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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
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google-native.aiplatform/v1beta1.getHyperparameterTuningJob

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native

    Gets a HyperparameterTuningJob

    Using getHyperparameterTuningJob

    Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

    function getHyperparameterTuningJob(args: GetHyperparameterTuningJobArgs, opts?: InvokeOptions): Promise<GetHyperparameterTuningJobResult>
function getHyperparameterTuningJobOutput(args: GetHyperparameterTuningJobOutputArgs, opts?: InvokeOptions): Output<GetHyperparameterTuningJobResult>
    def get_hyperparameter_tuning_job(hyperparameter_tuning_job_id: Optional[str] = None,
                                  location: Optional[str] = None,
                                  project: Optional[str] = None,
                                  opts: Optional[InvokeOptions] = None) -> GetHyperparameterTuningJobResult
def get_hyperparameter_tuning_job_output(hyperparameter_tuning_job_id: Optional[pulumi.Input[str]] = None,
                                  location: Optional[pulumi.Input[str]] = None,
                                  project: Optional[pulumi.Input[str]] = None,
                                  opts: Optional[InvokeOptions] = None) -> Output[GetHyperparameterTuningJobResult]
    func LookupHyperparameterTuningJob(ctx *Context, args *LookupHyperparameterTuningJobArgs, opts ...InvokeOption) (*LookupHyperparameterTuningJobResult, error)
func LookupHyperparameterTuningJobOutput(ctx *Context, args *LookupHyperparameterTuningJobOutputArgs, opts ...InvokeOption) LookupHyperparameterTuningJobResultOutput

    > Note: This function is named LookupHyperparameterTuningJob in the Go SDK.

    public static class GetHyperparameterTuningJob 
{
    public static Task<GetHyperparameterTuningJobResult> InvokeAsync(GetHyperparameterTuningJobArgs args, InvokeOptions? opts = null)
    public static Output<GetHyperparameterTuningJobResult> Invoke(GetHyperparameterTuningJobInvokeArgs args, InvokeOptions? opts = null)
}
    public static CompletableFuture<GetHyperparameterTuningJobResult> getHyperparameterTuningJob(GetHyperparameterTuningJobArgs args, InvokeOptions options)
public static Output<GetHyperparameterTuningJobResult> getHyperparameterTuningJob(GetHyperparameterTuningJobArgs args, InvokeOptions options)

    fn::invoke:
  function: google-native:aiplatform/v1beta1:getHyperparameterTuningJob
  arguments:
    # arguments dictionary

    The following arguments are supported:

    getHyperparameterTuningJob Result

    The following output properties are available:

