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

google-native.notebooks/v1.Execution

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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

Creates a new Execution in a given project and location. Auto-naming is currently not supported for this resource.

Create Execution Resource

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

Constructor syntax

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

@overload
def Execution(resource_name: str,
              opts: Optional[ResourceOptions] = None,
              execution_id: Optional[str] = None,
              description: Optional[str] = None,
              execution_template: Optional[ExecutionTemplateArgs] = None,
              location: Optional[str] = None,
              output_notebook_file: Optional[str] = None,
              project: Optional[str] = None)
func NewExecution(ctx *Context, name string, args ExecutionArgs, opts ...ResourceOption) (*Execution, error)
public Execution(string name, ExecutionArgs args, CustomResourceOptions? opts = null)
public Execution(String name, ExecutionArgs args)
public Execution(String name, ExecutionArgs args, CustomResourceOptions options)
type: google-native:notebooks/v1:Execution
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name This property is required. string
The unique name of the resource.
args This property is required. ExecutionArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
resource_name This property is required. str
The unique name of the resource.
args This property is required. ExecutionArgs
The arguments to resource properties.
opts ResourceOptions
Bag of options to control resource's behavior.
ctx Context
Context object for the current deployment.
name This property is required. string
The unique name of the resource.
args This property is required. ExecutionArgs
The arguments to resource properties.
opts ResourceOption
Bag of options to control resource's behavior.
name This property is required. string
The unique name of the resource.
args This property is required. ExecutionArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
name This property is required. String
The unique name of the resource.
args This property is required. ExecutionArgs
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 exampleexecutionResourceResourceFromNotebooksv1 = new GoogleNative.Notebooks.V1.Execution("exampleexecutionResourceResourceFromNotebooksv1", new()
{
    ExecutionId = "string",
    Description = "string",
    ExecutionTemplate = new GoogleNative.Notebooks.V1.Inputs.ExecutionTemplateArgs
    {
        Labels = 
        {
            { "string", "string" },
        },
        OutputNotebookFolder = "string",
        InputNotebookFile = "string",
        JobType = GoogleNative.Notebooks.V1.ExecutionTemplateJobType.JobTypeUnspecified,
        KernelSpec = "string",
        AcceleratorConfig = new GoogleNative.Notebooks.V1.Inputs.SchedulerAcceleratorConfigArgs
        {
            CoreCount = "string",
            Type = GoogleNative.Notebooks.V1.SchedulerAcceleratorConfigType.SchedulerAcceleratorTypeUnspecified,
        },
        MasterType = "string",
        DataprocParameters = new GoogleNative.Notebooks.V1.Inputs.DataprocParametersArgs
        {
            Cluster = "string",
        },
        Parameters = "string",
        ParamsYamlFile = "string",
        ContainerImageUri = "string",
        ServiceAccount = "string",
        Tensorboard = "string",
        VertexAiParameters = new GoogleNative.Notebooks.V1.Inputs.VertexAIParametersArgs
        {
            Env = 
            {
                { "string", "string" },
            },
            Network = "string",
        },
    },
    Location = "string",
    OutputNotebookFile = "string",
    Project = "string",
});
Copy
example, err := notebooks.NewExecution(ctx, "exampleexecutionResourceResourceFromNotebooksv1", &notebooks.ExecutionArgs{
	ExecutionId: pulumi.String("string"),
	Description: pulumi.String("string"),
	ExecutionTemplate: &notebooks.ExecutionTemplateArgs{
		Labels: pulumi.StringMap{
			"string": pulumi.String("string"),
		},
		OutputNotebookFolder: pulumi.String("string"),
		InputNotebookFile:    pulumi.String("string"),
		JobType:              notebooks.ExecutionTemplateJobTypeJobTypeUnspecified,
		KernelSpec:           pulumi.String("string"),
		AcceleratorConfig: &notebooks.SchedulerAcceleratorConfigArgs{
			CoreCount: pulumi.String("string"),
			Type:      notebooks.SchedulerAcceleratorConfigTypeSchedulerAcceleratorTypeUnspecified,
		},
		MasterType: pulumi.String("string"),
		DataprocParameters: &notebooks.DataprocParametersArgs{
			Cluster: pulumi.String("string"),
		},
		Parameters:        pulumi.String("string"),
		ParamsYamlFile:    pulumi.String("string"),
		ContainerImageUri: pulumi.String("string"),
		ServiceAccount:    pulumi.String("string"),
