Google Cloud Native v0.32.0, Nov 29 23

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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

google-native.aiplatform/v1.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

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.

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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)
// Output-based functions aren't available in Java yet

fn::invoke:
  function: google-native:aiplatform/v1:getHyperparameterTuningJob
  arguments:
    # arguments dictionary

The following arguments are supported:

HyperparameterTuningJobId string
Location string
Project string

HyperparameterTuningJobId string
Location string
Project string

hyperparameterTuningJobId String
location String
project String

hyperparameterTuningJobId string
location string
project string

hyperparameter_tuning_job_id str
location str
project str

hyperparameterTuningJobId String
location String
project String

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.V1.Outputs.GoogleCloudAiplatformV1EncryptionSpecResponse: 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.V1.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.V1.Outputs.GoogleCloudAiplatformV1StudySpecResponse: Study configuration of the HyperparameterTuningJob.
TrialJobSpec Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1CustomJobSpecResponse: The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
Trials List<Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1TrialResponse>: 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 GoogleCloudAiplatformV1EncryptionSpecResponse: 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 GoogleCloudAiplatformV1StudySpecResponse: Study configuration of the HyperparameterTuningJob.
TrialJobSpec GoogleCloudAiplatformV1CustomJobSpecResponse: The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
Trials []GoogleCloudAiplatformV1TrialResponse: 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 GoogleCloudAiplatformV1EncryptionSpecResponse: 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 GoogleCloudAiplatformV1StudySpecResponse: Study configuration of the HyperparameterTuningJob.
trialJobSpec GoogleCloudAiplatformV1CustomJobSpecResponse: The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
trials List<GoogleCloudAiplatformV1TrialResponse>: 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 GoogleCloudAiplatformV1EncryptionSpecResponse: 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 GoogleCloudAiplatformV1StudySpecResponse: Study configuration of the HyperparameterTuningJob.
trialJobSpec GoogleCloudAiplatformV1CustomJobSpecResponse: The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
trials GoogleCloudAiplatformV1TrialResponse[]: 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 GoogleCloudAiplatformV1EncryptionSpecResponse: 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 GoogleCloudAiplatformV1StudySpecResponse: Study configuration of the HyperparameterTuningJob.
trial_job_spec GoogleCloudAiplatformV1CustomJobSpecResponse: The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
trials Sequence[GoogleCloudAiplatformV1TrialResponse]: 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

GoogleCloudAiplatformV1ContainerSpecResponse

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.V1.Inputs.GoogleCloudAiplatformV1EnvVarResponse>: 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 []GoogleCloudAiplatformV1EnvVarResponse: 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<GoogleCloudAiplatformV1EnvVarResponse>: 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 GoogleCloudAiplatformV1EnvVarResponse[]: 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[GoogleCloudAiplatformV1EnvVarResponse]: 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.

GoogleCloudAiplatformV1CustomJobSpecResponse

BaseOutputDirectory Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationResponse: 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.
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.V1.Inputs.GoogleCloudAiplatformV1SchedulingResponse: 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.V1.Inputs.GoogleCloudAiplatformV1WorkerPoolSpecResponse>: 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 GoogleCloudAiplatformV1GcsDestinationResponse: 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.
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 GoogleCloudAiplatformV1SchedulingResponse: 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 []GoogleCloudAiplatformV1WorkerPoolSpecResponse: 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 GoogleCloudAiplatformV1GcsDestinationResponse: 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.
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 GoogleCloudAiplatformV1SchedulingResponse: 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<GoogleCloudAiplatformV1WorkerPoolSpecResponse>: 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 GoogleCloudAiplatformV1GcsDestinationResponse: 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.
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 GoogleCloudAiplatformV1SchedulingResponse: 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 GoogleCloudAiplatformV1WorkerPoolSpecResponse[]: 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 GoogleCloudAiplatformV1GcsDestinationResponse: 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.
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 GoogleCloudAiplatformV1SchedulingResponse: 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[GoogleCloudAiplatformV1WorkerPoolSpecResponse]: 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.
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.

GoogleCloudAiplatformV1DiskSpecResponse

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).

GoogleCloudAiplatformV1EncryptionSpecResponse

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.

GoogleCloudAiplatformV1EnvVarResponse

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.

GoogleCloudAiplatformV1GcsDestinationResponse

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.

GoogleCloudAiplatformV1MachineSpecResponse

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").

GoogleCloudAiplatformV1MeasurementMetricResponse

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.

