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/v1.CustomJob

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

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

pulumi/pulumi-google-native

Create CustomJob Resource

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

Constructor syntax

new CustomJob(name: string, args: CustomJobArgs, opts?: CustomResourceOptions);

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

@overload
def CustomJob(resource_name: str,
              opts: Optional[ResourceOptions] = None,
              display_name: Optional[str] = None,
              job_spec: Optional[GoogleCloudAiplatformV1CustomJobSpecArgs] = None,
              encryption_spec: Optional[GoogleCloudAiplatformV1EncryptionSpecArgs] = None,
              labels: Optional[Mapping[str, str]] = None,
              location: Optional[str] = None,
              project: Optional[str] = None)

func NewCustomJob(ctx *Context, name string, args CustomJobArgs, opts ...ResourceOption) (*CustomJob, error)

public CustomJob(string name, CustomJobArgs args, CustomResourceOptions? opts = null)

public CustomJob(String name, CustomJobArgs args)
public CustomJob(String name, CustomJobArgs args, CustomResourceOptions options)

type: google-native:aiplatform/v1:CustomJob
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 CustomJobArgs: 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 CustomJobArgs: 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 CustomJobArgs: The arguments to resource properties.
opts ResourceOption: Bag of options to control resource's behavior.

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

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

Constructor example

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

var customJobResource = new GoogleNative.Aiplatform.V1.CustomJob("customJobResource", new()
{
    DisplayName = "string",
    JobSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1CustomJobSpecArgs
    {
        WorkerPoolSpecs = new[]
        {
            new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1WorkerPoolSpecArgs
            {
                ContainerSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ContainerSpecArgs
                {
                    ImageUri = "string",
                    Args = new[]
                    {
                        "string",
                    },
                    Command = new[]
                    {
                        "string",
                    },
                    Env = new[]
                    {
                        new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarArgs
                        {
                            Name = "string",
                            Value = "string",
                        },
                    },
                },
                DiskSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1DiskSpecArgs
                {
                    BootDiskSizeGb = 0,
                    BootDiskType = "string",
                },
                MachineSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1MachineSpecArgs
                {
                    AcceleratorCount = 0,
                    AcceleratorType = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
                    MachineType = "string",
                    TpuTopology = "string",
                },
                NfsMounts = new[]
                {
                    new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1NfsMountArgs
                    {
                        MountPoint = "string",
                        Path = "string",
                        Server = "string",
                    },
                },
                PythonPackageSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PythonPackageSpecArgs
                {
                    ExecutorImageUri = "string",
                    PackageUris = new[]
                    {
                        "string",
                    },
                    PythonModule = "string",
                    Args = new[]
                    {
                        "string",
                    },
                    Env = new[]
                    {
                        new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarArgs
                        {
                            Name = "string",
                            Value = "string",
                        },
                    },
                },
                ReplicaCount = "string",
            },
        },
        BaseOutputDirectory = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationArgs
        {
            OutputUriPrefix = "string",
        },
        EnableDashboardAccess = false,
        EnableWebAccess = false,
        Experiment = "string",
        ExperimentRun = "string",
        Network = "string",
        ProtectedArtifactLocationId = "string",
        ReservedIpRanges = new[]
        {
            "string",
        },
        Scheduling = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SchedulingArgs
        {
            DisableRetries = false,
            RestartJobOnWorkerRestart = false,
            Timeout = "string",
        },
        ServiceAccount = "string",
        Tensorboard = "string",
    },
    EncryptionSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpecArgs
    {
        KmsKeyName = "string",
    },
    Labels = 
    {
        { "string", "string" },
    },
    Location = "string",
    Project = "string",
});