		Tensorboard:       pulumi.String("string"),
		VertexAiParameters: &notebooks.VertexAIParametersArgs{
			Env: pulumi.StringMap{
				"string": pulumi.String("string"),
			},
			Network: pulumi.String("string"),
		},
	},
	Location:           pulumi.String("string"),
	OutputNotebookFile: pulumi.String("string"),
	Project:            pulumi.String("string"),
})
Copy
var exampleexecutionResourceResourceFromNotebooksv1 = new Execution("exampleexecutionResourceResourceFromNotebooksv1", ExecutionArgs.builder()
    .executionId("string")
    .description("string")
    .executionTemplate(ExecutionTemplateArgs.builder()
        .labels(Map.of("string", "string"))
        .outputNotebookFolder("string")
        .inputNotebookFile("string")
        .jobType("JOB_TYPE_UNSPECIFIED")
        .kernelSpec("string")
        .acceleratorConfig(SchedulerAcceleratorConfigArgs.builder()
            .coreCount("string")
            .type("SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIED")
            .build())
        .masterType("string")
        .dataprocParameters(DataprocParametersArgs.builder()
            .cluster("string")
            .build())
        .parameters("string")
        .paramsYamlFile("string")
        .containerImageUri("string")
        .serviceAccount("string")
        .tensorboard("string")
        .vertexAiParameters(VertexAIParametersArgs.builder()
            .env(Map.of("string", "string"))
            .network("string")
            .build())
        .build())
    .location("string")
    .outputNotebookFile("string")
    .project("string")
    .build());
Copy
exampleexecution_resource_resource_from_notebooksv1 = google_native.notebooks.v1.Execution("exampleexecutionResourceResourceFromNotebooksv1",
    execution_id="string",
    description="string",
    execution_template={
        "labels": {
            "string": "string",
        },
        "output_notebook_folder": "string",
        "input_notebook_file": "string",
        "job_type": google_native.notebooks.v1.ExecutionTemplateJobType.JOB_TYPE_UNSPECIFIED,
        "kernel_spec": "string",
        "accelerator_config": {
            "core_count": "string",
            "type": google_native.notebooks.v1.SchedulerAcceleratorConfigType.SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIED,
        },
        "master_type": "string",
        "dataproc_parameters": {
            "cluster": "string",
        },
        "parameters": "string",
        "params_yaml_file": "string",
        "container_image_uri": "string",
        "service_account": "string",
        "tensorboard": "string",
        "vertex_ai_parameters": {
            "env": {
                "string": "string",
            },
            "network": "string",
        },
    },
    location="string",
    output_notebook_file="string",
    project="string")
Copy
const exampleexecutionResourceResourceFromNotebooksv1 = new google_native.notebooks.v1.Execution("exampleexecutionResourceResourceFromNotebooksv1", {
    executionId: "string",
    description: "string",
    executionTemplate: {
        labels: {
            string: "string",
        },
        outputNotebookFolder: "string",
        inputNotebookFile: "string",
        jobType: google_native.notebooks.v1.ExecutionTemplateJobType.JobTypeUnspecified,
        kernelSpec: "string",
        acceleratorConfig: {
            coreCount: "string",
            type: google_native.notebooks.v1.SchedulerAcceleratorConfigType.SchedulerAcceleratorTypeUnspecified,
        },
        masterType: "string",
        dataprocParameters: {
            cluster: "string",
        },
        parameters: "string",
        paramsYamlFile: "string",
        containerImageUri: "string",
        serviceAccount: "string",
        tensorboard: "string",
        vertexAiParameters: {
            env: {
                string: "string",
            },
            network: "string",
        },
    },
    location: "string",
    outputNotebookFile: "string",
    project: "string",
});
Copy
type: google-native:notebooks/v1:Execution
properties:
    description: string
    executionId: string
    executionTemplate:
        acceleratorConfig:
            coreCount: string
            type: SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIED
        containerImageUri: string
        dataprocParameters:
            cluster: string
        inputNotebookFile: string
        jobType: JOB_TYPE_UNSPECIFIED
        kernelSpec: string
        labels:
            string: string
        masterType: string
        outputNotebookFolder: string
        parameters: string
        paramsYamlFile: string
        serviceAccount: string
        tensorboard: string
        vertexAiParameters:
            env:
                string: string
            network: string
    location: string
    outputNotebookFile: string
    project: string
Copy

