Interface CustomJobSpecOrBuilder (3.34.0)

public interface CustomJobSpecOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getBaseOutputDirectory()

public abstract GcsDestination getBaseOutputDirectory()

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 = <base_output_directory>/model/
AIP_CHECKPOINT_DIR = <base_output_directory>/checkpoints/
AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/logs/

For CustomJob backing a Trial of HyperparameterTuningJob:
AIP_MODEL_DIR = <base_output_directory>/<trial_id>/model/
AIP_CHECKPOINT_DIR = <base_output_directory>/<trial_id>/checkpoints/
AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/<trial_id>/logs/

.google.cloud.aiplatform.v1beta1.GcsDestination base_output_directory = 6;

Returns
Type	Description
`GcsDestination`	The baseOutputDirectory.

getBaseOutputDirectoryOrBuilder()

public abstract GcsDestinationOrBuilder getBaseOutputDirectoryOrBuilder()

The following Vertex AI environment variables will be passed to containers or python modules when this field is set:

For CustomJob:

AIP_MODEL_DIR = <base_output_directory>/model/
AIP_CHECKPOINT_DIR = <base_output_directory>/checkpoints/
AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/logs/

For CustomJob backing a Trial of HyperparameterTuningJob:
AIP_MODEL_DIR = <base_output_directory>/<trial_id>/model/
AIP_CHECKPOINT_DIR = <base_output_directory>/<trial_id>/checkpoints/
AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/<trial_id>/logs/

.google.cloud.aiplatform.v1beta1.GcsDestination base_output_directory = 6;

Returns
Type	Description
`GcsDestinationOrBuilder`

getEnableDashboardAccess()

public abstract boolean getEnableDashboardAccess()

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).

bool enable_dashboard_access = 16 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
`boolean`	The enableDashboardAccess.

getEnableWebAccess()

public abstract boolean getEnableWebAccess()

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).

bool enable_web_access = 10 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
`boolean`	The enableWebAccess.

getExperiment()

public abstract String getExperiment()

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

string experiment = 17 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`String`	The experiment.

getExperimentBytes()

public abstract ByteString getExperimentBytes()

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

string experiment = 17 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`ByteString`	The bytes for experiment.

getExperimentRun()

public abstract String getExperimentRun()

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

string experiment_run = 18 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`String`	The experimentRun.

getExperimentRunBytes()

public abstract ByteString getExperimentRunBytes()

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

string experiment_run = 18 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`ByteString`	The bytes for experimentRun.

getNetwork()

public abstract String getNetwork()

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.

string network = 5 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`String`	The network.

getNetworkBytes()

public abstract ByteString getNetworkBytes()

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.

string network = 5 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`ByteString`	The bytes for network.

getPersistentResourceId()

public abstract String getPersistentResourceId()

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.

string persistent_resource_id = 14 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`String`	The persistentResourceId.

getPersistentResourceIdBytes()

public abstract ByteString getPersistentResourceIdBytes()

Optional. The ID of the PersistentResource in the same Project and Location which to run

string persistent_resource_id = 14 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`ByteString`	The bytes for persistentResourceId.

getProtectedArtifactLocationId()

public abstract String getProtectedArtifactLocationId()

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

string protected_artifact_location_id = 19;

Returns
Type	Description
`String`	The protectedArtifactLocationId.

getProtectedArtifactLocationIdBytes()

public abstract ByteString getProtectedArtifactLocationIdBytes()

string protected_artifact_location_id = 19;

Returns
Type	Description
`ByteString`	The bytes for protectedArtifactLocationId.

getReservedIpRanges(int index)

public abstract String getReservedIpRanges(int index)

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'].

repeated string reserved_ip_ranges = 13 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
`index`	`int` The index of the element to return.

Returns
Type	Description
`String`	The reservedIpRanges at the given index.

getReservedIpRangesBytes(int index)

public abstract ByteString getReservedIpRangesBytes(int index)

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'].

repeated string reserved_ip_ranges = 13 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
`index`	`int` The index of the value to return.

Returns
Type	Description
`ByteString`	The bytes of the reservedIpRanges at the given index.

getReservedIpRangesCount()

public abstract int getReservedIpRangesCount()

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'].

repeated string reserved_ip_ranges = 13 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
`int`	The count of reservedIpRanges.

getReservedIpRangesList()

public abstract List<String> getReservedIpRangesList()

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'].

repeated string reserved_ip_ranges = 13 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
`List<String>`	A list containing the reservedIpRanges.

getScheduling()

public abstract Scheduling getScheduling()

Scheduling options for a CustomJob.

.google.cloud.aiplatform.v1beta1.Scheduling scheduling = 3;

Returns
Type	Description
`Scheduling`	The scheduling.

getSchedulingOrBuilder()

public abstract SchedulingOrBuilder getSchedulingOrBuilder()

Scheduling options for a CustomJob.

.google.cloud.aiplatform.v1beta1.Scheduling scheduling = 3;

Returns
Type	Description
`SchedulingOrBuilder`

getServiceAccount()

public abstract String getServiceAccount()

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.

string service_account = 4;

Returns
Type	Description
`String`	The serviceAccount.

getServiceAccountBytes()

public abstract ByteString getServiceAccountBytes()

string service_account = 4;

Returns
Type	Description
`ByteString`	The bytes for serviceAccount.

getTensorboard()

public abstract String getTensorboard()

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

string tensorboard = 7 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`String`	The tensorboard.

getTensorboardBytes()

public abstract ByteString getTensorboardBytes()

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

string tensorboard = 7 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }

Returns
Type	Description
`ByteString`	The bytes for tensorboard.

getWorkerPoolSpecs(int index)

public abstract WorkerPoolSpec getWorkerPoolSpecs(int index)

Required. 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.