Google Cloud Native v0.32.0, Nov 29 23

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/v1beta1.PersistentResource

Explore with Pulumi AI

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 PersistentResource Resource

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

Constructor syntax

new PersistentResource(name: string, args: PersistentResourceArgs, opts?: CustomResourceOptions);

@overload
def PersistentResource(resource_name: str,
                       args: PersistentResourceArgs,
                       opts: Optional[ResourceOptions] = None)

@overload
def PersistentResource(resource_name: str,
                       opts: Optional[ResourceOptions] = None,
                       persistent_resource_id: Optional[str] = None,
                       resource_pools: Optional[Sequence[GoogleCloudAiplatformV1beta1ResourcePoolArgs]] = None,
                       display_name: Optional[str] = None,
                       encryption_spec: Optional[GoogleCloudAiplatformV1beta1EncryptionSpecArgs] = None,
                       labels: Optional[Mapping[str, str]] = None,
                       location: Optional[str] = None,
                       name: Optional[str] = None,
                       network: Optional[str] = None,
                       project: Optional[str] = None,
                       reserved_ip_ranges: Optional[Sequence[str]] = None,
                       resource_runtime_spec: Optional[GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs] = None)

func NewPersistentResource(ctx *Context, name string, args PersistentResourceArgs, opts ...ResourceOption) (*PersistentResource, error)

public PersistentResource(string name, PersistentResourceArgs args, CustomResourceOptions? opts = null)

public PersistentResource(String name, PersistentResourceArgs args)
public PersistentResource(String name, PersistentResourceArgs args, CustomResourceOptions options)

type: google-native:aiplatform/v1beta1:PersistentResource
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 PersistentResourceArgs: 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 PersistentResourceArgs: 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 PersistentResourceArgs: The arguments to resource properties.
opts ResourceOption: Bag of options to control resource's behavior.

name string: The unique name of the resource.
args PersistentResourceArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

name String: The unique name of the resource.
args PersistentResourceArgs: The arguments to resource properties.
options CustomResourceOptions: Bag of options to control resource's behavior.

Example

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

var persistentResourceResource = new GoogleNative.Aiplatform.V1Beta1.PersistentResource("persistentResourceResource", new()
{
    PersistentResourceId = "string",
    ResourcePools = new[]
    {
        new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ResourcePoolArgs
        {
            MachineSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecArgs
            {
                AcceleratorCount = 0,
                AcceleratorType = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
                MachineType = "string",
                TpuTopology = "string",
            },
            AutoscalingSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecArgs
            {
                MaxReplicaCount = "string",
                MinReplicaCount = "string",
            },
            DiskSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1DiskSpecArgs
            {
                BootDiskSizeGb = 0,
                BootDiskType = "string",
            },
            Id = "string",
            ReplicaCount = "string",
        },
    },
    DisplayName = "string",
    EncryptionSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpecArgs
    {
        KmsKeyName = "string",
    },
    Labels = 
    {
        { "string", "string" },
    },
    Location = "string",
    Name = "string",
    Network = "string",
    Project = "string",
    ReservedIpRanges = new[]
    {
        "string",
    },
    ResourceRuntimeSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs
    {
        RaySpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1RaySpecArgs
        {
            HeadNodeResourcePoolId = "string",
            ImageUri = "string",
            ResourcePoolImages = 
            {
                { "string", "string" },
            },
        },
        ServiceAccountSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ServiceAccountSpecArgs
        {
            EnableCustomServiceAccount = false,
            ServiceAccount = "string",
        },
    },
});

