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

google-native.aiplatform/v1beta1.DeploymentResourcePool

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

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

    Create a DeploymentResourcePool.

    Create DeploymentResourcePool Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new DeploymentResourcePool(name: string, args: DeploymentResourcePoolArgs, opts?: CustomResourceOptions);
    @overload
def DeploymentResourcePool(resource_name: str,
                           args: DeploymentResourcePoolArgs,
                           opts: Optional[ResourceOptions] = None)

@overload
def DeploymentResourcePool(resource_name: str,
                           opts: Optional[ResourceOptions] = None,
                           dedicated_resources: Optional[GoogleCloudAiplatformV1beta1DedicatedResourcesArgs] = None,
                           deployment_resource_pool_id: Optional[str] = None,
                           location: Optional[str] = None,
                           name: Optional[str] = None,
                           project: Optional[str] = None)
    func NewDeploymentResourcePool(ctx *Context, name string, args DeploymentResourcePoolArgs, opts ...ResourceOption) (*DeploymentResourcePool, error)
    public DeploymentResourcePool(string name, DeploymentResourcePoolArgs args, CustomResourceOptions? opts = null)
    public DeploymentResourcePool(String name, DeploymentResourcePoolArgs args)
public DeploymentResourcePool(String name, DeploymentResourcePoolArgs args, CustomResourceOptions options)

    type: google-native:aiplatform/v1beta1:DeploymentResourcePool
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args DeploymentResourcePoolArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args DeploymentResourcePoolArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args DeploymentResourcePoolArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args DeploymentResourcePoolArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args DeploymentResourcePoolArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var google_nativeDeploymentResourcePoolResource = new GoogleNative.Aiplatform.V1Beta1.DeploymentResourcePool("google-nativeDeploymentResourcePoolResource", new()
{
    DedicatedResources = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1DedicatedResourcesArgs
    {
        MachineSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecArgs
        {
            AcceleratorCount = 0,
            AcceleratorType = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
            MachineType = "string",
            TpuTopology = "string",
        },
        MinReplicaCount = 0,
        AutoscalingMetricSpecs = new[]
        {
            new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1AutoscalingMetricSpecArgs
            {
                MetricName = "string",
                Target = 0,
            },
        },
        MaxReplicaCount = 0,
    },
    DeploymentResourcePoolId = "string",
    Location = "string",
    Name = "string",
    Project = "string",
});

    example, err := aiplatformv1beta1.NewDeploymentResourcePool(ctx, "google-nativeDeploymentResourcePoolResource", &aiplatformv1beta1.DeploymentResourcePoolArgs{
	DedicatedResources: &aiplatform.GoogleCloudAiplatformV1beta1DedicatedResourcesArgs{
		MachineSpec: &aiplatform.GoogleCloudAiplatformV1beta1MachineSpecArgs{
			AcceleratorCount: pulumi.Int(0),
			AcceleratorType:  aiplatformv1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified,
			MachineType:      pulumi.String("string"),
			TpuTopology:      pulumi.String("string"),
		},
		MinReplicaCount: pulumi.Int(0),
		AutoscalingMetricSpecs: aiplatform.GoogleCloudAiplatformV1beta1AutoscalingMetricSpecArray{
			&aiplatform.GoogleCloudAiplatformV1beta1AutoscalingMetricSpecArgs{
				MetricName: pulumi.String("string"),
				Target:     pulumi.Int(0),
			},
		},
		MaxReplicaCount: pulumi.Int(0),
	},
	DeploymentResourcePoolId: pulumi.String("string"),
	Location:                 pulumi.String("string"),
	Name:                     pulumi.String("string"),
	Project:                  pulumi.String("string"),
})

    var google_nativeDeploymentResourcePoolResource = new DeploymentResourcePool("google-nativeDeploymentResourcePoolResource", DeploymentResourcePoolArgs.builder()
    .dedicatedResources(GoogleCloudAiplatformV1beta1DedicatedResourcesArgs.builder()
        .machineSpec(GoogleCloudAiplatformV1beta1MachineSpecArgs.builder()
            .acceleratorCount(0)
            .acceleratorType("ACCELERATOR_TYPE_UNSPECIFIED")
            .machineType("string")
            .tpuTopology("string")
            .build())
        .minReplicaCount(0)
        .autoscalingMetricSpecs(GoogleCloudAiplatformV1beta1AutoscalingMetricSpecArgs.builder()
            .metricName("string")
            .target(0)
            .build())
        .maxReplicaCount(0)
        .build())
    .deploymentResourcePoolId("string")
    .location("string")
    .name("string")
    .project("string")
    .build());