GoogleCloudAiplatformV1MeasurementResponse

ElapsedDuration string: Time that the Trial has been running at the point of this Measurement.
Metrics List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1MeasurementMetricResponse>: 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 []GoogleCloudAiplatformV1MeasurementMetricResponse: 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<GoogleCloudAiplatformV1MeasurementMetricResponse>: 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 GoogleCloudAiplatformV1MeasurementMetricResponse[]: 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[GoogleCloudAiplatformV1MeasurementMetricResponse]: 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.

GoogleCloudAiplatformV1NfsMountResponse

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.

GoogleCloudAiplatformV1PythonPackageSpecResponse

Args List<string>: Command line arguments to be passed to the Python task.
Env List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarResponse>: 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 []GoogleCloudAiplatformV1EnvVarResponse: 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<GoogleCloudAiplatformV1EnvVarResponse>: 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 GoogleCloudAiplatformV1EnvVarResponse[]: 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[GoogleCloudAiplatformV1EnvVarResponse]: 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.

GoogleCloudAiplatformV1SchedulingResponse

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.

GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponse

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.

GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponse

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.

GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponse

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.

GoogleCloudAiplatformV1StudySpecMetricSpecResponse

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.V1.Inputs.GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponse: 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 GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponse: 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 GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponse: 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 GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponse: 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 GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponse: 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.

GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponse

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.

GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponse

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.

GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse

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.

GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse

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.

GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse

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.

GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponse

ParameterSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecResponse: The spec for a conditional parameter.
ParentCategoricalValues Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
ParentDiscreteValues Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
ParentIntValues Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse: The spec for matching values from a parent parameter of INTEGER type.

ParameterSpec GoogleCloudAiplatformV1StudySpecParameterSpecResponse: The spec for a conditional parameter.
ParentCategoricalValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
ParentDiscreteValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
ParentIntValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse: The spec for matching values from a parent parameter of INTEGER type.

parameterSpec GoogleCloudAiplatformV1StudySpecParameterSpecResponse: The spec for a conditional parameter.
parentCategoricalValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
parentDiscreteValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
parentIntValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse: The spec for matching values from a parent parameter of INTEGER type.

parameterSpec GoogleCloudAiplatformV1StudySpecParameterSpecResponse: The spec for a conditional parameter.
parentCategoricalValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
parentDiscreteValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
parentIntValues GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse: The spec for matching values from a parent parameter of INTEGER type.

parameter_spec GoogleCloudAiplatformV1StudySpecParameterSpecResponse: The spec for a conditional parameter.
parent_categorical_values GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
parent_discrete_values GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
parent_int_values GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse: 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.

GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponse

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.

GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponse

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.

GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponse

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.

GoogleCloudAiplatformV1StudySpecParameterSpecResponse

CategoricalValueSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponse: The value spec for a 'CATEGORICAL' parameter.
ConditionalParameterSpecs List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponse>: 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.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponse: The value spec for a 'DISCRETE' parameter.
DoubleValueSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponse: The value spec for a 'DOUBLE' parameter.
IntegerValueSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponse: 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 GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponse: The value spec for a 'CATEGORICAL' parameter.
ConditionalParameterSpecs []GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponse: 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 GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponse: The value spec for a 'DISCRETE' parameter.
DoubleValueSpec GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponse: The value spec for a 'DOUBLE' parameter.
IntegerValueSpec GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponse: 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 GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponse: The value spec for a 'CATEGORICAL' parameter.
conditionalParameterSpecs List<GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponse>: 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 GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponse: The value spec for a 'DISCRETE' parameter.
doubleValueSpec GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponse: The value spec for a 'DOUBLE' parameter.
integerValueSpec GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponse: 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 GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponse: The value spec for a 'CATEGORICAL' parameter.
conditionalParameterSpecs GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponse[]: 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 GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponse: The value spec for a 'DISCRETE' parameter.
doubleValueSpec GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponse: The value spec for a 'DOUBLE' parameter.
integerValueSpec GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponse: 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 GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponse: The value spec for a 'CATEGORICAL' parameter.
conditional_parameter_specs Sequence[GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponse]: 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 GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponse: The value spec for a 'DISCRETE' parameter.
double_value_spec GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponse: The value spec for a 'DOUBLE' parameter.
integer_value_spec GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponse: 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.