    CreateTime string
    Time when the HyperparameterTuningJob was created.
    DisplayName string
    The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
    EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1EncryptionSpecResponse
    Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
    EndTime string
    Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    Error Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse
    Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    Labels Dictionary<string, string>
    The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    MaxFailedTrialCount int
    The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
    MaxTrialCount int
    The desired total number of Trials.
    Name string
    Resource name of the HyperparameterTuningJob.
    ParallelTrialCount int
    The desired number of Trials to run in parallel.
    StartTime string
    Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
    State string
    The detailed state of the job.
    StudySpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1StudySpecResponse
    Study configuration of the HyperparameterTuningJob.
    TrialJobSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1CustomJobSpecResponse
    The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
    Trials List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1TrialResponse>
    Trials of the HyperparameterTuningJob.
    UpdateTime string
    Time when the HyperparameterTuningJob was most recently updated.
    CreateTime string
    Time when the HyperparameterTuningJob was created.
    DisplayName string
    The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
    EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse
    Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
    EndTime string
    Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    Error GoogleRpcStatusResponse
    Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    Labels map[string]string
    The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    MaxFailedTrialCount int
    The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
    MaxTrialCount int
    The desired total number of Trials.
    Name string
    Resource name of the HyperparameterTuningJob.
    ParallelTrialCount int
    The desired number of Trials to run in parallel.
    StartTime string
    Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
    State string
    The detailed state of the job.
    StudySpec GoogleCloudAiplatformV1beta1StudySpecResponse
    Study configuration of the HyperparameterTuningJob.
    TrialJobSpec GoogleCloudAiplatformV1beta1CustomJobSpecResponse
    The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
    Trials []GoogleCloudAiplatformV1beta1TrialResponse
    Trials of the HyperparameterTuningJob.
    UpdateTime string
    Time when the HyperparameterTuningJob was most recently updated.
    createTime String
    Time when the HyperparameterTuningJob was created.
    displayName String
    The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
    encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse
    Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
    endTime String
    Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    error GoogleRpcStatusResponse
    Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    labels Map<String,String>
    The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    maxFailedTrialCount Integer
    The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
    maxTrialCount Integer
    The desired total number of Trials.
    name String
    Resource name of the HyperparameterTuningJob.
    parallelTrialCount Integer
    The desired number of Trials to run in parallel.
    startTime String
    Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
    state String
    The detailed state of the job.
    studySpec GoogleCloudAiplatformV1beta1StudySpecResponse
    Study configuration of the HyperparameterTuningJob.
    trialJobSpec GoogleCloudAiplatformV1beta1CustomJobSpecResponse
    The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
    trials List<GoogleCloudAiplatformV1beta1TrialResponse>
    Trials of the HyperparameterTuningJob.
    updateTime String
    Time when the HyperparameterTuningJob was most recently updated.
    createTime string
    Time when the HyperparameterTuningJob was created.
    displayName string
    The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
    encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse
    Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
    endTime string
    Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    error GoogleRpcStatusResponse
    Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    labels {[key: string]: string}
    The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    maxFailedTrialCount number
    The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
    maxTrialCount number
    The desired total number of Trials.
    name string
    Resource name of the HyperparameterTuningJob.
    parallelTrialCount number
    The desired number of Trials to run in parallel.
    startTime string
    Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
    state string
    The detailed state of the job.
    studySpec GoogleCloudAiplatformV1beta1StudySpecResponse
    Study configuration of the HyperparameterTuningJob.
    trialJobSpec GoogleCloudAiplatformV1beta1CustomJobSpecResponse
    The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
    trials GoogleCloudAiplatformV1beta1TrialResponse[]
    Trials of the HyperparameterTuningJob.
    updateTime string
    Time when the HyperparameterTuningJob was most recently updated.
    create_time str
    Time when the HyperparameterTuningJob was created.
    display_name str
    The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
    encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpecResponse
    Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
    end_time str
    Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    error GoogleRpcStatusResponse
    Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    labels Mapping[str, str]
    The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    max_failed_trial_count int
    The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
    max_trial_count int
    The desired total number of Trials.
    name str
    Resource name of the HyperparameterTuningJob.
    parallel_trial_count int
    The desired number of Trials to run in parallel.
    start_time str
    Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
    state str
    The detailed state of the job.
    study_spec GoogleCloudAiplatformV1beta1StudySpecResponse
    Study configuration of the HyperparameterTuningJob.
    trial_job_spec GoogleCloudAiplatformV1beta1CustomJobSpecResponse
    The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
    trials Sequence[GoogleCloudAiplatformV1beta1TrialResponse]
    Trials of the HyperparameterTuningJob.
    update_time str
    Time when the HyperparameterTuningJob was most recently updated.
    createTime String
    Time when the HyperparameterTuningJob was created.
    displayName String
    The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
    encryptionSpec Property Map
    Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
    endTime String
    Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    error Property Map
    Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    labels Map<String>
    The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    maxFailedTrialCount Number
    The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
    maxTrialCount Number
    The desired total number of Trials.
    name String
    Resource name of the HyperparameterTuningJob.
    parallelTrialCount Number
    The desired number of Trials to run in parallel.
    startTime String
    Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
    state String
    The detailed state of the job.
    studySpec Property Map
    Study configuration of the HyperparameterTuningJob.
    trialJobSpec Property Map
    The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
    trials List<Property Map>
    Trials of the HyperparameterTuningJob.
    updateTime String
    Time when the HyperparameterTuningJob was most recently updated.

    Supporting Types

    GoogleCloudAiplatformV1beta1ContainerSpecResponse

    Args List<string>
    The arguments to be passed when starting the container.
    Command List<string>
    The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
    Env List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVarResponse>
    Environment variables to be passed to the container. Maximum limit is 100.
    ImageUri string
    The URI of a container image in the Container Registry that is to be run on each worker replica.
    Args []string
    The arguments to be passed when starting the container.
    Command []string
    The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
    Env []GoogleCloudAiplatformV1beta1EnvVarResponse
    Environment variables to be passed to the container. Maximum limit is 100.
    ImageUri string
    The URI of a container image in the Container Registry that is to be run on each worker replica.
    args List<String>
    The arguments to be passed when starting the container.
    command List<String>
    The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
    env List<GoogleCloudAiplatformV1beta1EnvVarResponse>
    Environment variables to be passed to the container. Maximum limit is 100.
    imageUri String
    The URI of a container image in the Container Registry that is to be run on each worker replica.
    args string[]
    The arguments to be passed when starting the container.
    command string[]
    The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
    env GoogleCloudAiplatformV1beta1EnvVarResponse[]
    Environment variables to be passed to the container. Maximum limit is 100.
    imageUri string
    The URI of a container image in the Container Registry that is to be run on each worker replica.
    args Sequence[str]
    The arguments to be passed when starting the container.
    command Sequence[str]
    The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
    env Sequence[GoogleCloudAiplatformV1beta1EnvVarResponse]
    Environment variables to be passed to the container. Maximum limit is 100.
    image_uri str
    The URI of a container image in the Container Registry that is to be run on each worker replica.
    args List<String>
    The arguments to be passed when starting the container.
    command List<String>
    The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
    env List<Property Map>
    Environment variables to be passed to the container. Maximum limit is 100.
    imageUri String
    The URI of a container image in the Container Registry that is to be run on each worker replica.