example, err := aiplatform.NewCustomJob(ctx, "customJobResource", &aiplatform.CustomJobArgs{
DisplayName: pulumi.String("string"),
JobSpec: &aiplatform.GoogleCloudAiplatformV1CustomJobSpecArgs{
WorkerPoolSpecs: aiplatform.GoogleCloudAiplatformV1WorkerPoolSpecArray{
&aiplatform.GoogleCloudAiplatformV1WorkerPoolSpecArgs{
ContainerSpec: &aiplatform.GoogleCloudAiplatformV1ContainerSpecArgs{
ImageUri: pulumi.String("string"),
Args: pulumi.StringArray{
pulumi.String("string"),
},
Command: pulumi.StringArray{
pulumi.String("string"),
},
Env: aiplatform.GoogleCloudAiplatformV1EnvVarArray{
&aiplatform.GoogleCloudAiplatformV1EnvVarArgs{
Name: pulumi.String("string"),
Value: pulumi.String("string"),
},
},
},
DiskSpec: &aiplatform.GoogleCloudAiplatformV1DiskSpecArgs{
BootDiskSizeGb: pulumi.Int(0),
BootDiskType: pulumi.String("string"),
},
MachineSpec: &aiplatform.GoogleCloudAiplatformV1MachineSpecArgs{
AcceleratorCount: pulumi.Int(0),
AcceleratorType: aiplatform.GoogleCloudAiplatformV1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified,
MachineType: pulumi.String("string"),
TpuTopology: pulumi.String("string"),
},
NfsMounts: aiplatform.GoogleCloudAiplatformV1NfsMountArray{
&aiplatform.GoogleCloudAiplatformV1NfsMountArgs{
MountPoint: pulumi.String("string"),
Path: pulumi.String("string"),
Server: pulumi.String("string"),
},
},
PythonPackageSpec: &aiplatform.GoogleCloudAiplatformV1PythonPackageSpecArgs{
ExecutorImageUri: pulumi.String("string"),
PackageUris: pulumi.StringArray{
pulumi.String("string"),
},
PythonModule: pulumi.String("string"),
Args: pulumi.StringArray{
pulumi.String("string"),
},
Env: aiplatform.GoogleCloudAiplatformV1EnvVarArray{
&aiplatform.GoogleCloudAiplatformV1EnvVarArgs{
Name: pulumi.String("string"),
Value: pulumi.String("string"),
},
},
},
ReplicaCount: pulumi.String("string"),
},
},
BaseOutputDirectory: &aiplatform.GoogleCloudAiplatformV1GcsDestinationArgs{
OutputUriPrefix: pulumi.String("string"),
},
EnableDashboardAccess: pulumi.Bool(false),
EnableWebAccess: pulumi.Bool(false),
Experiment: pulumi.String("string"),
ExperimentRun: pulumi.String("string"),
Network: pulumi.String("string"),
ProtectedArtifactLocationId: pulumi.String("string"),
ReservedIpRanges: pulumi.StringArray{
pulumi.String("string"),
},
Scheduling: &aiplatform.GoogleCloudAiplatformV1SchedulingArgs{
DisableRetries: pulumi.Bool(false),
RestartJobOnWorkerRestart: pulumi.Bool(false),
Timeout: pulumi.String("string"),
},
ServiceAccount: pulumi.String("string"),
Tensorboard: pulumi.String("string"),
},
EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1EncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Location: pulumi.String("string"),
Project: pulumi.String("string"),
})

var customJobResource = new CustomJob("customJobResource", CustomJobArgs.builder()
    .displayName("string")
    .jobSpec(GoogleCloudAiplatformV1CustomJobSpecArgs.builder()
        .workerPoolSpecs(GoogleCloudAiplatformV1WorkerPoolSpecArgs.builder()
            .containerSpec(GoogleCloudAiplatformV1ContainerSpecArgs.builder()
                .imageUri("string")
                .args("string")
                .command("string")
                .env(GoogleCloudAiplatformV1EnvVarArgs.builder()
                    .name("string")
                    .value("string")
                    .build())
                .build())
            .diskSpec(GoogleCloudAiplatformV1DiskSpecArgs.builder()
                .bootDiskSizeGb(0)
                .bootDiskType("string")
                .build())
            .machineSpec(GoogleCloudAiplatformV1MachineSpecArgs.builder()
                .acceleratorCount(0)
                .acceleratorType("ACCELERATOR_TYPE_UNSPECIFIED")
                .machineType("string")
                .tpuTopology("string")
                .build())
            .nfsMounts(GoogleCloudAiplatformV1NfsMountArgs.builder()
                .mountPoint("string")
                .path("string")
                .server("string")
                .build())
            .pythonPackageSpec(GoogleCloudAiplatformV1PythonPackageSpecArgs.builder()
                .executorImageUri("string")
                .packageUris("string")
                .pythonModule("string")
                .args("string")
                .env(GoogleCloudAiplatformV1EnvVarArgs.builder()
                    .name("string")
                    .value("string")
                    .build())
                .build())
            .replicaCount("string")
            .build())
        .baseOutputDirectory(GoogleCloudAiplatformV1GcsDestinationArgs.builder()
            .outputUriPrefix("string")
            .build())
        .enableDashboardAccess(false)
        .enableWebAccess(false)
        .experiment("string")
        .experimentRun("string")
        .network("string")
        .protectedArtifactLocationId("string")
        .reservedIpRanges("string")
        .scheduling(GoogleCloudAiplatformV1SchedulingArgs.builder()
            .disableRetries(false)
            .restartJobOnWorkerRestart(false)
            .timeout("string")
            .build())
        .serviceAccount("string")
        .tensorboard("string")
        .build())
    .encryptionSpec(GoogleCloudAiplatformV1EncryptionSpecArgs.builder()
        .kmsKeyName("string")
        .build())
    .labels(Map.of("string", "string"))
    .location("string")
    .project("string")
    .build());