GoogleCloudAiplatformV1StudySpecResponse

Algorithm string: The search algorithm specified for the Study.
ConvexAutomatedStoppingSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponse: The automated early stopping spec using convex stopping rule.
DecayCurveStoppingSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponse: The automated early stopping spec using decay curve rule.
MeasurementSelectionType string: Describe which measurement selection type will be used
MedianAutomatedStoppingSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponse: The automated early stopping spec using median rule.
Metrics List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecMetricSpecResponse>: 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.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecResponse>: The set of parameters to tune.
StudyStoppingConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponse: Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.

Algorithm string: The search algorithm specified for the Study.
ConvexAutomatedStoppingSpec GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponse: The automated early stopping spec using convex stopping rule.
DecayCurveStoppingSpec GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponse: The automated early stopping spec using decay curve rule.
MeasurementSelectionType string: Describe which measurement selection type will be used
MedianAutomatedStoppingSpec GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponse: The automated early stopping spec using median rule.
Metrics []GoogleCloudAiplatformV1StudySpecMetricSpecResponse: 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 []GoogleCloudAiplatformV1StudySpecParameterSpecResponse: The set of parameters to tune.
StudyStoppingConfig GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponse: Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.

algorithm String: The search algorithm specified for the Study.
convexAutomatedStoppingSpec GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponse: The automated early stopping spec using convex stopping rule.
decayCurveStoppingSpec GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponse: The automated early stopping spec using decay curve rule.
measurementSelectionType String: Describe which measurement selection type will be used
medianAutomatedStoppingSpec GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponse: The automated early stopping spec using median rule.
metrics List<GoogleCloudAiplatformV1StudySpecMetricSpecResponse>: 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<GoogleCloudAiplatformV1StudySpecParameterSpecResponse>: The set of parameters to tune.
studyStoppingConfig GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponse: Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.

algorithm string: The search algorithm specified for the Study.
convexAutomatedStoppingSpec GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponse: The automated early stopping spec using convex stopping rule.
decayCurveStoppingSpec GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponse: The automated early stopping spec using decay curve rule.
measurementSelectionType string: Describe which measurement selection type will be used
medianAutomatedStoppingSpec GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponse: The automated early stopping spec using median rule.
metrics GoogleCloudAiplatformV1StudySpecMetricSpecResponse[]: 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 GoogleCloudAiplatformV1StudySpecParameterSpecResponse[]: The set of parameters to tune.
studyStoppingConfig GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponse: Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.

algorithm str: The search algorithm specified for the Study.
convex_automated_stopping_spec GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponse: The automated early stopping spec using convex stopping rule.
decay_curve_stopping_spec GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponse: 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 GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponse: The automated early stopping spec using median rule.
metrics Sequence[GoogleCloudAiplatformV1StudySpecMetricSpecResponse]: 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[GoogleCloudAiplatformV1StudySpecParameterSpecResponse]: The set of parameters to tune.
study_stopping_config GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponse: Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.

algorithm String: The search algorithm specified for the Study.
convexAutomatedStoppingSpec Property Map: The automated early stopping spec 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.

GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponse

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.V1.Inputs.GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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.V1.Inputs.GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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 GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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 GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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 GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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 GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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 GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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 GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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 GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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 GoogleCloudAiplatformV1StudyTimeConstraintResponse: 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).

GoogleCloudAiplatformV1StudyTimeConstraintResponse

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.

GoogleCloudAiplatformV1TrialParameterResponse

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'.

GoogleCloudAiplatformV1TrialResponse

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.V1.Inputs.GoogleCloudAiplatformV1MeasurementResponse: 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.V1.Inputs.GoogleCloudAiplatformV1MeasurementResponse>: 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.V1.Inputs.GoogleCloudAiplatformV1TrialParameterResponse>: 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 GoogleCloudAiplatformV1MeasurementResponse: 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 []GoogleCloudAiplatformV1MeasurementResponse: 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 []GoogleCloudAiplatformV1TrialParameterResponse: 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 GoogleCloudAiplatformV1MeasurementResponse: 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<GoogleCloudAiplatformV1MeasurementResponse>: 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<GoogleCloudAiplatformV1TrialParameterResponse>: 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 GoogleCloudAiplatformV1MeasurementResponse: 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 GoogleCloudAiplatformV1MeasurementResponse[]: 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 GoogleCloudAiplatformV1TrialParameterResponse[]: 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 GoogleCloudAiplatformV1MeasurementResponse: 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[GoogleCloudAiplatformV1MeasurementResponse]: 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[GoogleCloudAiplatformV1TrialParameterResponse]: 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.