    GoogleCloudAiplatformV1beta1CustomJobSpecResponse

    BaseOutputDirectory Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse
    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/
    EnableDashboardAccess 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).
    EnableWebAccess 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).
    Experiment string
    Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
    ExperimentRun string
    Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
    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.
    PersistentResourceId 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.
    ProtectedArtifactLocationId 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
    ReservedIpRanges List<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 Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SchedulingResponse
    Scheduling options for a CustomJob.
    ServiceAccount 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 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}
    WorkerPoolSpecs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse>
    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.
    BaseOutputDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse
    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/
    EnableDashboardAccess 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).
    EnableWebAccess 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).
    Experiment string
    Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
    ExperimentRun string
    Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
    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.
    PersistentResourceId 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.
    ProtectedArtifactLocationId 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
    ReservedIpRanges []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 GoogleCloudAiplatformV1beta1SchedulingResponse
    Scheduling options for a CustomJob.
    ServiceAccount 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 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}
    WorkerPoolSpecs []GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse
    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.
    baseOutputDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse
    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/
    enableDashboardAccess 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).
    enableWebAccess 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 String
    Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
    experimentRun String
    Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
    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.
    persistentResourceId 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.
    protectedArtifactLocationId 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
    reservedIpRanges List<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 GoogleCloudAiplatformV1beta1SchedulingResponse
    Scheduling options for a CustomJob.
    serviceAccount 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 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}
    workerPoolSpecs List<GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse>
    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.
    baseOutputDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse
    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/
    enableDashboardAccess 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).
    enableWebAccess 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 string
    Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
    experimentRun string
    Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
    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.
    persistentResourceId 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.
    protectedArtifactLocationId 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
    reservedIpRanges 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 GoogleCloudAiplatformV1beta1SchedulingResponse
    Scheduling options for a CustomJob.
    serviceAccount 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 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}
    workerPoolSpecs GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse[]
    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.
    base_output_directory GoogleCloudAiplatformV1beta1GcsDestinationResponse
    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/
    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).
    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).
    experiment str
    Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
    experiment_run str
    Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
    network str
    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.
    persistent_resource_id str
    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.
    protected_artifact_location_id str
    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 Sequence[str]
    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 GoogleCloudAiplatformV1beta1SchedulingResponse
    Scheduling options for a CustomJob.
    service_account str
    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 str
    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 Sequence[GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse]
    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.
    baseOutputDirectory Property Map
    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/
    enableDashboardAccess 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).
    enableWebAccess 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 String
    Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
    experimentRun String
    Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
    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.
    persistentResourceId 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.
    protectedArtifactLocationId 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
    reservedIpRanges List<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 Property Map
    Scheduling options for a CustomJob.
    serviceAccount 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 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}
    workerPoolSpecs List<Property Map>
    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.

    GoogleCloudAiplatformV1beta1DiskSpecResponse

    BootDiskSizeGb int
    Size in GB of the boot disk (default is 100GB).
    BootDiskType string
    Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
    BootDiskSizeGb int
    Size in GB of the boot disk (default is 100GB).
    BootDiskType string
    Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
    bootDiskSizeGb Integer
    Size in GB of the boot disk (default is 100GB).
    bootDiskType String
    Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
    bootDiskSizeGb number
    Size in GB of the boot disk (default is 100GB).
    bootDiskType string
    Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
    boot_disk_size_gb int
    Size in GB of the boot disk (default is 100GB).
    boot_disk_type str
    Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
    bootDiskSizeGb Number
    Size in GB of the boot disk (default is 100GB).
    bootDiskType String
    Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).

    GoogleCloudAiplatformV1beta1EncryptionSpecResponse

    KmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    KmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName String
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kms_key_name str
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName String
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    GoogleCloudAiplatformV1beta1EnvVarResponse

    Name string
    Name of the environment variable. Must be a valid C identifier.
    Value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    Name string
    Name of the environment variable. Must be a valid C identifier.
    Value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name String
    Name of the environment variable. Must be a valid C identifier.
    value String
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name string
    Name of the environment variable. Must be a valid C identifier.
    value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name str
    Name of the environment variable. Must be a valid C identifier.
    value str
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name String
    Name of the environment variable. Must be a valid C identifier.
    value String
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

    GoogleCloudAiplatformV1beta1GcsDestinationResponse

    OutputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    OutputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix String
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    output_uri_prefix str
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix String
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

    GoogleCloudAiplatformV1beta1MachineSpecResponse

    AcceleratorCount int
    The number of accelerators to attach to the machine.
    AcceleratorType string
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    MachineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    TpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    AcceleratorCount int
    The number of accelerators to attach to the machine.
    AcceleratorType string
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    MachineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    TpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount Integer
    The number of accelerators to attach to the machine.
    acceleratorType String
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType String
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology String
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount number
    The number of accelerators to attach to the machine.
    acceleratorType string
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    accelerator_count int
    The number of accelerators to attach to the machine.
    accelerator_type str
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machine_type str
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpu_topology str
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount Number
    The number of accelerators to attach to the machine.
    acceleratorType String
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType String
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology String
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

    GoogleCloudAiplatformV1beta1MeasurementMetricResponse

    MetricId string
    The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
    Value double
    The value for this metric.
    MetricId string
    The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
    Value float64
    The value for this metric.
    metricId String
    The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
    value Double
    The value for this metric.
    metricId string
    The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
    value number
    The value for this metric.
    metric_id str
    The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
    value float
    The value for this metric.
    metricId String
    The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
    value Number
    The value for this metric.

