Google Cloud Ai Platform V1 Client - Class CustomJobSpec (0.18.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class CustomJobSpec.

Represents the spec of a CustomJob.

Generated from protobuf message google.cloud.aiplatform.v1.CustomJobSpec

Namespace

Google \ Cloud \ AIPlatform \ V1

Methods

__construct

Constructor.

Parameters
Name	Description
`data`	`array` Optional. Data for populating the Message object.
`↳ worker_pool_specs`	`array<Google\Cloud\AIPlatform\V1\WorkerPoolSpec>` 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.
`↳ scheduling`	`Google\Cloud\AIPlatform\V1\Scheduling` Scheduling options for a CustomJob.
`↳ service_account`	`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.
`↳ network`	`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.
`↳ reserved_ip_ranges`	`array` 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'].
`↳ base_output_directory`	`Google\Cloud\AIPlatform\V1\GcsDestination` 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/`
`↳ tensorboard`	`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}`
`↳ enable_web_access`	`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).
`↳ enable_dashboard_access`	`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).
`↳ experiment`	`string` Optional. The Experiment associated with this job. Format: `projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}`
`↳ experiment_run`	`string` Optional. The Experiment Run associated with this job. Format: `projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}`

getWorkerPoolSpecs

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.

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setWorkerPoolSpecs

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.

Parameter
Name	Description
`var`	`array<Google\Cloud\AIPlatform\V1\WorkerPoolSpec>`

Returns
Type	Description
`$this`

getScheduling

Scheduling options for a CustomJob.

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\Scheduling\|null`

hasScheduling

clearScheduling

setScheduling

Scheduling options for a CustomJob.

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\Scheduling`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`string`

setServiceAccount

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.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`string`

setNetwork

Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getReservedIpRanges

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'].

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setReservedIpRanges

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'].

Parameter
Name	Description
`var`	`string[]`

Returns
Type	Description
`$this`

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/

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\GcsDestination\|null`

hasBaseOutputDirectory

clearBaseOutputDirectory

setBaseOutputDirectory

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/

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\GcsDestination`

Returns
Type	Description
`$this`

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}

Returns
Type	Description
`string`

setTensorboard

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

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

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).

Returns
Type	Description
`bool`

setEnableWebAccess

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).

Parameter
Name	Description
`var`	`bool`

Returns
Type	Description
`$this`

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).

Returns
Type	Description
`bool`

setEnableDashboardAccess

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).

Parameter
Name	Description
`var`	`bool`

Returns
Type	Description
`$this`

getExperiment

Optional. The Experiment associated with this job.

Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

Returns
Type	Description
`string`

setExperiment

Optional. The Experiment associated with this job.

Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getExperimentRun

Optional. The Experiment Run associated with this job.

Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

Returns
Type	Description
`string`

setExperimentRun

Optional. The Experiment Run associated with this job.

Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`