custom_job_resource = google_native.aiplatform.v1.CustomJob("customJobResource",
    display_name="string",
    job_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1CustomJobSpecArgs(
        worker_pool_specs=[google_native.aiplatform.v1.GoogleCloudAiplatformV1WorkerPoolSpecArgs(
            container_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1ContainerSpecArgs(
                image_uri="string",
                args=["string"],
                command=["string"],
                env=[google_native.aiplatform.v1.GoogleCloudAiplatformV1EnvVarArgs(
                    name="string",
                    value="string",
                )],
            ),
            disk_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1DiskSpecArgs(
                boot_disk_size_gb=0,
                boot_disk_type="string",
            ),
            machine_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1MachineSpecArgs(
                accelerator_count=0,
                accelerator_type=google_native.aiplatform.v1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.ACCELERATOR_TYPE_UNSPECIFIED,
                machine_type="string",
                tpu_topology="string",
            ),
            nfs_mounts=[google_native.aiplatform.v1.GoogleCloudAiplatformV1NfsMountArgs(
                mount_point="string",
                path="string",
                server="string",
            )],
            python_package_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1PythonPackageSpecArgs(
                executor_image_uri="string",
                package_uris=["string"],
                python_module="string",
                args=["string"],
                env=[google_native.aiplatform.v1.GoogleCloudAiplatformV1EnvVarArgs(
                    name="string",
                    value="string",
                )],
            ),
            replica_count="string",
        )],
        base_output_directory=google_native.aiplatform.v1.GoogleCloudAiplatformV1GcsDestinationArgs(
            output_uri_prefix="string",
        ),
        enable_dashboard_access=False,
        enable_web_access=False,
        experiment="string",
        experiment_run="string",
        network="string",
        protected_artifact_location_id="string",
        reserved_ip_ranges=["string"],
        scheduling=google_native.aiplatform.v1.GoogleCloudAiplatformV1SchedulingArgs(
            disable_retries=False,
            restart_job_on_worker_restart=False,
            timeout="string",
        ),
        service_account="string",
        tensorboard="string",
    ),
    encryption_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1EncryptionSpecArgs(
        kms_key_name="string",
    ),
    labels={
        "string": "string",
    },
    location="string",
    project="string")

const customJobResource = new google_native.aiplatform.v1.CustomJob("customJobResource", {
    displayName: "string",
    jobSpec: {
        workerPoolSpecs: [{
            containerSpec: {
                imageUri: "string",
                args: ["string"],
                command: ["string"],
                env: [{
                    name: "string",
                    value: "string",
                }],
            },
            diskSpec: {
                bootDiskSizeGb: 0,
                bootDiskType: "string",
            },
            machineSpec: {
                acceleratorCount: 0,
                acceleratorType: google_native.aiplatform.v1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
                machineType: "string",
                tpuTopology: "string",
            },
            nfsMounts: [{
                mountPoint: "string",
                path: "string",
                server: "string",
            }],
            pythonPackageSpec: {
                executorImageUri: "string",
                packageUris: ["string"],
                pythonModule: "string",
                args: ["string"],
                env: [{
                    name: "string",
                    value: "string",
                }],
            },
            replicaCount: "string",
        }],
        baseOutputDirectory: {
            outputUriPrefix: "string",
        },
        enableDashboardAccess: false,
        enableWebAccess: false,
        experiment: "string",
        experimentRun: "string",
        network: "string",
        protectedArtifactLocationId: "string",
        reservedIpRanges: ["string"],
        scheduling: {
            disableRetries: false,
            restartJobOnWorkerRestart: false,
            timeout: "string",
        },
        serviceAccount: "string",
        tensorboard: "string",
    },
    encryptionSpec: {
        kmsKeyName: "string",
    },
    labels: {
        string: "string",
    },
    location: "string",
    project: "string",
});

type: google-native:aiplatform/v1:CustomJob
properties:
    displayName: string
    encryptionSpec:
        kmsKeyName: string
    jobSpec:
        baseOutputDirectory:
            outputUriPrefix: string
        enableDashboardAccess: false
        enableWebAccess: false
        experiment: string
        experimentRun: string
        network: string
        protectedArtifactLocationId: string
        reservedIpRanges:
            - string
        scheduling:
            disableRetries: false
            restartJobOnWorkerRestart: false
            timeout: string
        serviceAccount: string
        tensorboard: string
        workerPoolSpecs:
            - containerSpec:
                args:
                    - string
                command:
                    - string
                env:
                    - name: string
                      value: string
                imageUri: string
              diskSpec:
                bootDiskSizeGb: 0
                bootDiskType: string
              machineSpec:
                acceleratorCount: 0
                acceleratorType: ACCELERATOR_TYPE_UNSPECIFIED
                machineType: string
                tpuTopology: string
              nfsMounts:
                - mountPoint: string
                  path: string
                  server: string
              pythonPackageSpec:
                args:
                    - string
                env:
                    - name: string
                      value: string
                executorImageUri: string
                packageUris:
                    - string
                pythonModule: string
              replicaCount: string
    labels:
        string: string
    location: string
    project: string

CustomJob 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 CustomJob resource accepts the following input properties:

DisplayName string: The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
JobSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1CustomJobSpec: Job spec.
EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpec: Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.
Labels Dictionary<string, string>: The labels with user-defined metadata to organize CustomJobs. 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
Project string

DisplayName string: The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
JobSpec GoogleCloudAiplatformV1CustomJobSpecArgs: Job spec.
EncryptionSpec GoogleCloudAiplatformV1EncryptionSpecArgs: Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.
Labels map[string]string: The labels with user-defined metadata to organize CustomJobs. 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
Project string

displayName String: The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
jobSpec GoogleCloudAiplatformV1CustomJobSpec: Job spec.
encryptionSpec GoogleCloudAiplatformV1EncryptionSpec: Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.
labels Map<String,String>: The labels with user-defined metadata to organize CustomJobs. 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
project String

displayName string: The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
jobSpec GoogleCloudAiplatformV1CustomJobSpec: Job spec.
encryptionSpec GoogleCloudAiplatformV1EncryptionSpec: Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.
labels {[key: string]: string}: The labels with user-defined metadata to organize CustomJobs. 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
project string

display_name str: The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
job_spec GoogleCloudAiplatformV1CustomJobSpecArgs: Job spec.
encryption_spec GoogleCloudAiplatformV1EncryptionSpecArgs: Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.
labels Mapping[str, str]: The labels with user-defined metadata to organize CustomJobs. 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
project str

displayName String: The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
jobSpec Property Map: Job spec.
encryptionSpec Property Map: Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.
labels Map<String>: The labels with user-defined metadata to organize CustomJobs. 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
project String