    GoogleCloudAiplatformV1beta1MeasurementResponse

    ElapsedDuration string
    Time that the Trial has been running at the point of this Measurement.
    Metrics List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MeasurementMetricResponse>
    A list of metrics got by evaluating the objective functions using suggested Parameter values.
    StepCount string
    The number of steps the machine learning model has been trained for. Must be non-negative.
    ElapsedDuration string
    Time that the Trial has been running at the point of this Measurement.
    Metrics []GoogleCloudAiplatformV1beta1MeasurementMetricResponse
    A list of metrics got by evaluating the objective functions using suggested Parameter values.
    StepCount string
    The number of steps the machine learning model has been trained for. Must be non-negative.
    elapsedDuration String
    Time that the Trial has been running at the point of this Measurement.
    metrics List<GoogleCloudAiplatformV1beta1MeasurementMetricResponse>
    A list of metrics got by evaluating the objective functions using suggested Parameter values.
    stepCount String
    The number of steps the machine learning model has been trained for. Must be non-negative.
    elapsedDuration string
    Time that the Trial has been running at the point of this Measurement.
    metrics GoogleCloudAiplatformV1beta1MeasurementMetricResponse[]
    A list of metrics got by evaluating the objective functions using suggested Parameter values.
    stepCount string
    The number of steps the machine learning model has been trained for. Must be non-negative.
    elapsed_duration str
    Time that the Trial has been running at the point of this Measurement.
    metrics Sequence[GoogleCloudAiplatformV1beta1MeasurementMetricResponse]
    A list of metrics got by evaluating the objective functions using suggested Parameter values.
    step_count str
    The number of steps the machine learning model has been trained for. Must be non-negative.
    elapsedDuration String
    Time that the Trial has been running at the point of this Measurement.
    metrics List<Property Map>
    A list of metrics got by evaluating the objective functions using suggested Parameter values.
    stepCount String
    The number of steps the machine learning model has been trained for. Must be non-negative.

    GoogleCloudAiplatformV1beta1NfsMountResponse

    MountPoint string
    Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
    Path string
    Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path
    Server string
    IP address of the NFS server.
    MountPoint string
    Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
    Path string
    Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path
    Server string
    IP address of the NFS server.
    mountPoint String
    Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
    path String
    Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path
    server String
    IP address of the NFS server.
    mountPoint string
    Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
    path string
    Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path
    server string
    IP address of the NFS server.
    mount_point str
    Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
    path str
    Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path
    server str
    IP address of the NFS server.
    mountPoint String
    Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
    path String
    Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path
    server String
    IP address of the NFS server.

    GoogleCloudAiplatformV1beta1PythonPackageSpecResponse

    Args List<string>
    Command line arguments to be passed to the Python task.
    Env List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVarResponse>
    Environment variables to be passed to the python module. Maximum limit is 100.
    ExecutorImageUri string
    The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
    PackageUris List<string>
    The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
    PythonModule string
    The Python module name to run after installing the packages.
    Args []string
    Command line arguments to be passed to the Python task.
    Env []GoogleCloudAiplatformV1beta1EnvVarResponse
    Environment variables to be passed to the python module. Maximum limit is 100.
    ExecutorImageUri string
    The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
    PackageUris []string
    The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
    PythonModule string
    The Python module name to run after installing the packages.
    args List<String>
    Command line arguments to be passed to the Python task.
    env List<GoogleCloudAiplatformV1beta1EnvVarResponse>
    Environment variables to be passed to the python module. Maximum limit is 100.
    executorImageUri String
    The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
    packageUris List<String>
    The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
    pythonModule String
    The Python module name to run after installing the packages.
    args string[]
    Command line arguments to be passed to the Python task.
    env GoogleCloudAiplatformV1beta1EnvVarResponse[]
    Environment variables to be passed to the python module. Maximum limit is 100.
    executorImageUri string
    The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
    packageUris string[]
    The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
    pythonModule string
    The Python module name to run after installing the packages.
    args Sequence[str]
    Command line arguments to be passed to the Python task.
    env Sequence[GoogleCloudAiplatformV1beta1EnvVarResponse]
    Environment variables to be passed to the python module. Maximum limit is 100.
    executor_image_uri str
    The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
    package_uris Sequence[str]
    The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
    python_module str
    The Python module name to run after installing the packages.
    args List<String>
    Command line arguments to be passed to the Python task.
    env List<Property Map>
    Environment variables to be passed to the python module. Maximum limit is 100.
    executorImageUri String
    The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
    packageUris List<String>
    The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
    pythonModule String
    The Python module name to run after installing the packages.

