Struct GoogleCloudAiplatformV1CustomJobSpec

pub struct GoogleCloudAiplatformV1CustomJobSpec {

    pub worker_pool_specs: Option<Vec<GoogleCloudAiplatformV1WorkerPoolSpec>>,
    pub scheduling: Option<GoogleCloudAiplatformV1Scheduling>,
    pub tensorboard: Option<String>,
    pub experiment: Option<String>,
    pub experiment_run: Option<String>,
    pub protected_artifact_location_id: Option<String>,
    pub reserved_ip_ranges: Option<Vec<String>>,
    pub service_account: Option<String>,
    pub persistent_resource_id: Option<String>,
    pub network: Option<String>,
    pub enable_dashboard_access: Option<bool>,
    pub base_output_directory: Option<GoogleCloudAiplatformV1GcsDestination>,
    pub models: Option<Vec<String>>,
    pub enable_web_access: Option<bool>,
}

Represents the spec of a CustomJob.

This type is not used in any activity, and only used as part of another schema.

Fields

worker_pool_specs: Option<Vec<GoogleCloudAiplatformV1WorkerPoolSpec>>

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: Option<GoogleCloudAiplatformV1Scheduling>

Scheduling options for a CustomJob.

tensorboard: Option<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}

experiment: Option<String>

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

experiment_run: Option<String>

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

protected_artifact_location_id: Option<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

reserved_ip_ranges: Option<Vec<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’].

service_account: Option<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.

persistent_resource_id: Option<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.

network: Option<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.

enable_dashboard_access: Option<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).

base_output_directory: Option<GoogleCloudAiplatformV1GcsDestination>

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/

models: Option<Vec<String>>

Optional. The name of the Model resources for which to generate a mapping to artifact URIs. Applicable only to some of the Google-provided custom jobs. Format: projects/{project}/locations/{location}/models/{model} In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden If no version ID or alias is specified, the “default” version will be returned. The “default” version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version.

enable_web_access: Option<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).

Trait Implementations

impl Clone for GoogleCloudAiplatformV1CustomJobSpec

fn clone(&self) -> GoogleCloudAiplatformV1CustomJobSpec

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GoogleCloudAiplatformV1CustomJobSpec

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for GoogleCloudAiplatformV1CustomJobSpec

fn default() -> GoogleCloudAiplatformV1CustomJobSpec

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GoogleCloudAiplatformV1CustomJobSpec

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GoogleCloudAiplatformV1CustomJobSpec

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Part for GoogleCloudAiplatformV1CustomJobSpec