Outputs

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

CreateTime string: Time when the CustomJob was created.
EndTime string: Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
Error Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleRpcStatusResponse: Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
Id string: The provider-assigned unique ID for this managed resource.
Name string: Resource name of a CustomJob.
StartTime string: Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.
State string: The detailed state of the job.
UpdateTime string: Time when the CustomJob was most recently updated.
WebAccessUris Dictionary<string, string>: URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

CreateTime string: Time when the CustomJob was created.
EndTime string: Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
Error GoogleRpcStatusResponse: Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
Id string: The provider-assigned unique ID for this managed resource.
Name string: Resource name of a CustomJob.
StartTime string: Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.
State string: The detailed state of the job.
UpdateTime string: Time when the CustomJob was most recently updated.
WebAccessUris map[string]string: URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

createTime String: Time when the CustomJob was created.
endTime String: Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse: Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id String: The provider-assigned unique ID for this managed resource.
name String: Resource name of a CustomJob.
startTime String: Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.
state String: The detailed state of the job.
updateTime String: Time when the CustomJob was most recently updated.
webAccessUris Map<String,String>: URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

createTime string: Time when the CustomJob was created.
endTime string: Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse: Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id string: The provider-assigned unique ID for this managed resource.
name string: Resource name of a CustomJob.
startTime string: Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.
state string: The detailed state of the job.
updateTime string: Time when the CustomJob was most recently updated.
webAccessUris {[key: string]: string}: URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

create_time str: Time when the CustomJob was created.
end_time str: Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse: Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id str: The provider-assigned unique ID for this managed resource.
name str: Resource name of a CustomJob.
start_time str: Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.
state str: The detailed state of the job.
update_time str: Time when the CustomJob was most recently updated.
web_access_uris Mapping[str, str]: URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

createTime String: Time when the CustomJob was created.
endTime String: Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error Property Map: Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id String: The provider-assigned unique ID for this managed resource.
name String: Resource name of a CustomJob.
startTime String: Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.
state String: The detailed state of the job.
updateTime String: Time when the CustomJob was most recently updated.
webAccessUris Map<String>: URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

Supporting Types

GoogleCloudAiplatformV1ContainerSpec, GoogleCloudAiplatformV1ContainerSpecArgs

ImageUri string: The URI of a container image in the Container Registry that is to be run on each worker replica.
Args List<string>: The arguments to be passed when starting the container.
Command List<string>: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
Env List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVar>: Environment variables to be passed to the container. Maximum limit is 100.

ImageUri string: The URI of a container image in the Container Registry that is to be run on each worker replica.
Args []string: The arguments to be passed when starting the container.
Command []string: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
Env []GoogleCloudAiplatformV1EnvVar: Environment variables to be passed to the container. Maximum limit is 100.

imageUri String: The URI of a container image in the Container Registry that is to be run on each worker replica.
args List<String>: The arguments to be passed when starting the container.
command List<String>: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env List<GoogleCloudAiplatformV1EnvVar>: Environment variables to be passed to the container. Maximum limit is 100.

imageUri string: The URI of a container image in the Container Registry that is to be run on each worker replica.
args string[]: The arguments to be passed when starting the container.
command string[]: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env GoogleCloudAiplatformV1EnvVar[]: Environment variables to be passed to the container. Maximum limit is 100.

image_uri str: The URI of a container image in the Container Registry that is to be run on each worker replica.
args Sequence[str]: The arguments to be passed when starting the container.
command Sequence[str]: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env Sequence[GoogleCloudAiplatformV1EnvVar]: Environment variables to be passed to the container. Maximum limit is 100.

imageUri String: The URI of a container image in the Container Registry that is to be run on each worker replica.
args List<String>: The arguments to be passed when starting the container.
command List<String>: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env List<Property Map>: Environment variables to be passed to the container. Maximum limit is 100.

GoogleCloudAiplatformV1ContainerSpecResponse, GoogleCloudAiplatformV1ContainerSpecResponseArgs

Args List<string>: The arguments to be passed when starting the container.
Command List<string>: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
Env List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarResponse>: Environment variables to be passed to the container. Maximum limit is 100.
ImageUri string: The URI of a container image in the Container Registry that is to be run on each worker replica.