example, err := aiplatformv1beta1.NewPersistentResource(ctx, "persistentResourceResource", &aiplatformv1beta1.PersistentResourceArgs{
PersistentResourceId: pulumi.String("string"),
ResourcePools: aiplatform.GoogleCloudAiplatformV1beta1ResourcePoolArray{
&aiplatform.GoogleCloudAiplatformV1beta1ResourcePoolArgs{
MachineSpec: &aiplatform.GoogleCloudAiplatformV1beta1MachineSpecArgs{
AcceleratorCount: pulumi.Int(0),
AcceleratorType: aiplatformv1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified,
MachineType: pulumi.String("string"),
TpuTopology: pulumi.String("string"),
},
AutoscalingSpec: &aiplatform.GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecArgs{
MaxReplicaCount: pulumi.String("string"),
MinReplicaCount: pulumi.String("string"),
},
DiskSpec: &aiplatform.GoogleCloudAiplatformV1beta1DiskSpecArgs{
BootDiskSizeGb: pulumi.Int(0),
BootDiskType: pulumi.String("string"),
},
Id: pulumi.String("string"),
ReplicaCount: pulumi.String("string"),
},
},
DisplayName: pulumi.String("string"),
EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1beta1EncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Location: pulumi.String("string"),
Name: pulumi.String("string"),
Network: pulumi.String("string"),
Project: pulumi.String("string"),
ReservedIpRanges: pulumi.StringArray{
pulumi.String("string"),
},
ResourceRuntimeSpec: &aiplatform.GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs{
RaySpec: &aiplatform.GoogleCloudAiplatformV1beta1RaySpecArgs{
HeadNodeResourcePoolId: pulumi.String("string"),
ImageUri: pulumi.String("string"),
ResourcePoolImages: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
ServiceAccountSpec: &aiplatform.GoogleCloudAiplatformV1beta1ServiceAccountSpecArgs{
EnableCustomServiceAccount: pulumi.Bool(false),
ServiceAccount: pulumi.String("string"),
},
},
})

var persistentResourceResource = new PersistentResource("persistentResourceResource", PersistentResourceArgs.builder()        
    .persistentResourceId("string")
    .resourcePools(GoogleCloudAiplatformV1beta1ResourcePoolArgs.builder()
        .machineSpec(GoogleCloudAiplatformV1beta1MachineSpecArgs.builder()
            .acceleratorCount(0)
            .acceleratorType("ACCELERATOR_TYPE_UNSPECIFIED")
            .machineType("string")
            .tpuTopology("string")
            .build())
        .autoscalingSpec(GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecArgs.builder()
            .maxReplicaCount("string")
            .minReplicaCount("string")
            .build())
        .diskSpec(GoogleCloudAiplatformV1beta1DiskSpecArgs.builder()
            .bootDiskSizeGb(0)
            .bootDiskType("string")
            .build())
        .id("string")
        .replicaCount("string")
        .build())
    .displayName("string")
    .encryptionSpec(GoogleCloudAiplatformV1beta1EncryptionSpecArgs.builder()
        .kmsKeyName("string")
        .build())
    .labels(Map.of("string", "string"))
    .location("string")
    .name("string")
    .network("string")
    .project("string")
    .reservedIpRanges("string")
    .resourceRuntimeSpec(GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs.builder()
        .raySpec(GoogleCloudAiplatformV1beta1RaySpecArgs.builder()
            .headNodeResourcePoolId("string")
            .imageUri("string")
            .resourcePoolImages(Map.of("string", "string"))
            .build())
        .serviceAccountSpec(GoogleCloudAiplatformV1beta1ServiceAccountSpecArgs.builder()
            .enableCustomServiceAccount(false)
            .serviceAccount("string")
            .build())
        .build())
    .build());

persistent_resource_resource = google_native.aiplatform.v1beta1.PersistentResource("persistentResourceResource",
    persistent_resource_id="string",
    resource_pools=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ResourcePoolArgs(
        machine_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1MachineSpecArgs(
            accelerator_count=0,
            accelerator_type=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.ACCELERATOR_TYPE_UNSPECIFIED,
            machine_type="string",
            tpu_topology="string",
        ),
        autoscaling_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecArgs(
            max_replica_count="string",
            min_replica_count="string",
        ),
        disk_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1DiskSpecArgs(
            boot_disk_size_gb=0,
            boot_disk_type="string",
        ),
        id="string",
        replica_count="string",
    )],
    display_name="string",
    encryption_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1EncryptionSpecArgs(
        kms_key_name="string",
    ),
    labels={
        "string": "string",
    },
    location="string",
    name="string",
    network="string",
    project="string",
    reserved_ip_ranges=["string"],
    resource_runtime_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs(
        ray_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1RaySpecArgs(
            head_node_resource_pool_id="string",
            image_uri="string",
            resource_pool_images={
                "string": "string",
            },
        ),
        service_account_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ServiceAccountSpecArgs(
            enable_custom_service_account=False,
            service_account="string",
        ),
    ))