    GoogleCloudAiplatformV1beta1SchedulingResponse

    DisableRetries bool
    Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
    RestartJobOnWorkerRestart bool
    Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
    Timeout string
    The maximum job running time. The default is 7 days.
    DisableRetries bool
    Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
    RestartJobOnWorkerRestart bool
    Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
    Timeout string
    The maximum job running time. The default is 7 days.
    disableRetries Boolean
    Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
    restartJobOnWorkerRestart Boolean
    Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
    timeout String
    The maximum job running time. The default is 7 days.
    disableRetries boolean
    Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
    restartJobOnWorkerRestart boolean
    Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
    timeout string
    The maximum job running time. The default is 7 days.
    disable_retries bool
    Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
    restart_job_on_worker_restart bool
    Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
    timeout str
    The maximum job running time. The default is 7 days.
    disableRetries Boolean
    Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
    restartJobOnWorkerRestart Boolean
    Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
    timeout String
    The maximum job running time. The default is 7 days.

    GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse

    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    MinStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    UpdateAllStoppedTrials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    UseElapsedDuration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    MinStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    UpdateAllStoppedTrials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    UseElapsedDuration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount String
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials Boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learning_rate_parameter_name str
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    max_step_count str
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    min_measurement_count str
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    min_step_count str
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    update_all_stopped_trials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    use_elapsed_duration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount String
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials Boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

    GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse

    AutoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    UseSeconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    AutoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    UseSeconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder String
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressive_order str
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learning_rate_parameter_name str
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    max_num_steps str
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    min_num_steps str
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    use_seconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder String
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

    GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse

    UseElapsedDuration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    UseElapsedDuration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration Boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    use_elapsed_duration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration Boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.

    GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse

    UseElapsedDuration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    UseElapsedDuration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration Boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    use_elapsed_duration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration Boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.

    GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse

    Goal string
    The optimization goal of the metric.
    MetricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    SafetyConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    Goal string
    The optimization goal of the metric.
    MetricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    SafetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal String
    The optimization goal of the metric.
    metricId String
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal string
    The optimization goal of the metric.
    metricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal str
    The optimization goal of the metric.
    metric_id str
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safety_config GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal String
    The optimization goal of the metric.
    metricId String
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig Property Map
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.

    GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse

    DesiredMinSafeTrialsFraction double
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    SafetyThreshold double
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    DesiredMinSafeTrialsFraction float64
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    SafetyThreshold float64
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction Double
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold Double
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction number
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold number
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desired_min_safe_trials_fraction float
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safety_threshold float
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction Number
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold Number
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse

    DefaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values List<string>
    The list of possible categories.
    DefaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values []string
    The list of possible categories.
    defaultValue String
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<String>
    The list of possible categories.
    defaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values string[]
    The list of possible categories.
    default_value str
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values Sequence[str]
    The list of possible categories.
    defaultValue String
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<String>
    The list of possible categories.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse

    Values List<string>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    Values []string
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values string[]
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values Sequence[str]
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse

    Values List<double>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    Values []float64
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values List<Double>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values number[]
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values Sequence[float]
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values List<Number>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse

    Values List<string>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    Values []string
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values string[]
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values Sequence[str]
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse

    ParameterSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse
    The spec for a conditional parameter.
    ParentCategoricalValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse
    The spec for matching values from a parent parameter of CATEGORICAL type.
    ParentDiscreteValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse
    The spec for matching values from a parent parameter of DISCRETE type.
    ParentIntValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse
    The spec for matching values from a parent parameter of INTEGER type.
    parameterSpec Property Map
    The spec for a conditional parameter.
    parentCategoricalValues Property Map
    The spec for matching values from a parent parameter of CATEGORICAL type.
    parentDiscreteValues Property Map
    The spec for matching values from a parent parameter of DISCRETE type.
    parentIntValues Property Map
    The spec for matching values from a parent parameter of INTEGER type.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse

    DefaultValue double
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values List<double>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    DefaultValue float64
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values []float64
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue Double
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<Double>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue number
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values number[]
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    default_value float
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values Sequence[float]
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue Number
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<Number>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse

    DefaultValue double
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue double
    Inclusive maximum value of the parameter.
    MinValue double
    Inclusive minimum value of the parameter.
    DefaultValue float64
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue float64
    Inclusive maximum value of the parameter.
    MinValue float64
    Inclusive minimum value of the parameter.
    defaultValue Double
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue Double
    Inclusive maximum value of the parameter.
    minValue Double
    Inclusive minimum value of the parameter.
    defaultValue number
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue number
    Inclusive maximum value of the parameter.
    minValue number
    Inclusive minimum value of the parameter.
    default_value float
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    max_value float
    Inclusive maximum value of the parameter.
    min_value float
    Inclusive minimum value of the parameter.
    defaultValue Number
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue Number
    Inclusive maximum value of the parameter.
    minValue Number
    Inclusive minimum value of the parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse

    DefaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue string
    Inclusive maximum value of the parameter.
    MinValue string
    Inclusive minimum value of the parameter.
    DefaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue string
    Inclusive maximum value of the parameter.
    MinValue string
    Inclusive minimum value of the parameter.
    defaultValue String
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue String
    Inclusive maximum value of the parameter.
    minValue String
    Inclusive minimum value of the parameter.
    defaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue string
    Inclusive maximum value of the parameter.
    minValue string
    Inclusive minimum value of the parameter.
    default_value str
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    max_value str
    Inclusive maximum value of the parameter.
    min_value str
    Inclusive minimum value of the parameter.
    defaultValue String
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue String
    Inclusive maximum value of the parameter.
    minValue String
    Inclusive minimum value of the parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse

    CategoricalValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    ConditionalParameterSpecs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    DiscreteValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    DoubleValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    IntegerValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    ParameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    ScaleType string
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    CategoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    ConditionalParameterSpecs []GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    DiscreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    DoubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    IntegerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    ParameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    ScaleType string
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    categoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs List<GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    parameterId String
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    scaleType String
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    categoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse[]
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    parameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    scaleType string
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    categorical_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    conditional_parameter_specs Sequence[GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse]
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discrete_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    double_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    integer_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    parameter_id str
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    scale_type str
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    categoricalValueSpec Property Map
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs List<Property Map>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec Property Map
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec Property Map
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec Property Map
    The value spec for an 'INTEGER' parameter.
    parameterId String
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    scaleType String
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.

    GoogleCloudAiplatformV1beta1StudySpecResponse

    Algorithm string
    The search algorithm specified for the Study.
    ConvexAutomatedStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    ConvexStopConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    DecayCurveStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    MeasurementSelectionType string
    Describe which measurement selection type will be used
    MedianAutomatedStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    Metrics List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse>
    Metric specs for the Study.
    ObservationNoise string
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Parameters List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse>
    The set of parameters to tune.
    StudyStoppingConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    TransferLearningConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    Algorithm string
    The search algorithm specified for the Study.
    ConvexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    ConvexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    DecayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    MeasurementSelectionType string
    Describe which measurement selection type will be used
    MedianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    Metrics []GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse
    Metric specs for the Study.
    ObservationNoise string
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Parameters []GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse
    The set of parameters to tune.
    StudyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    TransferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    algorithm String
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    convexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    measurementSelectionType String
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    metrics List<GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse>
    Metric specs for the Study.
    observationNoise String
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    parameters List<GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse>
    The set of parameters to tune.
    studyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    algorithm string
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    convexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    measurementSelectionType string
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    metrics GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse[]
    Metric specs for the Study.
    observationNoise string
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    parameters GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse[]
    The set of parameters to tune.
    studyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    algorithm str
    The search algorithm specified for the Study.
    convex_automated_stopping_spec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    convex_stop_config GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decay_curve_stopping_spec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    measurement_selection_type str
    Describe which measurement selection type will be used
    median_automated_stopping_spec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    metrics Sequence[GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse]
    Metric specs for the Study.
    observation_noise str
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    parameters Sequence[GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse]
    The set of parameters to tune.
    study_stopping_config GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transfer_learning_config GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    algorithm String
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec Property Map
    The automated early stopping spec using convex stopping rule.
    convexStopConfig Property Map
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec Property Map
    The automated early stopping spec using decay curve rule.
    measurementSelectionType String
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec Property Map
    The automated early stopping spec using median rule.
    metrics List<Property Map>
    Metric specs for the Study.
    observationNoise String
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    parameters List<Property Map>
    The set of parameters to tune.
    studyStoppingConfig Property Map
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig Property Map
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob

    GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse

    MaxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    MaxNumTrials int
    If there are more than this many trials, stop the study.
    MaxNumTrialsNoProgress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    MaximumRuntimeConstraint Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    MinNumTrials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    MinimumRuntimeConstraint Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    ShouldStopAsap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    MaxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    MaxNumTrials int
    If there are more than this many trials, stop the study.
    MaxNumTrialsNoProgress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    MaximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    MinNumTrials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    MinimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    ShouldStopAsap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress String
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials Integer
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress Integer
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    minNumTrials Integer
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap Boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials number
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress number
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    minNumTrials number
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    max_duration_no_progress str
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    max_num_trials int
    If there are more than this many trials, stop the study.
    max_num_trials_no_progress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximum_runtime_constraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    min_num_trials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimum_runtime_constraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    should_stop_asap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress String
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials Number
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress Number
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint Property Map
    If the specified time or duration has passed, stop the study.
    minNumTrials Number
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint Property Map
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap Boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).

    GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse

    DisableTransferLearning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    PriorStudyNames List<string>
    Names of previously completed studies
    DisableTransferLearning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    PriorStudyNames []string
    Names of previously completed studies
    disableTransferLearning Boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    priorStudyNames List<String>
    Names of previously completed studies
    disableTransferLearning boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    priorStudyNames string[]
    Names of previously completed studies
    disable_transfer_learning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    prior_study_names Sequence[str]
    Names of previously completed studies
    disableTransferLearning Boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    priorStudyNames List<String>
    Names of previously completed studies

    GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse

    EndTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    MaxDuration string
    Counts the wallclock time passed since the creation of this Study.
    EndTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    MaxDuration string
    Counts the wallclock time passed since the creation of this Study.
    endTime String
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration String
    Counts the wallclock time passed since the creation of this Study.
    endTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration string
    Counts the wallclock time passed since the creation of this Study.
    end_time str
    Compares the wallclock time to this time. Must use UTC timezone.
    max_duration str
    Counts the wallclock time passed since the creation of this Study.
    endTime String
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration String
    Counts the wallclock time passed since the creation of this Study.

    GoogleCloudAiplatformV1beta1TrialParameterResponse

    ParameterId string
    The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
    Value object
    The value of the parameter. number_value will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. string_value will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
    ParameterId string
    The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
    Value interface{}
    The value of the parameter. number_value will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. string_value will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
    parameterId String
    The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
    value Object
    The value of the parameter. number_value will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. string_value will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
    parameterId string
    The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
    value any
    The value of the parameter. number_value will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. string_value will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
    parameter_id str
    The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
    value Any
    The value of the parameter. number_value will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. string_value will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
    parameterId String
    The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
    value Any
    The value of the parameter. number_value will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. string_value will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.

    GoogleCloudAiplatformV1beta1TrialResponse

    ClientId string
    The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
    CustomJob string
    The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
    EndTime string
    Time when the Trial's status changed to SUCCEEDED or INFEASIBLE.
    FinalMeasurement Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MeasurementResponse
    The final measurement containing the objective value.
    InfeasibleReason string
    A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.
    Measurements List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MeasurementResponse>
    A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
    Name string
    Resource name of the Trial assigned by the service.
    Parameters List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1TrialParameterResponse>
    The parameters of the Trial.
    StartTime string
    Time when the Trial was started.
    State string
    The detailed state of the Trial.
    WebAccessUris Dictionary<string, string>
    URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is true. The keys are names of each node used for the trial; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
    ClientId string
    The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
    CustomJob string
    The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
    EndTime string
    Time when the Trial's status changed to SUCCEEDED or INFEASIBLE.
    FinalMeasurement GoogleCloudAiplatformV1beta1MeasurementResponse
    The final measurement containing the objective value.
    InfeasibleReason string
    A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.
    Measurements []GoogleCloudAiplatformV1beta1MeasurementResponse
    A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
    Name string
    Resource name of the Trial assigned by the service.
    Parameters []GoogleCloudAiplatformV1beta1TrialParameterResponse
    The parameters of the Trial.
    StartTime string
    Time when the Trial was started.
    State string
    The detailed state of the Trial.
    WebAccessUris map[string]string
    URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is true. The keys are names of each node used for the trial; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
    clientId String
    The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
    customJob String
    The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
    endTime String
    Time when the Trial's status changed to SUCCEEDED or INFEASIBLE.
    finalMeasurement GoogleCloudAiplatformV1beta1MeasurementResponse
    The final measurement containing the objective value.
    infeasibleReason String
    A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.
    measurements List<GoogleCloudAiplatformV1beta1MeasurementResponse>
    A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
    name String
    Resource name of the Trial assigned by the service.
    parameters List<GoogleCloudAiplatformV1beta1TrialParameterResponse>
    The parameters of the Trial.
    startTime String
    Time when the Trial was started.
    state String
    The detailed state of the Trial.
    webAccessUris Map<String,String>
    URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is true. The keys are names of each node used for the trial; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
    clientId string
    The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
    customJob string
    The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
    endTime string
    Time when the Trial's status changed to SUCCEEDED or INFEASIBLE.
    finalMeasurement GoogleCloudAiplatformV1beta1MeasurementResponse
    The final measurement containing the objective value.
    infeasibleReason string
    A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.
    measurements GoogleCloudAiplatformV1beta1MeasurementResponse[]
    A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
    name string
    Resource name of the Trial assigned by the service.
    parameters GoogleCloudAiplatformV1beta1TrialParameterResponse[]
    The parameters of the Trial.
    startTime string
    Time when the Trial was started.
    state string
    The detailed state of the Trial.
    webAccessUris {[key: string]: string}
    URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is true. The keys are names of each node used for the trial; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
    client_id str
    The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
    custom_job str
    The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
    end_time str
    Time when the Trial's status changed to SUCCEEDED or INFEASIBLE.
    final_measurement GoogleCloudAiplatformV1beta1MeasurementResponse
    The final measurement containing the objective value.
    infeasible_reason str
    A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.
    measurements Sequence[GoogleCloudAiplatformV1beta1MeasurementResponse]
    A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
    name str
    Resource name of the Trial assigned by the service.
    parameters Sequence[GoogleCloudAiplatformV1beta1TrialParameterResponse]
    The parameters of the Trial.
    start_time str
    Time when the Trial was started.
    state str
    The detailed state of the Trial.
    web_access_uris Mapping[str, str]
    URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is true. The keys are names of each node used for the trial; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
    clientId String
    The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
    customJob String
    The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
    endTime String
    Time when the Trial's status changed to SUCCEEDED or INFEASIBLE.
    finalMeasurement Property Map
    The final measurement containing the objective value.
    infeasibleReason String
    A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.
    measurements List<Property Map>
    A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
    name String
    Resource name of the Trial assigned by the service.
    parameters List<Property Map>
    The parameters of the Trial.
    startTime String
    Time when the Trial was started.
    state String
    The detailed state of the Trial.
    webAccessUris Map<String>
    URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is true. The keys are names of each node used for the trial; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

    GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse

    ContainerSpec GoogleCloudAiplatformV1beta1ContainerSpecResponse
    The custom container task.
    DiskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse
    Disk spec.
    MachineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Optional. Immutable. The specification of a single machine.
    NfsMounts []GoogleCloudAiplatformV1beta1NfsMountResponse
    Optional. List of NFS mount spec.
    PythonPackageSpec GoogleCloudAiplatformV1beta1PythonPackageSpecResponse
    The Python packaged task.
    ReplicaCount string
    Optional. The number of worker replicas to use for this worker pool.
    containerSpec GoogleCloudAiplatformV1beta1ContainerSpecResponse
    The custom container task.
    diskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse
    Disk spec.
    machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Optional. Immutable. The specification of a single machine.
    nfsMounts List<GoogleCloudAiplatformV1beta1NfsMountResponse>
    Optional. List of NFS mount spec.
    pythonPackageSpec GoogleCloudAiplatformV1beta1PythonPackageSpecResponse
    The Python packaged task.
    replicaCount String
    Optional. The number of worker replicas to use for this worker pool.
    containerSpec GoogleCloudAiplatformV1beta1ContainerSpecResponse
    The custom container task.
    diskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse
    Disk spec.
    machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Optional. Immutable. The specification of a single machine.
    nfsMounts GoogleCloudAiplatformV1beta1NfsMountResponse[]
    Optional. List of NFS mount spec.
    pythonPackageSpec GoogleCloudAiplatformV1beta1PythonPackageSpecResponse
    The Python packaged task.
    replicaCount string
    Optional. The number of worker replicas to use for this worker pool.
    container_spec GoogleCloudAiplatformV1beta1ContainerSpecResponse
    The custom container task.
    disk_spec GoogleCloudAiplatformV1beta1DiskSpecResponse
    Disk spec.
    machine_spec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Optional. Immutable. The specification of a single machine.
    nfs_mounts Sequence[GoogleCloudAiplatformV1beta1NfsMountResponse]
    Optional. List of NFS mount spec.
    python_package_spec GoogleCloudAiplatformV1beta1PythonPackageSpecResponse
    The Python packaged task.
    replica_count str
    Optional. The number of worker replicas to use for this worker pool.
    containerSpec Property Map
    The custom container task.
    diskSpec Property Map
    Disk spec.
    machineSpec Property Map
    Optional. Immutable. The specification of a single machine.
    nfsMounts List<Property Map>
    Optional. List of NFS mount spec.
    pythonPackageSpec Property Map
    The Python packaged task.
    replicaCount String
    Optional. The number of worker replicas to use for this worker pool.

    GoogleRpcStatusResponse

    Code int
    The status code, which should be an enum value of google.rpc.Code.
    Details List<ImmutableDictionary<string, string>>
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    Message string
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    Code int
    The status code, which should be an enum value of google.rpc.Code.
    Details []map[string]string
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    Message string
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    code Integer
    The status code, which should be an enum value of google.rpc.Code.
    details List<Map<String,String>>
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    message String
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    code number
    The status code, which should be an enum value of google.rpc.Code.
    details {[key: string]: string}[]
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    message string
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    code int
    The status code, which should be an enum value of google.rpc.Code.
    details Sequence[Mapping[str, str]]
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    message str
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    code Number
    The status code, which should be an enum value of google.rpc.Code.
    details List<Map<String>>
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    message String
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

    Package Details

    Repository
    Google Cloud Native pulumi/pulumi-google-native
    License
    Apache-2.0
    google-native logo

    Google Cloud Native is in preview. Google Cloud Classic is fully supported.

    Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
    pulumi/pulumi-google-native