Args []string: The arguments to be passed when starting the container.
Command []string: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
Env []GoogleCloudAiplatformV1EnvVarResponse: Environment variables to be passed to the container. Maximum limit is 100.
ImageUri string: The URI of a container image in the Container Registry that is to be run on each worker replica.

args List<String>: The arguments to be passed when starting the container.
command List<String>: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env List<GoogleCloudAiplatformV1EnvVarResponse>: Environment variables to be passed to the container. Maximum limit is 100.
imageUri String: The URI of a container image in the Container Registry that is to be run on each worker replica.

args string[]: The arguments to be passed when starting the container.
command string[]: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env GoogleCloudAiplatformV1EnvVarResponse[]: Environment variables to be passed to the container. Maximum limit is 100.
imageUri string: The URI of a container image in the Container Registry that is to be run on each worker replica.

args Sequence[str]: The arguments to be passed when starting the container.
command Sequence[str]: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env Sequence[GoogleCloudAiplatformV1EnvVarResponse]: Environment variables to be passed to the container. Maximum limit is 100.
image_uri str: The URI of a container image in the Container Registry that is to be run on each worker replica.

args List<String>: The arguments to be passed when starting the container.
command List<String>: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env List<Property Map>: Environment variables to be passed to the container. Maximum limit is 100.
imageUri String: The URI of a container image in the Container Registry that is to be run on each worker replica.

GoogleCloudAiplatformV1CustomJobSpec, GoogleCloudAiplatformV1CustomJobSpecArgs

WorkerPoolSpecs List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1WorkerPoolSpec>: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
BaseOutputDirectory Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestination: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
EnableDashboardAccess bool: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
EnableWebAccess bool: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
Experiment string: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
ExperimentRun string: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
Network string: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
ProtectedArtifactLocationId string: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
ReservedIpRanges List<string>: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
Scheduling Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1Scheduling: Scheduling options for a CustomJob.
ServiceAccount string: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
Tensorboard string: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

WorkerPoolSpecs []GoogleCloudAiplatformV1WorkerPoolSpec: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
BaseOutputDirectory GoogleCloudAiplatformV1GcsDestination: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
EnableDashboardAccess bool: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
EnableWebAccess bool: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
Experiment string: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
ExperimentRun string: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
Network string: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
ProtectedArtifactLocationId string: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
ReservedIpRanges []string: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
Scheduling GoogleCloudAiplatformV1Scheduling: Scheduling options for a CustomJob.
ServiceAccount string: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
Tensorboard string: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

workerPoolSpecs List<GoogleCloudAiplatformV1WorkerPoolSpec>: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
baseOutputDirectory GoogleCloudAiplatformV1GcsDestination: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enableDashboardAccess Boolean: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enableWebAccess Boolean: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment String: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experimentRun String: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
network String: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
protectedArtifactLocationId String: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reservedIpRanges List<String>: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling GoogleCloudAiplatformV1Scheduling: Scheduling options for a CustomJob.
serviceAccount String: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard String: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

workerPoolSpecs GoogleCloudAiplatformV1WorkerPoolSpec[]: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
baseOutputDirectory GoogleCloudAiplatformV1GcsDestination: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enableDashboardAccess boolean: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enableWebAccess boolean: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment string: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experimentRun string: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
network string: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
protectedArtifactLocationId string: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reservedIpRanges string[]: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling GoogleCloudAiplatformV1Scheduling: Scheduling options for a CustomJob.
serviceAccount string: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard string: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

worker_pool_specs Sequence[GoogleCloudAiplatformV1WorkerPoolSpec]: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
base_output_directory GoogleCloudAiplatformV1GcsDestination: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enable_dashboard_access bool: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enable_web_access bool: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment str: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experiment_run str: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
network str: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
protected_artifact_location_id str: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reserved_ip_ranges Sequence[str]: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling GoogleCloudAiplatformV1Scheduling: Scheduling options for a CustomJob.
service_account str: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard str: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

workerPoolSpecs List<Property Map>: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
baseOutputDirectory Property Map: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enableDashboardAccess Boolean: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enableWebAccess Boolean: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment String: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experimentRun String: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
network String: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
protectedArtifactLocationId String: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reservedIpRanges List<String>: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling Property Map: Scheduling options for a CustomJob.
serviceAccount String: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard String: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

GoogleCloudAiplatformV1CustomJobSpecResponse, GoogleCloudAiplatformV1CustomJobSpecResponseArgs

BaseOutputDirectory Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationResponse: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
EnableDashboardAccess bool: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
EnableWebAccess bool: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
Experiment string: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
ExperimentRun string: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
Network string: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
ProtectedArtifactLocationId string: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
ReservedIpRanges List<string>: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
Scheduling Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SchedulingResponse: Scheduling options for a CustomJob.
ServiceAccount string: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
Tensorboard string: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
WorkerPoolSpecs List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1WorkerPoolSpecResponse>: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