const persistentResourceResource = new google_native.aiplatform.v1beta1.PersistentResource("persistentResourceResource", {
    persistentResourceId: "string",
    resourcePools: [{
        machineSpec: {
            acceleratorCount: 0,
            acceleratorType: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
            machineType: "string",
            tpuTopology: "string",
        },
        autoscalingSpec: {
            maxReplicaCount: "string",
            minReplicaCount: "string",
        },
        diskSpec: {
            bootDiskSizeGb: 0,
            bootDiskType: "string",
        },
        id: "string",
        replicaCount: "string",
    }],
    displayName: "string",
    encryptionSpec: {
        kmsKeyName: "string",
    },
    labels: {
        string: "string",
    },
    location: "string",
    name: "string",
    network: "string",
    project: "string",
    reservedIpRanges: ["string"],
    resourceRuntimeSpec: {
        raySpec: {
            headNodeResourcePoolId: "string",
            imageUri: "string",
            resourcePoolImages: {
                string: "string",
            },
        },
        serviceAccountSpec: {
            enableCustomServiceAccount: false,
            serviceAccount: "string",
        },
    },
});

type: google-native:aiplatform/v1beta1:PersistentResource
properties:
    displayName: string
    encryptionSpec:
        kmsKeyName: string
    labels:
        string: string
    location: string
    name: string
    network: string
    persistentResourceId: string
    project: string
    reservedIpRanges:
        - string
    resourcePools:
        - autoscalingSpec:
            maxReplicaCount: string
            minReplicaCount: string
          diskSpec:
            bootDiskSizeGb: 0
            bootDiskType: string
          id: string
          machineSpec:
            acceleratorCount: 0
            acceleratorType: ACCELERATOR_TYPE_UNSPECIFIED
            machineType: string
            tpuTopology: string
          replicaCount: string
    resourceRuntimeSpec:
        raySpec:
            headNodeResourcePoolId: string
            imageUri: string
            resourcePoolImages:
                string: string
        serviceAccountSpec:
            enableCustomServiceAccount: false
            serviceAccount: string

PersistentResource Resource Properties

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

Inputs

The PersistentResource resource accepts the following input properties:

PersistentResourceId string: Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
ResourcePools List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ResourcePool>: The spec of the pools of different resources.
DisplayName string: Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters.
EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpec: Optional. Customer-managed encryption key spec for a PersistentResource. If set, this PersistentResource and all sub-resources of this PersistentResource will be secured by this key.
Labels Dictionary<string, string>: Optional. The labels with user-defined metadata to organize PersistentResource. 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.
Location string
Name string: Immutable. Resource name of a PersistentResource.
Network string: Optional. The full name of the Compute Engine network to peered with Vertex AI to host the persistent resources. 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 resources aren't peered with any network.
Project string
ReservedIpRanges List<string>: Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this persistent resource. If set, we will deploy the persistent resource within the provided IP ranges. Otherwise, the persistent resource is deployed to any IP ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
ResourceRuntimeSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ResourceRuntimeSpec: Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration.

PersistentResourceId string: Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
ResourcePools []GoogleCloudAiplatformV1beta1ResourcePoolArgs: The spec of the pools of different resources.
DisplayName string: Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters.
EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecArgs: Optional. Customer-managed encryption key spec for a PersistentResource. If set, this PersistentResource and all sub-resources of this PersistentResource will be secured by this key.
Labels map[string]string: Optional. The labels with user-defined metadata to organize PersistentResource. 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.
Location string
Name string: Immutable. Resource name of a PersistentResource.
Network string: Optional. The full name of the Compute Engine network to peered with Vertex AI to host the persistent resources. 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 resources aren't peered with any network.
Project string
ReservedIpRanges []string: Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this persistent resource. If set, we will deploy the persistent resource within the provided IP ranges. Otherwise, the persistent resource is deployed to any IP ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
ResourceRuntimeSpec GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs: Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration.

persistentResourceId String: Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
resourcePools List<GoogleCloudAiplatformV1beta1ResourcePool>: The spec of the pools of different resources.
displayName String: Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec: Optional. Customer-managed encryption key spec for a PersistentResource. If set, this PersistentResource and all sub-resources of this PersistentResource will be secured by this key.
labels Map<String,String>: Optional. The labels with user-defined metadata to organize PersistentResource. 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.
location String
name String: Immutable. Resource name of a PersistentResource.
network String: Optional. The full name of the Compute Engine network to peered with Vertex AI to host the persistent resources. 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 resources aren't peered with any network.
project String
reservedIpRanges List<String>: Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this persistent resource. If set, we will deploy the persistent resource within the provided IP ranges. Otherwise, the persistent resource is deployed to any IP ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
resourceRuntimeSpec GoogleCloudAiplatformV1beta1ResourceRuntimeSpec: Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration.

