GoogleCloudAiplatformV1beta1CustomJobSpec class

Represents the spec of a CustomJob.

Constructors

GoogleCloudAiplatformV1beta1CustomJobSpec({Input<GoogleCloudAiplatformV1beta1GcsDestination>? baseOutputDirectory, Input<bool>? enableDashboardAccess, Input<bool>? enableWebAccess, Input<String>? experiment, Input<String>? experimentRun, Input<String>? network, Input<String>? persistentResourceId, Input<String>? protectedArtifactLocationId, Input<List<String>>? reservedIpRanges, Input<GoogleCloudAiplatformV1beta1Scheduling>? scheduling, Input<String>? serviceAccount, Input<String>? tensorboard, required Input<List<GoogleCloudAiplatformV1beta1WorkerPoolSpec>> workerPoolSpecs})
Creates a new GoogleCloudAiplatformV1beta1CustomJobSpec. baseOutputDirectory The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/ enableDashboardAccess Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). enableWebAccess Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). experiment Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name} experimentRun Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name} network Optional. The full name of the Compute Engine network to which the Job 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. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. persistentResourceId Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected. protectedArtifactLocationId The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations reservedIpRanges Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job 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'. scheduling Scheduling options for a CustomJob. serviceAccount Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used. tensorboard Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} workerPoolSpecs The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
const
GoogleCloudAiplatformV1beta1CustomJobSpec.fromMap(Map<String, dynamic> map)
factory

Properties

baseOutputDirectory → Input<GoogleCloudAiplatformV1beta1GcsDestination>?
The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
final
enableDashboardAccess → Input<bool>?
Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
final
enableWebAccess → Input<bool>?
Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
final
experiment → Input<String>?
Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
final
experimentRun → Input<String>?
Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
final
hashCode → int
The hash code for this object.
no setterinherited
network → Input<String>?
Optional. The full name of the Compute Engine network to which the Job 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. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
final
persistentResourceId → Input<String>?
Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.
final
protectedArtifactLocationId → Input<String>?
The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
final
reservedIpRanges → Input<List<String>>?
Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job 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
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
scheduling → Input<GoogleCloudAiplatformV1beta1Scheduling>?
Scheduling options for a CustomJob.
final
serviceAccount → Input<String>?
Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
final
tensorboard → Input<String>?
Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
final
workerPoolSpecs → Input<List<GoogleCloudAiplatformV1beta1WorkerPoolSpec>>
The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
final

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toMap() → Map<String, dynamic>
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited