Struct google_api_proto::google::cloud::aiplatform::v1beta1::CustomJobSpec

pub struct CustomJobSpec {

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

Represents the spec of a CustomJob.

Fields

persistent_resource_id: 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.

worker_pool_specs: Vec<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: Option<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: 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’].

base_output_directory: Option<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][google.cloud.aiplatform.v1beta1.Trial.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/

protected_artifact_location_id: 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

tensorboard: String

Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1beta1.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][google.cloud.aiplatform.v1beta1.CustomJob.web_access_uris] or [Trial.web_access_uris][google.cloud.aiplatform.v1beta1.Trial.web_access_uris] (within [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1beta1.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][google.cloud.aiplatform.v1beta1.CustomJob.web_access_uris] or [Trial.web_access_uris][google.cloud.aiplatform.v1beta1.Trial.web_access_uris] (within [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1beta1.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}

models: 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.

Trait Implementations

impl Clone for CustomJobSpec

fn clone(&self) -> CustomJobSpec

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CustomJobSpec

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

Formats the value using the given formatter.

impl Default for CustomJobSpec

fn default() -> Self

Returns the “default value” for a type.

impl Message for CustomJobSpec

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for CustomJobSpec

fn eq(&self, other: &CustomJobSpec) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CustomJobSpec