persistentResourceId string: Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
resourcePools GoogleCloudAiplatformV1beta1ResourcePool[]: The spec of the pools of different resources.
displayName string: Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec: Optional. Customer-managed encryption key spec for a PersistentResource. If set, this PersistentResource and all sub-resources of this PersistentResource will be secured by this key.
labels {[key: string]: string}: Optional. The labels with user-defined metadata to organize PersistentResource. 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.
location string
name string: Immutable. Resource name of a PersistentResource.
network string: Optional. The full name of the Compute Engine network to peered with Vertex AI to host the persistent resources. 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 resources aren't peered with any network.
project string
reservedIpRanges string[]: Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this persistent resource. If set, we will deploy the persistent resource within the provided IP ranges. Otherwise, the persistent resource is deployed to any IP ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
resourceRuntimeSpec GoogleCloudAiplatformV1beta1ResourceRuntimeSpec: Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration.

persistent_resource_id str: Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
resource_pools Sequence[GoogleCloudAiplatformV1beta1ResourcePoolArgs]: The spec of the pools of different resources.
display_name str: Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpecArgs: Optional. Customer-managed encryption key spec for a PersistentResource. If set, this PersistentResource and all sub-resources of this PersistentResource will be secured by this key.
labels Mapping[str, str]: Optional. The labels with user-defined metadata to organize PersistentResource. 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.
location str
name str: Immutable. Resource name of a PersistentResource.
network str: Optional. The full name of the Compute Engine network to peered with Vertex AI to host the persistent resources. 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 resources aren't peered with any network.
project str
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 persistent resource. If set, we will deploy the persistent resource within the provided IP ranges. Otherwise, the persistent resource is deployed to any IP ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
resource_runtime_spec GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs: Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration.

persistentResourceId String: Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
resourcePools List<Property Map>: The spec of the pools of different resources.
displayName String: Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec Property Map: Optional. Customer-managed encryption key spec for a PersistentResource. If set, this PersistentResource and all sub-resources of this PersistentResource will be secured by this key.
labels Map<String>: Optional. The labels with user-defined metadata to organize PersistentResource. 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.
location String
name String: Immutable. Resource name of a PersistentResource.
network String: Optional. The full name of the Compute Engine network to peered with Vertex AI to host the persistent resources. 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 resources aren't peered with any network.
project String
reservedIpRanges List<String>: Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this persistent resource. If set, we will deploy the persistent resource within the provided IP ranges. Otherwise, the persistent resource is deployed to any IP ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
resourceRuntimeSpec Property Map: Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration.

Outputs

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

CreateTime string: Time when the PersistentResource was created.
Error Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse: Only populated when persistent resource's state is STOPPING or ERROR.
Id string: The provider-assigned unique ID for this managed resource.
ResourceRuntime Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ResourceRuntimeResponse: Runtime information of the Persistent Resource.
StartTime string: Time when the PersistentResource for the first time entered the RUNNING state.
State string: The detailed state of a Study.
UpdateTime string: Time when the PersistentResource was most recently updated.

CreateTime string: Time when the PersistentResource was created.
Error GoogleRpcStatusResponse: Only populated when persistent resource's state is STOPPING or ERROR.
Id string: The provider-assigned unique ID for this managed resource.
ResourceRuntime GoogleCloudAiplatformV1beta1ResourceRuntimeResponse: Runtime information of the Persistent Resource.
StartTime string: Time when the PersistentResource for the first time entered the RUNNING state.
State string: The detailed state of a Study.
UpdateTime string: Time when the PersistentResource was most recently updated.

createTime String: Time when the PersistentResource was created.
error GoogleRpcStatusResponse: Only populated when persistent resource's state is STOPPING or ERROR.
id String: The provider-assigned unique ID for this managed resource.
resourceRuntime GoogleCloudAiplatformV1beta1ResourceRuntimeResponse: Runtime information of the Persistent Resource.
startTime String: Time when the PersistentResource for the first time entered the RUNNING state.
state String: The detailed state of a Study.
updateTime String: Time when the PersistentResource was most recently updated.

