJobRequestPipelineJob class
Constructors
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ScheduleCreatePipelineJobRequestPipelineJob({Input<String?>? createTime, Input<String?>? displayName, Input<ScheduleCreatePipelineJobRequestPipelineJobEncryptionSpec?>? encryptionSpec, Input<String?>? endTime, Input<Map<String, String>?>? labels, Input<String?>? name, Input<String?>? network, Input<String?>? pipelineSpec, Input<bool?>? preflightValidations, Input<ScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfig?>? pscInterfaceConfig, Input<List<String>?>? reservedIpRanges, Input<ScheduleCreatePipelineJobRequestPipelineJobRuntimeConfig?>? runtimeConfig, Input<String?>? scheduleName, Input<String?>? serviceAccount, Input<String?>? startTime, Input<String?>? state, Input<List<ScheduleCreatePipelineJobRequestPipelineJobTemplateMetadata>?>? templateMetadatas, Input<String?>? templateUri, Input<String?>? updateTime})
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Creates a new ScheduleCreatePipelineJobRequestPipelineJob.
createTime (Output)
displayName The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource.
endTime (Output)
labels The labels with user-defined metadata to organize PipelineJob. 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. Note there is some reserved label key for Vertex AI Pipelines. - vertex-ai-pipelines-run-billing-id, user set value will get overrided.
name (Output)
network The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. Private services access must already be configured for the network. Pipeline job will apply the network configuration to the Google Cloud resources being launched, if applied, such as Vertex AI Training or Dataflow job. If left unspecified, the workload is not peered with any network.
pipelineSpec A compiled definition of a pipeline, represented as a JSON object. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined in Python using the Kubeflow Pipelines SDK.
preflightValidations Whether to do component level validations before job creation.
pscInterfaceConfig Configuration for PSC-I.
reservedIpRanges A list of names for the reserved ip ranges under the VPC network that can be used for this Pipeline Job's workload. If set, we will deploy the Pipeline Job's workload within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: 'vertex-ai-ip-range'.
runtimeConfig The runtime config of a PipelineJob.
scheduleName (Output)
serviceAccount The service account that the pipeline workload runs as. If not specified, the Compute Engine default service account in the project will be used. See https://cloud.google.com/compute/docs/access/service-accounts#default_service_account Users starting the pipeline must have the iam.serviceAccounts.actAs permission on this service account.
startTime (Output)
state (Output)
templateMetadatas (Output)
templateUri A template uri from where the PipelineJob.pipeline_spec, if empty, will be downloaded. Currently, only uri from Vertex Template Registry & Gallery is supported. Reference to https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline-template.
updateTime (Output)
const
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ScheduleCreatePipelineJobRequestPipelineJob.fromMap(Map<String, dynamic> map)
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factory
Properties
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createTime
→ Input<String?>?
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(Output)
Pipeline creation time.
final
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displayName
→ Input<String?>?
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The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
final
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encryptionSpec
→ Input<ScheduleCreatePipelineJobRequestPipelineJobEncryptionSpec?>?
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Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource.
Structure is documented below.
final
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endTime
→ Input<String?>?
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(Output)
Pipeline end time.
final
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hashCode
→ int
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The hash code for this object.
no setterinherited
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labels
→ Input<Map<String, String>?>?
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The labels with user-defined metadata to organize PipelineJob. 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. Note there is some reserved label key for Vertex AI Pipelines. -
vertex-ai-pipelines-run-billing-id, user set value will get overrided.
final
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name
→ Input<String?>?
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(Output)
The resource name of the PipelineJob.
final
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network
→ Input<String?>?
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The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example,
projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. Private services access must already be configured for the network. Pipeline job will apply the network configuration to the Google Cloud resources being launched, if applied, such as Vertex AI Training or Dataflow job. If left unspecified, the workload is not peered with any network.
final
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pipelineSpec
→ Input<String?>?
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A compiled definition of a pipeline, represented as a
JSON object. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined in Python using the Kubeflow Pipelines SDK.
final
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preflightValidations
→ Input<bool?>?
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Whether to do component level validations before job creation.
final
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pscInterfaceConfig
→ Input<ScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfig?>?
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Configuration for PSC-I.
Structure is documented below.
final
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reservedIpRanges
→ Input<List<String>?>?
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A list of names for the reserved ip ranges under the VPC network that can be used for this Pipeline Job's workload. If set, we will deploy the Pipeline Job's workload within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example:
'vertex-ai-ip-range'.
final
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runtimeConfig
→ Input<ScheduleCreatePipelineJobRequestPipelineJobRuntimeConfig?>?
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The runtime config of a PipelineJob.
Structure is documented below.
final
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runtimeType
→ Type
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A representation of the runtime type of the object.
no setterinherited
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scheduleName
→ Input<String?>?
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(Output)
The schedule resource name. Only returned if the Pipeline is created by Schedule API.
final
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serviceAccount
→ Input<String?>?
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The service account that the pipeline workload runs as. If not specified, the Compute Engine default service account in the project will be used. See https://cloud.google.com/compute/docs/access/service-accounts#default_service_account Users starting the pipeline must have the
iam.serviceAccounts.actAs permission on this service account.
final
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startTime
→ Input<String?>?
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(Output)
Pipeline start time.
final
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state
→ Input<String?>?
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(Output)
Possible values: PIPELINE_STATE_QUEUED PIPELINE_STATE_PENDING PIPELINE_STATE_RUNNING PIPELINE_STATE_SUCCEEDED PIPELINE_STATE_FAILED PIPELINE_STATE_CANCELLING PIPELINE_STATE_CANCELLED PIPELINE_STATE_PAUSED
final
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templateMetadatas
→ Input<List<ScheduleCreatePipelineJobRequestPipelineJobTemplateMetadata>?>?
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(Output)
Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry.
Structure is documented below.
final
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templateUri
→ Input<String?>?
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A template uri from where the PipelineJob.pipeline_spec, if empty, will be downloaded. Currently, only uri from Vertex Template Registry & Gallery is supported. Reference to https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline-template.
final
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updateTime
→ Input<String?>?
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(Output)
Timestamp when this PipelineJob was most recently updated.
final