BaseOutputDirectory GoogleCloudAiplatformV1GcsDestinationResponse: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
EnableDashboardAccess bool: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
EnableWebAccess bool: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
Experiment string: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
ExperimentRun string: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
Network string: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
ProtectedArtifactLocationId string: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
ReservedIpRanges []string: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
Scheduling GoogleCloudAiplatformV1SchedulingResponse: Scheduling options for a CustomJob.
ServiceAccount string: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
Tensorboard string: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
WorkerPoolSpecs []GoogleCloudAiplatformV1WorkerPoolSpecResponse: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

baseOutputDirectory GoogleCloudAiplatformV1GcsDestinationResponse: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enableDashboardAccess Boolean: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enableWebAccess Boolean: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment String: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experimentRun String: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
network String: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
protectedArtifactLocationId String: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reservedIpRanges List<String>: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling GoogleCloudAiplatformV1SchedulingResponse: Scheduling options for a CustomJob.
serviceAccount String: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard String: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
workerPoolSpecs List<GoogleCloudAiplatformV1WorkerPoolSpecResponse>: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

baseOutputDirectory GoogleCloudAiplatformV1GcsDestinationResponse: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enableDashboardAccess boolean: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enableWebAccess boolean: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment string: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experimentRun string: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
network string: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
protectedArtifactLocationId string: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reservedIpRanges string[]: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling GoogleCloudAiplatformV1SchedulingResponse: Scheduling options for a CustomJob.
serviceAccount string: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard string: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
workerPoolSpecs GoogleCloudAiplatformV1WorkerPoolSpecResponse[]: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

base_output_directory GoogleCloudAiplatformV1GcsDestinationResponse: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enable_dashboard_access bool: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enable_web_access bool: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment str: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experiment_run str: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
network str: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
protected_artifact_location_id str: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reserved_ip_ranges Sequence[str]: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling GoogleCloudAiplatformV1SchedulingResponse: Scheduling options for a CustomJob.
service_account str: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard str: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
worker_pool_specs Sequence[GoogleCloudAiplatformV1WorkerPoolSpecResponse]: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

baseOutputDirectory Property Map: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enableDashboardAccess Boolean: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enableWebAccess Boolean: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment String: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experimentRun String: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
network String: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
protectedArtifactLocationId String: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reservedIpRanges List<String>: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling Property Map: Scheduling options for a CustomJob.
serviceAccount String: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard String: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
workerPoolSpecs List<Property Map>: The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

GoogleCloudAiplatformV1DiskSpec, GoogleCloudAiplatformV1DiskSpecArgs

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).

GoogleCloudAiplatformV1DiskSpecResponse, GoogleCloudAiplatformV1DiskSpecResponseArgs

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).

GoogleCloudAiplatformV1EncryptionSpec, GoogleCloudAiplatformV1EncryptionSpecArgs

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.

GoogleCloudAiplatformV1EncryptionSpecResponse, GoogleCloudAiplatformV1EncryptionSpecResponseArgs

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.

GoogleCloudAiplatformV1EnvVar, GoogleCloudAiplatformV1EnvVarArgs

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

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

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

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

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

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

GoogleCloudAiplatformV1EnvVarResponse, GoogleCloudAiplatformV1EnvVarResponseArgs

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

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

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

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

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

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

GoogleCloudAiplatformV1GcsDestination, GoogleCloudAiplatformV1GcsDestinationArgs

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

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

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

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

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

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

GoogleCloudAiplatformV1GcsDestinationResponse, GoogleCloudAiplatformV1GcsDestinationResponseArgs

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

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

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

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

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

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

GoogleCloudAiplatformV1MachineSpec, GoogleCloudAiplatformV1MachineSpecArgs

AcceleratorCount int: The number of accelerators to attach to the machine.
AcceleratorType Pulumi.GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1MachineSpecAcceleratorType: 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 GoogleCloudAiplatformV1MachineSpecAcceleratorType: 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 GoogleCloudAiplatformV1MachineSpecAcceleratorType: 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 GoogleCloudAiplatformV1MachineSpecAcceleratorType: 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 GoogleCloudAiplatformV1MachineSpecAcceleratorType: 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" | "TPU_V2" | "TPU_V3" | "TPU_V4_POD": 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").

GoogleCloudAiplatformV1MachineSpecAcceleratorType, GoogleCloudAiplatformV1MachineSpecAcceleratorTypeArgs

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.
TpuV2: TPU_V2TPU v2.
TpuV3: TPU_V3TPU v3.
TpuV4Pod: TPU_V4_PODTPU v4.

GoogleCloudAiplatformV1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified: ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaK80: NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaP100: NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaV100: NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaP4: NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaT4: NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaA100: NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaA10080gb: NVIDIA_A100_80GBNvidia A100 80GB GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaL4: NVIDIA_L4Nvidia L4 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeTpuV2: TPU_V2TPU v2.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeTpuV3: TPU_V3TPU v3.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeTpuV4Pod: TPU_V4_PODTPU v4.

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.
TpuV2: TPU_V2TPU v2.
TpuV3: TPU_V3TPU v3.
TpuV4Pod: TPU_V4_PODTPU v4.

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.
TpuV2: TPU_V2TPU v2.
TpuV3: TPU_V3TPU v3.
TpuV4Pod: TPU_V4_PODTPU v4.

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.
TPU_V2: TPU_V2TPU v2.
TPU_V3: TPU_V3TPU v3.
TPU_V4_POD: TPU_V4_PODTPU v4.

"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.
"TPU_V2": TPU_V2TPU v2.
"TPU_V3": TPU_V3TPU v3.
"TPU_V4_POD": TPU_V4_PODTPU v4.

GoogleCloudAiplatformV1MachineSpecResponse, GoogleCloudAiplatformV1MachineSpecResponseArgs

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").