createTime string: Time when the PersistentResource was created.
error GoogleRpcStatusResponse: Only populated when persistent resource's state is STOPPING or ERROR.
id string: The provider-assigned unique ID for this managed resource.
resourceRuntime GoogleCloudAiplatformV1beta1ResourceRuntimeResponse: Runtime information of the Persistent Resource.
startTime string: Time when the PersistentResource for the first time entered the RUNNING state.
state string: The detailed state of a Study.
updateTime string: Time when the PersistentResource was most recently updated.

create_time str: Time when the PersistentResource was created.
error GoogleRpcStatusResponse: Only populated when persistent resource's state is STOPPING or ERROR.
id str: The provider-assigned unique ID for this managed resource.
resource_runtime GoogleCloudAiplatformV1beta1ResourceRuntimeResponse: Runtime information of the Persistent Resource.
start_time str: Time when the PersistentResource for the first time entered the RUNNING state.
state str: The detailed state of a Study.
update_time str: Time when the PersistentResource was most recently updated.

createTime String: Time when the PersistentResource was created.
error Property Map: Only populated when persistent resource's state is STOPPING or ERROR.
id String: The provider-assigned unique ID for this managed resource.
resourceRuntime Property Map: Runtime information of the Persistent Resource.
startTime String: Time when the PersistentResource for the first time entered the RUNNING state.
state String: The detailed state of a Study.
updateTime String: Time when the PersistentResource was most recently updated.

Supporting Types

GoogleCloudAiplatformV1beta1DiskSpec, GoogleCloudAiplatformV1beta1DiskSpecArgs

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).

GoogleCloudAiplatformV1beta1DiskSpecResponse, GoogleCloudAiplatformV1beta1DiskSpecResponseArgs

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).

GoogleCloudAiplatformV1beta1EncryptionSpec, GoogleCloudAiplatformV1beta1EncryptionSpecArgs

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.

GoogleCloudAiplatformV1beta1EncryptionSpecResponse, GoogleCloudAiplatformV1beta1EncryptionSpecResponseArgs

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.

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").

GoogleCloudAiplatformV1beta1RaySpec, GoogleCloudAiplatformV1beta1RaySpecArgs

HeadNodeResourcePoolId string: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
ImageUri string: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
ResourcePoolImages Dictionary<string, string>: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

HeadNodeResourcePoolId string: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
ImageUri string: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
ResourcePoolImages map[string]string: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

headNodeResourcePoolId String: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
imageUri String: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
resourcePoolImages Map<String,String>: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

headNodeResourcePoolId string: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
imageUri string: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
resourcePoolImages {[key: string]: string}: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

head_node_resource_pool_id str: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
image_uri str: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
resource_pool_images Mapping[str, str]: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

headNodeResourcePoolId String: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
imageUri String: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
resourcePoolImages Map<String>: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

GoogleCloudAiplatformV1beta1RaySpecResponse, GoogleCloudAiplatformV1beta1RaySpecResponseArgs

HeadNodeResourcePoolId string: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
ImageUri string: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
ResourcePoolImages Dictionary<string, string>: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

HeadNodeResourcePoolId string: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
ImageUri string: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
ResourcePoolImages map[string]string: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

headNodeResourcePoolId String: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
imageUri String: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
resourcePoolImages Map<String,String>: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

headNodeResourcePoolId string: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
imageUri string: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
resourcePoolImages {[key: string]: string}: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

head_node_resource_pool_id str: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
image_uri str: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
resource_pool_images Mapping[str, str]: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

headNodeResourcePoolId String: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
imageUri String: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
resourcePoolImages Map<String>: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

GoogleCloudAiplatformV1beta1ResourcePool, GoogleCloudAiplatformV1beta1ResourcePoolArgs

MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpec: Immutable. The specification of a single machine.
AutoscalingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec: Optional. Optional spec to configure GKE autoscaling
DiskSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1DiskSpec: Optional. Disk spec for the machine in this node pool.
Id string: Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
ReplicaCount string: Optional. The total number of machines to use for this resource pool.