    google_native_deployment_resource_pool_resource = google_native.aiplatform.v1beta1.DeploymentResourcePool("google-nativeDeploymentResourcePoolResource",
    dedicated_resources={
        "machine_spec": {
            "accelerator_count": 0,
            "accelerator_type": google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.ACCELERATOR_TYPE_UNSPECIFIED,
            "machine_type": "string",
            "tpu_topology": "string",
        },
        "min_replica_count": 0,
        "autoscaling_metric_specs": [{
            "metric_name": "string",
            "target": 0,
        }],
        "max_replica_count": 0,
    },
    deployment_resource_pool_id="string",
    location="string",
    name="string",
    project="string")

    const google_nativeDeploymentResourcePoolResource = new google_native.aiplatform.v1beta1.DeploymentResourcePool("google-nativeDeploymentResourcePoolResource", {
    dedicatedResources: {
        machineSpec: {
            acceleratorCount: 0,
            acceleratorType: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
            machineType: "string",
            tpuTopology: "string",
        },
        minReplicaCount: 0,
        autoscalingMetricSpecs: [{
            metricName: "string",
            target: 0,
        }],
        maxReplicaCount: 0,
    },
    deploymentResourcePoolId: "string",
    location: "string",
    name: "string",
    project: "string",
});

    type: google-native:aiplatform/v1beta1:DeploymentResourcePool
properties:
    dedicatedResources:
        autoscalingMetricSpecs:
            - metricName: string
              target: 0
        machineSpec:
            acceleratorCount: 0
            acceleratorType: ACCELERATOR_TYPE_UNSPECIFIED
            machineType: string
            tpuTopology: string
        maxReplicaCount: 0
        minReplicaCount: 0
    deploymentResourcePoolId: string
    location: string
    name: string
    project: string

    DeploymentResourcePool Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The DeploymentResourcePool resource accepts the following input properties:

    DedicatedResources Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1DedicatedResources
    The underlying DedicatedResources that the DeploymentResourcePool uses.
    DeploymentResourcePoolId string
    The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
    Location string
    Name string
    Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
    Project string
    DedicatedResources GoogleCloudAiplatformV1beta1DedicatedResourcesArgs
    The underlying DedicatedResources that the DeploymentResourcePool uses.
    DeploymentResourcePoolId string
    The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
    Location string
    Name string
    Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
    Project string
    dedicatedResources GoogleCloudAiplatformV1beta1DedicatedResources
    The underlying DedicatedResources that the DeploymentResourcePool uses.
    deploymentResourcePoolId String
    The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
    location String
    name String
    Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
    project String
    dedicatedResources GoogleCloudAiplatformV1beta1DedicatedResources
    The underlying DedicatedResources that the DeploymentResourcePool uses.
    deploymentResourcePoolId string
    The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
    location string
    name string
    Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
    project string
    dedicated_resources GoogleCloudAiplatformV1beta1DedicatedResourcesArgs
    The underlying DedicatedResources that the DeploymentResourcePool uses.
    deployment_resource_pool_id str
    The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
    location str
    name str
    Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
    project str
    dedicatedResources Property Map
    The underlying DedicatedResources that the DeploymentResourcePool uses.
    deploymentResourcePoolId String
    The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
    location String
    name String
    Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
    project String

    Outputs

    All input properties are implicitly available as output properties. Additionally, the DeploymentResourcePool resource produces the following output properties:

    CreateTime string
    Timestamp when this DeploymentResourcePool was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    CreateTime string
    Timestamp when this DeploymentResourcePool was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    createTime String
    Timestamp when this DeploymentResourcePool was created.
    id String
    The provider-assigned unique ID for this managed resource.
    createTime string
    Timestamp when this DeploymentResourcePool was created.
    id string
    The provider-assigned unique ID for this managed resource.
    create_time str
    Timestamp when this DeploymentResourcePool was created.
    id str
    The provider-assigned unique ID for this managed resource.
    createTime String
    Timestamp when this DeploymentResourcePool was created.
    id String
    The provider-assigned unique ID for this managed resource.