GoogleCloudAiplatformV1WorkerPoolSpecResponse

ContainerSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ContainerSpecResponse: The custom container task.
DiskSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1DiskSpecResponse: Disk spec.
MachineSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1MachineSpecResponse: Optional. Immutable. The specification of a single machine.
NfsMounts List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1NfsMountResponse>: Optional. List of NFS mount spec.
PythonPackageSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PythonPackageSpecResponse: The Python packaged task.
ReplicaCount string: Optional. The number of worker replicas to use for this worker pool.

ContainerSpec GoogleCloudAiplatformV1ContainerSpecResponse: The custom container task.
DiskSpec GoogleCloudAiplatformV1DiskSpecResponse: Disk spec.
MachineSpec GoogleCloudAiplatformV1MachineSpecResponse: Optional. Immutable. The specification of a single machine.
NfsMounts []GoogleCloudAiplatformV1NfsMountResponse: Optional. List of NFS mount spec.
PythonPackageSpec GoogleCloudAiplatformV1PythonPackageSpecResponse: The Python packaged task.
ReplicaCount string: Optional. The number of worker replicas to use for this worker pool.

containerSpec GoogleCloudAiplatformV1ContainerSpecResponse: The custom container task.
diskSpec GoogleCloudAiplatformV1DiskSpecResponse: Disk spec.
machineSpec GoogleCloudAiplatformV1MachineSpecResponse: Optional. Immutable. The specification of a single machine.
nfsMounts List<GoogleCloudAiplatformV1NfsMountResponse>: Optional. List of NFS mount spec.
pythonPackageSpec GoogleCloudAiplatformV1PythonPackageSpecResponse: The Python packaged task.
replicaCount String: Optional. The number of worker replicas to use for this worker pool.

containerSpec GoogleCloudAiplatformV1ContainerSpecResponse: The custom container task.
diskSpec GoogleCloudAiplatformV1DiskSpecResponse: Disk spec.
machineSpec GoogleCloudAiplatformV1MachineSpecResponse: Optional. Immutable. The specification of a single machine.
nfsMounts GoogleCloudAiplatformV1NfsMountResponse[]: Optional. List of NFS mount spec.
pythonPackageSpec GoogleCloudAiplatformV1PythonPackageSpecResponse: The Python packaged task.
replicaCount string: Optional. The number of worker replicas to use for this worker pool.

container_spec GoogleCloudAiplatformV1ContainerSpecResponse: The custom container task.
disk_spec GoogleCloudAiplatformV1DiskSpecResponse: Disk spec.
machine_spec GoogleCloudAiplatformV1MachineSpecResponse: Optional. Immutable. The specification of a single machine.
nfs_mounts Sequence[GoogleCloudAiplatformV1NfsMountResponse]: Optional. List of NFS mount spec.
python_package_spec GoogleCloudAiplatformV1PythonPackageSpecResponse: 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 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

google-native.aiplatform/v1.getHyperparameterTuningJob

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Using getHyperparameterTuningJob

getHyperparameterTuningJob Result

Supporting Types

GoogleCloudAiplatformV1ContainerSpecResponse

GoogleCloudAiplatformV1CustomJobSpecResponse

GoogleCloudAiplatformV1DiskSpecResponse

GoogleCloudAiplatformV1EncryptionSpecResponse

GoogleCloudAiplatformV1EnvVarResponse

GoogleCloudAiplatformV1GcsDestinationResponse

GoogleCloudAiplatformV1MachineSpecResponse

GoogleCloudAiplatformV1MeasurementMetricResponse

GoogleCloudAiplatformV1MeasurementResponse

GoogleCloudAiplatformV1NfsMountResponse

GoogleCloudAiplatformV1PythonPackageSpecResponse

GoogleCloudAiplatformV1SchedulingResponse

GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponse

GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponse

GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponse

GoogleCloudAiplatformV1StudySpecMetricSpecResponse

GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponse

GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponse

GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse

GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse

GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse

GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponse

GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponse

GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponse

GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponse

GoogleCloudAiplatformV1StudySpecParameterSpecResponse

GoogleCloudAiplatformV1StudySpecResponse

GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponse

GoogleCloudAiplatformV1StudyTimeConstraintResponse

GoogleCloudAiplatformV1TrialParameterResponse

GoogleCloudAiplatformV1TrialResponse

GoogleCloudAiplatformV1WorkerPoolSpecResponse

GoogleRpcStatusResponse

Package Details

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