MachineSpec GoogleCloudAiplatformV1beta1MachineSpec: Immutable. The specification of a single machine.
AutoscalingSpec GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec: Optional. Optional spec to configure GKE autoscaling
DiskSpec GoogleCloudAiplatformV1beta1DiskSpec: Optional. Disk spec for the machine in this node pool.
Id string: Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
ReplicaCount string: Optional. The total number of machines to use for this resource pool.

machineSpec GoogleCloudAiplatformV1beta1MachineSpec: Immutable. The specification of a single machine.
autoscalingSpec GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec: Optional. Optional spec to configure GKE autoscaling
diskSpec GoogleCloudAiplatformV1beta1DiskSpec: Optional. Disk spec for the machine in this node pool.
id String: Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
replicaCount String: Optional. The total number of machines to use for this resource pool.

machineSpec GoogleCloudAiplatformV1beta1MachineSpec: Immutable. The specification of a single machine.
autoscalingSpec GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec: Optional. Optional spec to configure GKE autoscaling
diskSpec GoogleCloudAiplatformV1beta1DiskSpec: Optional. Disk spec for the machine in this node pool.
id string: Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
replicaCount string: Optional. The total number of machines to use for this resource pool.

machine_spec GoogleCloudAiplatformV1beta1MachineSpec: Immutable. The specification of a single machine.
autoscaling_spec GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec: Optional. Optional spec to configure GKE autoscaling
disk_spec GoogleCloudAiplatformV1beta1DiskSpec: Optional. Disk spec for the machine in this node pool.
id str: Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
replica_count str: Optional. The total number of machines to use for this resource pool.

machineSpec Property Map: Immutable. The specification of a single machine.
autoscalingSpec Property Map: Optional. Optional spec to configure GKE autoscaling
diskSpec Property Map: Optional. Disk spec for the machine in this node pool.
id String: Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
replicaCount String: Optional. The total number of machines to use for this resource pool.

GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec, GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecArgs

MaxReplicaCount string: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
MinReplicaCount string: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

MaxReplicaCount string: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
MinReplicaCount string: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

maxReplicaCount String: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
minReplicaCount String: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

maxReplicaCount string: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
minReplicaCount string: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

max_replica_count str: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
min_replica_count str: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

maxReplicaCount String: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
minReplicaCount String: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponse, GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponseArgs

MaxReplicaCount string: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
MinReplicaCount string: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

MaxReplicaCount string: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
MinReplicaCount string: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

maxReplicaCount String: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
minReplicaCount String: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

maxReplicaCount string: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
minReplicaCount string: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

max_replica_count str: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
min_replica_count str: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

maxReplicaCount String: Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
minReplicaCount String: Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

GoogleCloudAiplatformV1beta1ResourcePoolResponse, GoogleCloudAiplatformV1beta1ResourcePoolResponseArgs

AutoscalingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponse: Optional. Optional spec to configure GKE autoscaling
DiskSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1DiskSpecResponse: Optional. Disk spec for the machine in this node pool.
MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
ReplicaCount string: Optional. The total number of machines to use for this resource pool.
UsedReplicaCount string: The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count.

AutoscalingSpec GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponse: Optional. Optional spec to configure GKE autoscaling
DiskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse: Optional. Disk spec for the machine in this node pool.
MachineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
ReplicaCount string: Optional. The total number of machines to use for this resource pool.
UsedReplicaCount string: The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count.

autoscalingSpec GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponse: Optional. Optional spec to configure GKE autoscaling
diskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse: Optional. Disk spec for the machine in this node pool.
machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
replicaCount String: Optional. The total number of machines to use for this resource pool.
usedReplicaCount String: The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count.

autoscalingSpec GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponse: Optional. Optional spec to configure GKE autoscaling
diskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse: Optional. Disk spec for the machine in this node pool.
machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
replicaCount string: Optional. The total number of machines to use for this resource pool.
usedReplicaCount string: The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count.

autoscaling_spec GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponse: Optional. Optional spec to configure GKE autoscaling
disk_spec GoogleCloudAiplatformV1beta1DiskSpecResponse: Optional. Disk spec for the machine in this node pool.
machine_spec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
replica_count str: Optional. The total number of machines to use for this resource pool.
used_replica_count str: The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count.

autoscalingSpec Property Map: Optional. Optional spec to configure GKE autoscaling
diskSpec Property Map: Optional. Disk spec for the machine in this node pool.
machineSpec Property Map: Immutable. The specification of a single machine.
replicaCount String: Optional. The total number of machines to use for this resource pool.
usedReplicaCount String: The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count.