    Supporting Types

    GoogleCloudAiplatformV1beta1AutoscalingMetricSpec, GoogleCloudAiplatformV1beta1AutoscalingMetricSpecArgs

    MetricName string
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    Target int
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    MetricName string
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    Target int
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    metricName String
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    target Integer
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    metricName string
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    target number
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    metric_name str
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    target int
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    metricName String
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    target Number
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

    GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse, GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponseArgs

    MetricName string
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    Target int
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    MetricName string
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    Target int
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    metricName String
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    target Integer
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    metricName string
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    target number
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    metric_name str
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    target int
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
    metricName String
    The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
    target Number
    The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

    GoogleCloudAiplatformV1beta1DedicatedResources, GoogleCloudAiplatformV1beta1DedicatedResourcesArgs

    MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine used by the prediction.
    MinReplicaCount int
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    AutoscalingMetricSpecs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1AutoscalingMetricSpec>
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    MaxReplicaCount int
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    MachineSpec GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine used by the prediction.
    MinReplicaCount int
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    AutoscalingMetricSpecs []GoogleCloudAiplatformV1beta1AutoscalingMetricSpec
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    MaxReplicaCount int
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    machineSpec GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine used by the prediction.
    minReplicaCount Integer
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    autoscalingMetricSpecs List<GoogleCloudAiplatformV1beta1AutoscalingMetricSpec>
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    maxReplicaCount Integer
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    machineSpec GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine used by the prediction.
    minReplicaCount number
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    autoscalingMetricSpecs GoogleCloudAiplatformV1beta1AutoscalingMetricSpec[]
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    maxReplicaCount number
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    machine_spec GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine used by the prediction.
    min_replica_count int
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    autoscaling_metric_specs Sequence[GoogleCloudAiplatformV1beta1AutoscalingMetricSpec]
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    max_replica_count int
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    machineSpec Property Map
    Immutable. The specification of a single machine used by the prediction.
    minReplicaCount Number
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    autoscalingMetricSpecs List<Property Map>
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    maxReplicaCount Number
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).

    GoogleCloudAiplatformV1beta1DedicatedResourcesResponse, GoogleCloudAiplatformV1beta1DedicatedResourcesResponseArgs

    AutoscalingMetricSpecs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse>
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine used by the prediction.
    MaxReplicaCount int
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    MinReplicaCount int
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    AutoscalingMetricSpecs []GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    MachineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine used by the prediction.
    MaxReplicaCount int
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    MinReplicaCount int
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    autoscalingMetricSpecs List<GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse>
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine used by the prediction.
    maxReplicaCount Integer
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    minReplicaCount Integer
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    autoscalingMetricSpecs GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse[]
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine used by the prediction.
    maxReplicaCount number
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    minReplicaCount number
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    autoscaling_metric_specs Sequence[GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse]
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    machine_spec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine used by the prediction.
    max_replica_count int
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    min_replica_count int
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
    autoscalingMetricSpecs List<Property Map>
    Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
    machineSpec Property Map
    Immutable. The specification of a single machine used by the prediction.
    maxReplicaCount Number
    Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
    minReplicaCount Number
    Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.

    GoogleCloudAiplatformV1beta1MachineSpec, GoogleCloudAiplatformV1beta1MachineSpecArgs

    AcceleratorCount int
    The number of accelerators to attach to the machine.
    AcceleratorType Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    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 GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    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 GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    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 GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    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 GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    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 "ACCELERATOR_TYPE_UNSPECIFIED" | "NVIDIA_TESLA_K80" | "NVIDIA_TESLA_P100" | "NVIDIA_TESLA_V100" | "NVIDIA_TESLA_P4" | "NVIDIA_TESLA_T4" | "NVIDIA_TESLA_A100" | "NVIDIA_A100_80GB" | "NVIDIA_L4" | "NVIDIA_H100_80GB" | "TPU_V2" | "TPU_V3" | "TPU_V4_POD" | "TPU_V5_LITEPOD"
    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").