GoogleCloudAiplatformV1NfsMount, GoogleCloudAiplatformV1NfsMountArgs

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

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

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

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

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

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

GoogleCloudAiplatformV1NfsMountResponse, GoogleCloudAiplatformV1NfsMountResponseArgs

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

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

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

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

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

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

GoogleCloudAiplatformV1PythonPackageSpec, GoogleCloudAiplatformV1PythonPackageSpecArgs

ExecutorImageUri string: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
PackageUris List<string>: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
PythonModule string: The Python module name to run after installing the packages.
Args List<string>: Command line arguments to be passed to the Python task.
Env List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVar>: Environment variables to be passed to the python module. Maximum limit is 100.

ExecutorImageUri string: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
PackageUris []string: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
PythonModule string: The Python module name to run after installing the packages.
Args []string: Command line arguments to be passed to the Python task.
Env []GoogleCloudAiplatformV1EnvVar: Environment variables to be passed to the python module. Maximum limit is 100.

executorImageUri String: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
packageUris List<String>: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
pythonModule String: The Python module name to run after installing the packages.
args List<String>: Command line arguments to be passed to the Python task.
env List<GoogleCloudAiplatformV1EnvVar>: Environment variables to be passed to the python module. Maximum limit is 100.

executorImageUri string: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
packageUris string[]: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
pythonModule string: The Python module name to run after installing the packages.
args string[]: Command line arguments to be passed to the Python task.
env GoogleCloudAiplatformV1EnvVar[]: Environment variables to be passed to the python module. Maximum limit is 100.

executor_image_uri str: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
package_uris Sequence[str]: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
python_module str: The Python module name to run after installing the packages.
args Sequence[str]: Command line arguments to be passed to the Python task.
env Sequence[GoogleCloudAiplatformV1EnvVar]: Environment variables to be passed to the python module. Maximum limit is 100.

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

GoogleCloudAiplatformV1PythonPackageSpecResponse, GoogleCloudAiplatformV1PythonPackageSpecResponseArgs

Args List<string>: Command line arguments to be passed to the Python task.
Env List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarResponse>: Environment variables to be passed to the python module. Maximum limit is 100.
ExecutorImageUri string: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
PackageUris List<string>: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
PythonModule string: The Python module name to run after installing the packages.

Args []string: Command line arguments to be passed to the Python task.
Env []GoogleCloudAiplatformV1EnvVarResponse: Environment variables to be passed to the python module. Maximum limit is 100.
ExecutorImageUri string: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
PackageUris []string: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
PythonModule string: The Python module name to run after installing the packages.

args List<String>: Command line arguments to be passed to the Python task.
env List<GoogleCloudAiplatformV1EnvVarResponse>: Environment variables to be passed to the python module. Maximum limit is 100.
executorImageUri String: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
packageUris List<String>: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
pythonModule String: The Python module name to run after installing the packages.

args string[]: Command line arguments to be passed to the Python task.
env GoogleCloudAiplatformV1EnvVarResponse[]: Environment variables to be passed to the python module. Maximum limit is 100.
executorImageUri string: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
packageUris string[]: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
pythonModule string: The Python module name to run after installing the packages.

args Sequence[str]: Command line arguments to be passed to the Python task.
env Sequence[GoogleCloudAiplatformV1EnvVarResponse]: Environment variables to be passed to the python module. Maximum limit is 100.
executor_image_uri str: The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
package_uris Sequence[str]: The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
python_module str: The Python module name to run after installing the packages.

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

GoogleCloudAiplatformV1Scheduling, GoogleCloudAiplatformV1SchedulingArgs

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

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

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

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

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

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

GoogleCloudAiplatformV1SchedulingResponse, GoogleCloudAiplatformV1SchedulingResponseArgs

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

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

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

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

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

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

GoogleCloudAiplatformV1WorkerPoolSpec, GoogleCloudAiplatformV1WorkerPoolSpecArgs

ContainerSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ContainerSpec: The custom container task.
DiskSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1DiskSpec: Disk spec.
MachineSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1MachineSpec: Optional. Immutable. The specification of a single machine.
NfsMounts List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1NfsMount>: Optional. List of NFS mount spec.
PythonPackageSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PythonPackageSpec: The Python packaged task.
ReplicaCount string: Optional. The number of worker replicas to use for this worker pool.

ContainerSpec GoogleCloudAiplatformV1ContainerSpec: The custom container task.
DiskSpec GoogleCloudAiplatformV1DiskSpec: Disk spec.
MachineSpec GoogleCloudAiplatformV1MachineSpec: Optional. Immutable. The specification of a single machine.
NfsMounts []GoogleCloudAiplatformV1NfsMount: Optional. List of NFS mount spec.
PythonPackageSpec GoogleCloudAiplatformV1PythonPackageSpec: The Python packaged task.
ReplicaCount string: Optional. The number of worker replicas to use for this worker pool.

containerSpec GoogleCloudAiplatformV1ContainerSpec: The custom container task.
diskSpec GoogleCloudAiplatformV1DiskSpec: Disk spec.
machineSpec GoogleCloudAiplatformV1MachineSpec: Optional. Immutable. The specification of a single machine.
nfsMounts List<GoogleCloudAiplatformV1NfsMount>: Optional. List of NFS mount spec.
pythonPackageSpec GoogleCloudAiplatformV1PythonPackageSpec: The Python packaged task.
replicaCount String: Optional. The number of worker replicas to use for this worker pool.