GoogleCloudAiplatformV1beta1ResourceRuntimeResponse, GoogleCloudAiplatformV1beta1ResourceRuntimeResponseArgs

AccessUris Dictionary<string, string>: URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" }
NotebookRuntimeTemplate string: The resource name of NotebookRuntimeTemplate for the RoV Persistent Cluster The NotebokRuntimeTemplate is created in the same VPC (if set), and with the same Ray and Python version as the Persistent Cluster. Example: "projects/1000/locations/us-central1/notebookRuntimeTemplates/abc123"

AccessUris map[string]string: URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" }
NotebookRuntimeTemplate string: The resource name of NotebookRuntimeTemplate for the RoV Persistent Cluster The NotebokRuntimeTemplate is created in the same VPC (if set), and with the same Ray and Python version as the Persistent Cluster. Example: "projects/1000/locations/us-central1/notebookRuntimeTemplates/abc123"

accessUris Map<String,String>: URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" }
notebookRuntimeTemplate String: The resource name of NotebookRuntimeTemplate for the RoV Persistent Cluster The NotebokRuntimeTemplate is created in the same VPC (if set), and with the same Ray and Python version as the Persistent Cluster. Example: "projects/1000/locations/us-central1/notebookRuntimeTemplates/abc123"

accessUris {[key: string]: string}: URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" }
notebookRuntimeTemplate string: The resource name of NotebookRuntimeTemplate for the RoV Persistent Cluster The NotebokRuntimeTemplate is created in the same VPC (if set), and with the same Ray and Python version as the Persistent Cluster. Example: "projects/1000/locations/us-central1/notebookRuntimeTemplates/abc123"

access_uris Mapping[str, str]: URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" }
notebook_runtime_template str: The resource name of NotebookRuntimeTemplate for the RoV Persistent Cluster The NotebokRuntimeTemplate is created in the same VPC (if set), and with the same Ray and Python version as the Persistent Cluster. Example: "projects/1000/locations/us-central1/notebookRuntimeTemplates/abc123"

accessUris Map<String>: URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" }
notebookRuntimeTemplate String: The resource name of NotebookRuntimeTemplate for the RoV Persistent Cluster The NotebokRuntimeTemplate is created in the same VPC (if set), and with the same Ray and Python version as the Persistent Cluster. Example: "projects/1000/locations/us-central1/notebookRuntimeTemplates/abc123"

GoogleCloudAiplatformV1beta1ResourceRuntimeSpec, GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs

RaySpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1RaySpec: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
ServiceAccountSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ServiceAccountSpec: Optional. Configure the use of workload identity on the PersistentResource

RaySpec GoogleCloudAiplatformV1beta1RaySpec: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
ServiceAccountSpec GoogleCloudAiplatformV1beta1ServiceAccountSpec: Optional. Configure the use of workload identity on the PersistentResource

raySpec GoogleCloudAiplatformV1beta1RaySpec: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
serviceAccountSpec GoogleCloudAiplatformV1beta1ServiceAccountSpec: Optional. Configure the use of workload identity on the PersistentResource

raySpec GoogleCloudAiplatformV1beta1RaySpec: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
serviceAccountSpec GoogleCloudAiplatformV1beta1ServiceAccountSpec: Optional. Configure the use of workload identity on the PersistentResource

ray_spec GoogleCloudAiplatformV1beta1RaySpec: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
service_account_spec GoogleCloudAiplatformV1beta1ServiceAccountSpec: Optional. Configure the use of workload identity on the PersistentResource

raySpec Property Map: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
serviceAccountSpec Property Map: Optional. Configure the use of workload identity on the PersistentResource

GoogleCloudAiplatformV1beta1ResourceRuntimeSpecResponse, GoogleCloudAiplatformV1beta1ResourceRuntimeSpecResponseArgs

RaySpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1RaySpecResponse: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
ServiceAccountSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ServiceAccountSpecResponse: Optional. Configure the use of workload identity on the PersistentResource

RaySpec GoogleCloudAiplatformV1beta1RaySpecResponse: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
ServiceAccountSpec GoogleCloudAiplatformV1beta1ServiceAccountSpecResponse: Optional. Configure the use of workload identity on the PersistentResource

raySpec GoogleCloudAiplatformV1beta1RaySpecResponse: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
serviceAccountSpec GoogleCloudAiplatformV1beta1ServiceAccountSpecResponse: Optional. Configure the use of workload identity on the PersistentResource

raySpec GoogleCloudAiplatformV1beta1RaySpecResponse: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
serviceAccountSpec GoogleCloudAiplatformV1beta1ServiceAccountSpecResponse: Optional. Configure the use of workload identity on the PersistentResource

ray_spec GoogleCloudAiplatformV1beta1RaySpecResponse: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
service_account_spec GoogleCloudAiplatformV1beta1ServiceAccountSpecResponse: Optional. Configure the use of workload identity on the PersistentResource

raySpec Property Map: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
serviceAccountSpec Property Map: Optional. Configure the use of workload identity on the PersistentResource

GoogleCloudAiplatformV1beta1ServiceAccountSpec, GoogleCloudAiplatformV1beta1ServiceAccountSpecArgs

EnableCustomServiceAccount bool: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
ServiceAccount string: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

EnableCustomServiceAccount bool: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
ServiceAccount string: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

enableCustomServiceAccount Boolean: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
serviceAccount String: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

enableCustomServiceAccount boolean: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
serviceAccount string: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

enable_custom_service_account bool: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
service_account str: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

enableCustomServiceAccount Boolean: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
serviceAccount String: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

GoogleCloudAiplatformV1beta1ServiceAccountSpecResponse, GoogleCloudAiplatformV1beta1ServiceAccountSpecResponseArgs

EnableCustomServiceAccount bool: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
ServiceAccount string: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

EnableCustomServiceAccount bool: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
ServiceAccount string: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

enableCustomServiceAccount Boolean: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
serviceAccount String: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

enableCustomServiceAccount boolean: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
serviceAccount string: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

enable_custom_service_account bool: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
service_account str: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

enableCustomServiceAccount Boolean: If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
serviceAccount String: Optional. Default service account that this PersistentResource's workloads run as. The workloads include: * Any runtime specified via ResourceRuntimeSpec on creation time, for example, Ray. * Jobs submitted to PersistentResource, if no other service account specified in the job specs. Only works when custom service account is enabled and users have the iam.serviceAccounts.actAs permission on this service account. Required if any containers are specified in ResourceRuntimeSpec.

GoogleRpcStatusResponse, GoogleRpcStatusResponseArgs

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/v1beta1.PersistentResource

On this page

On this page

Create PersistentResource Resource

Constructor syntax

Parameters

Example

PersistentResource Resource Properties

Inputs

Outputs

Supporting Types

GoogleCloudAiplatformV1beta1DiskSpec, GoogleCloudAiplatformV1beta1DiskSpecArgs

GoogleCloudAiplatformV1beta1DiskSpecResponse, GoogleCloudAiplatformV1beta1DiskSpecResponseArgs

GoogleCloudAiplatformV1beta1EncryptionSpec, GoogleCloudAiplatformV1beta1EncryptionSpecArgs

GoogleCloudAiplatformV1beta1EncryptionSpecResponse, GoogleCloudAiplatformV1beta1EncryptionSpecResponseArgs

GoogleCloudAiplatformV1beta1MachineSpec, GoogleCloudAiplatformV1beta1MachineSpecArgs

GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType, GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeArgs

GoogleCloudAiplatformV1beta1MachineSpecResponse, GoogleCloudAiplatformV1beta1MachineSpecResponseArgs

GoogleCloudAiplatformV1beta1RaySpec, GoogleCloudAiplatformV1beta1RaySpecArgs

GoogleCloudAiplatformV1beta1RaySpecResponse, GoogleCloudAiplatformV1beta1RaySpecResponseArgs

GoogleCloudAiplatformV1beta1ResourcePool, GoogleCloudAiplatformV1beta1ResourcePoolArgs

GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec, GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecArgs

GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponse, GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpecResponseArgs

GoogleCloudAiplatformV1beta1ResourcePoolResponse, GoogleCloudAiplatformV1beta1ResourcePoolResponseArgs

GoogleCloudAiplatformV1beta1ResourceRuntimeResponse, GoogleCloudAiplatformV1beta1ResourceRuntimeResponseArgs

GoogleCloudAiplatformV1beta1ResourceRuntimeSpec, GoogleCloudAiplatformV1beta1ResourceRuntimeSpecArgs

GoogleCloudAiplatformV1beta1ResourceRuntimeSpecResponse, GoogleCloudAiplatformV1beta1ResourceRuntimeSpecResponseArgs

GoogleCloudAiplatformV1beta1ServiceAccountSpec, GoogleCloudAiplatformV1beta1ServiceAccountSpecArgs

GoogleCloudAiplatformV1beta1ServiceAccountSpecResponse, GoogleCloudAiplatformV1beta1ServiceAccountSpecResponseArgs

GoogleRpcStatusResponse, GoogleRpcStatusResponseArgs

Package Details

On this page

On this page