Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::CustomJobSpec (v0.26.0)

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::CustomJobSpec.

Represents the spec of a CustomJob.

Inherits

Object

Extended By

Google::Protobuf::MessageExts::ClassMethods

Includes

Google::Protobuf::MessageExts

Methods

#base_output_directory

def base_output_directory() -> ::Google::Cloud::AIPlatform::V1::GcsDestination

Returns

(::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/

#base_output_directory=

def base_output_directory=(value) -> ::Google::Cloud::AIPlatform::V1::GcsDestination

Parameter

value (::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/

Returns

(::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/

#enable_dashboard_access

def enable_dashboard_access() -> ::Boolean

Returns

(::Boolean) — 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).

#enable_dashboard_access=

def enable_dashboard_access=(value) -> ::Boolean

Parameter

value (::Boolean) — 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

(::Boolean) — 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).

#enable_web_access

def enable_web_access() -> ::Boolean

Returns

(::Boolean) — 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_web_access=

def enable_web_access=(value) -> ::Boolean

Parameter

value (::Boolean) — 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

(::Boolean) — 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

def experiment() -> ::String

Returns

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

#experiment=

def experiment=(value) -> ::String

Parameter

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

Returns

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

#experiment_run

def experiment_run() -> ::String

Returns

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

#experiment_run=

def experiment_run=(value) -> ::String

Parameter

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

Returns

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

#network

def network() -> ::String

Returns

(::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.

#network=

def network=(value) -> ::String

Parameter

value (::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.

Returns

(::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

def reserved_ip_ranges() -> ::Array<::String>

Returns

(::Array<::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'].

#reserved_ip_ranges=

def reserved_ip_ranges=(value) -> ::Array<::String>

Parameter

value (::Array<::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'].

Returns

(::Array<::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'].

#scheduling

def scheduling() -> ::Google::Cloud::AIPlatform::V1::Scheduling

Returns

(::Google::Cloud::AIPlatform::V1::Scheduling) — Scheduling options for a CustomJob.

#scheduling=

def scheduling=(value) -> ::Google::Cloud::AIPlatform::V1::Scheduling

Parameter

value (::Google::Cloud::AIPlatform::V1::Scheduling) — Scheduling options for a CustomJob.

Returns

(::Google::Cloud::AIPlatform::V1::Scheduling) — Scheduling options for a CustomJob.

#service_account

def service_account() -> ::String

Returns

(::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.

#service_account=

def service_account=(value) -> ::String

Parameter

value (::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.

Returns

(::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.

#tensorboard

def tensorboard() -> ::String

Returns

(::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}

#tensorboard=

def tensorboard=(value) -> ::String

Parameter

value (::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}

Returns

(::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}

#worker_pool_specs

def worker_pool_specs() -> ::Array<::Google::Cloud::AIPlatform::V1::WorkerPoolSpec>

Returns

(::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.

#worker_pool_specs=

def worker_pool_specs=(value) -> ::Array<::Google::Cloud::AIPlatform::V1::WorkerPoolSpec>

Parameter

value (::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.

Returns

(::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.