    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType, GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeArgs

    AcceleratorTypeUnspecified
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    NvidiaTeslaK80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    NvidiaTeslaP100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    NvidiaTeslaV100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    NvidiaTeslaP4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    NvidiaTeslaT4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    NvidiaTeslaA100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    NvidiaA10080gb
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    NvidiaL4
    NVIDIA_L4Nvidia L4 GPU.
    NvidiaH10080gb
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    TpuV2
    TPU_V2TPU v2.
    TpuV3
    TPU_V3TPU v3.
    TpuV4Pod
    TPU_V4_PODTPU v4.
    TpuV5Litepod
    TPU_V5_LITEPODTPU v5.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaK80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaP100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaV100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaP4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaT4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaA100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaA10080gb
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaL4
    NVIDIA_L4Nvidia L4 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaH10080gb
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeTpuV2
    TPU_V2TPU v2.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeTpuV3
    TPU_V3TPU v3.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeTpuV4Pod
    TPU_V4_PODTPU v4.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeTpuV5Litepod
    TPU_V5_LITEPODTPU v5.
    AcceleratorTypeUnspecified
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    NvidiaTeslaK80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    NvidiaTeslaP100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    NvidiaTeslaV100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    NvidiaTeslaP4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    NvidiaTeslaT4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    NvidiaTeslaA100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    NvidiaA10080gb
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    NvidiaL4
    NVIDIA_L4Nvidia L4 GPU.
    NvidiaH10080gb
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    TpuV2
    TPU_V2TPU v2.
    TpuV3
    TPU_V3TPU v3.
    TpuV4Pod
    TPU_V4_PODTPU v4.
    TpuV5Litepod
    TPU_V5_LITEPODTPU v5.
    AcceleratorTypeUnspecified
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    NvidiaTeslaK80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    NvidiaTeslaP100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    NvidiaTeslaV100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    NvidiaTeslaP4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    NvidiaTeslaT4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    NvidiaTeslaA100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    NvidiaA10080gb
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    NvidiaL4
    NVIDIA_L4Nvidia L4 GPU.
    NvidiaH10080gb
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    TpuV2
    TPU_V2TPU v2.
    TpuV3
    TPU_V3TPU v3.
    TpuV4Pod
    TPU_V4_PODTPU v4.
    TpuV5Litepod
    TPU_V5_LITEPODTPU v5.
    ACCELERATOR_TYPE_UNSPECIFIED
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    NVIDIA_TESLA_K80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    NVIDIA_TESLA_P100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    NVIDIA_TESLA_V100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    NVIDIA_TESLA_P4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    NVIDIA_TESLA_T4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    NVIDIA_TESLA_A100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    NVIDIA_A10080GB
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    NVIDIA_L4
    NVIDIA_L4Nvidia L4 GPU.
    NVIDIA_H10080GB
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    TPU_V2
    TPU_V2TPU v2.
    TPU_V3
    TPU_V3TPU v3.
    TPU_V4_POD
    TPU_V4_PODTPU v4.
    TPU_V5_LITEPOD
    TPU_V5_LITEPODTPU v5.
    "ACCELERATOR_TYPE_UNSPECIFIED"
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    "NVIDIA_TESLA_K80"
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    "NVIDIA_TESLA_P100"
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    "NVIDIA_TESLA_V100"
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    "NVIDIA_TESLA_P4"
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    "NVIDIA_TESLA_T4"
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    "NVIDIA_TESLA_A100"
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    "NVIDIA_A100_80GB"
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    "NVIDIA_L4"
    NVIDIA_L4Nvidia L4 GPU.
    "NVIDIA_H100_80GB"
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    "TPU_V2"
    TPU_V2TPU v2.
    "TPU_V3"
    TPU_V3TPU v3.
    "TPU_V4_POD"
    TPU_V4_PODTPU v4.
    "TPU_V5_LITEPOD"
    TPU_V5_LITEPODTPU v5.

    GoogleCloudAiplatformV1beta1MachineSpecResponse, GoogleCloudAiplatformV1beta1MachineSpecResponseArgs

    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").

    Package Details

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

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

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