containerSpec GoogleCloudAiplatformV1ContainerSpec: The custom container task.
diskSpec GoogleCloudAiplatformV1DiskSpec: Disk spec.
machineSpec GoogleCloudAiplatformV1MachineSpec: Optional. Immutable. The specification of a single machine.
nfsMounts GoogleCloudAiplatformV1NfsMount[]: Optional. List of NFS mount spec.
pythonPackageSpec GoogleCloudAiplatformV1PythonPackageSpec: The Python packaged task.
replicaCount string: Optional. The number of worker replicas to use for this worker pool.

container_spec GoogleCloudAiplatformV1ContainerSpec: The custom container task.
disk_spec GoogleCloudAiplatformV1DiskSpec: Disk spec.
machine_spec GoogleCloudAiplatformV1MachineSpec: Optional. Immutable. The specification of a single machine.
nfs_mounts Sequence[GoogleCloudAiplatformV1NfsMount]: Optional. List of NFS mount spec.
python_package_spec GoogleCloudAiplatformV1PythonPackageSpec: The Python packaged task.
replica_count str: Optional. The number of worker replicas to use for this worker pool.

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

GoogleCloudAiplatformV1WorkerPoolSpecResponse, GoogleCloudAiplatformV1WorkerPoolSpecResponseArgs

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

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

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

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

container_spec GoogleCloudAiplatformV1ContainerSpecResponse: The custom container task.
disk_spec GoogleCloudAiplatformV1DiskSpecResponse: Disk spec.
machine_spec GoogleCloudAiplatformV1MachineSpecResponse: Optional. Immutable. The specification of a single machine.
nfs_mounts Sequence[GoogleCloudAiplatformV1NfsMountResponse]: Optional. List of NFS mount spec.
python_package_spec GoogleCloudAiplatformV1PythonPackageSpecResponse: The Python packaged task.
replica_count str: Optional. The number of worker replicas to use for this worker pool.

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

GoogleRpcStatusResponse, 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/v1.CustomJob

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Create CustomJob Resource

Constructor syntax

Parameters

Constructor example

CustomJob Resource Properties

Inputs

Outputs

Supporting Types

GoogleCloudAiplatformV1ContainerSpec, GoogleCloudAiplatformV1ContainerSpecArgs

GoogleCloudAiplatformV1ContainerSpecResponse, GoogleCloudAiplatformV1ContainerSpecResponseArgs

GoogleCloudAiplatformV1CustomJobSpec, GoogleCloudAiplatformV1CustomJobSpecArgs

GoogleCloudAiplatformV1CustomJobSpecResponse, GoogleCloudAiplatformV1CustomJobSpecResponseArgs

GoogleCloudAiplatformV1DiskSpec, GoogleCloudAiplatformV1DiskSpecArgs

GoogleCloudAiplatformV1DiskSpecResponse, GoogleCloudAiplatformV1DiskSpecResponseArgs

GoogleCloudAiplatformV1EncryptionSpec, GoogleCloudAiplatformV1EncryptionSpecArgs

GoogleCloudAiplatformV1EncryptionSpecResponse, GoogleCloudAiplatformV1EncryptionSpecResponseArgs

GoogleCloudAiplatformV1EnvVar, GoogleCloudAiplatformV1EnvVarArgs

GoogleCloudAiplatformV1EnvVarResponse, GoogleCloudAiplatformV1EnvVarResponseArgs

GoogleCloudAiplatformV1GcsDestination, GoogleCloudAiplatformV1GcsDestinationArgs

GoogleCloudAiplatformV1GcsDestinationResponse, GoogleCloudAiplatformV1GcsDestinationResponseArgs

GoogleCloudAiplatformV1MachineSpec, GoogleCloudAiplatformV1MachineSpecArgs

GoogleCloudAiplatformV1MachineSpecAcceleratorType, GoogleCloudAiplatformV1MachineSpecAcceleratorTypeArgs

GoogleCloudAiplatformV1MachineSpecResponse, GoogleCloudAiplatformV1MachineSpecResponseArgs

GoogleCloudAiplatformV1NfsMount, GoogleCloudAiplatformV1NfsMountArgs

GoogleCloudAiplatformV1NfsMountResponse, GoogleCloudAiplatformV1NfsMountResponseArgs

GoogleCloudAiplatformV1PythonPackageSpec, GoogleCloudAiplatformV1PythonPackageSpecArgs

GoogleCloudAiplatformV1PythonPackageSpecResponse, GoogleCloudAiplatformV1PythonPackageSpecResponseArgs

GoogleCloudAiplatformV1Scheduling, GoogleCloudAiplatformV1SchedulingArgs

GoogleCloudAiplatformV1SchedulingResponse, GoogleCloudAiplatformV1SchedulingResponseArgs

GoogleCloudAiplatformV1WorkerPoolSpec, GoogleCloudAiplatformV1WorkerPoolSpecArgs

GoogleCloudAiplatformV1WorkerPoolSpecResponse, GoogleCloudAiplatformV1WorkerPoolSpecResponseArgs

GoogleRpcStatusResponse, GoogleRpcStatusResponseArgs

Package Details

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