Execution Resource Properties

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

Inputs

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

The Execution resource accepts the following input properties:

ExecutionId
This property is required.
Changes to this property will trigger replacement.
string
Required. User-defined unique ID of this execution.
Description string
A brief description of this execution.
ExecutionTemplate Pulumi.GoogleNative.Notebooks.V1.Inputs.ExecutionTemplate
execute metadata including name, hardware spec, region, labels, etc.
Location Changes to this property will trigger replacement. string
OutputNotebookFile string
Output notebook file generated by this execution
Project Changes to this property will trigger replacement. string
ExecutionId
This property is required.
Changes to this property will trigger replacement.
string
Required. User-defined unique ID of this execution.
Description string
A brief description of this execution.
ExecutionTemplate ExecutionTemplateArgs
execute metadata including name, hardware spec, region, labels, etc.
Location Changes to this property will trigger replacement. string
OutputNotebookFile string
Output notebook file generated by this execution
Project Changes to this property will trigger replacement. string
executionId
This property is required.
Changes to this property will trigger replacement.
String
Required. User-defined unique ID of this execution.
description String
A brief description of this execution.
executionTemplate ExecutionTemplate
execute metadata including name, hardware spec, region, labels, etc.
location Changes to this property will trigger replacement. String
outputNotebookFile String
Output notebook file generated by this execution
project Changes to this property will trigger replacement. String
executionId
This property is required.
Changes to this property will trigger replacement.
string
Required. User-defined unique ID of this execution.
description string
A brief description of this execution.
executionTemplate ExecutionTemplate
execute metadata including name, hardware spec, region, labels, etc.
location Changes to this property will trigger replacement. string
outputNotebookFile string
Output notebook file generated by this execution
project Changes to this property will trigger replacement. string
execution_id
This property is required.
Changes to this property will trigger replacement.
str
Required. User-defined unique ID of this execution.
description str
A brief description of this execution.
execution_template ExecutionTemplateArgs
execute metadata including name, hardware spec, region, labels, etc.
location Changes to this property will trigger replacement. str
output_notebook_file str
Output notebook file generated by this execution
project Changes to this property will trigger replacement. str
executionId
This property is required.
Changes to this property will trigger replacement.
String
Required. User-defined unique ID of this execution.
description String
A brief description of this execution.
executionTemplate Property Map
execute metadata including name, hardware spec, region, labels, etc.
location Changes to this property will trigger replacement. String
outputNotebookFile String
Output notebook file generated by this execution
project Changes to this property will trigger replacement. String

Outputs

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

CreateTime string
Time the Execution was instantiated.
DisplayName string
Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
Id string
The provider-assigned unique ID for this managed resource.
JobUri string
The URI of the external job used to execute the notebook.
Name string
The resource name of the execute. Format: projects/{project_id}/locations/{location}/executions/{execution_id}
State string
State of the underlying AI Platform job.
UpdateTime string
Time the Execution was last updated.
CreateTime string
Time the Execution was instantiated.
DisplayName string
Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
Id string
The provider-assigned unique ID for this managed resource.
JobUri string
The URI of the external job used to execute the notebook.
Name string
The resource name of the execute. Format: projects/{project_id}/locations/{location}/executions/{execution_id}
State string
State of the underlying AI Platform job.
UpdateTime string
Time the Execution was last updated.
createTime String
Time the Execution was instantiated.
displayName String
Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
id String
The provider-assigned unique ID for this managed resource.
jobUri String
The URI of the external job used to execute the notebook.
name String
The resource name of the execute. Format: projects/{project_id}/locations/{location}/executions/{execution_id}
state String
State of the underlying AI Platform job.
updateTime String
Time the Execution was last updated.
createTime string
Time the Execution was instantiated.
displayName string
Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
id string
The provider-assigned unique ID for this managed resource.
jobUri string
The URI of the external job used to execute the notebook.
name string
The resource name of the execute. Format: projects/{project_id}/locations/{location}/executions/{execution_id}
state string
State of the underlying AI Platform job.
updateTime string
Time the Execution was last updated.
create_time str
Time the Execution was instantiated.
display_name str
Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
id str
The provider-assigned unique ID for this managed resource.
job_uri str
The URI of the external job used to execute the notebook.
name str
The resource name of the execute. Format: projects/{project_id}/locations/{location}/executions/{execution_id}
state str
State of the underlying AI Platform job.
update_time str
Time the Execution was last updated.
createTime String
Time the Execution was instantiated.
displayName String
Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
id String
The provider-assigned unique ID for this managed resource.
jobUri String
The URI of the external job used to execute the notebook.
name String
The resource name of the execute. Format: projects/{project_id}/locations/{location}/executions/{execution_id}
state String
State of the underlying AI Platform job.
updateTime String
Time the Execution was last updated.

Supporting Types

DataprocParameters
, DataprocParametersArgs

Cluster string
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
Cluster string
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
cluster String
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
cluster string
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
cluster str
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
cluster String
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}

DataprocParametersResponse
, DataprocParametersResponseArgs

Cluster This property is required. string
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
Cluster This property is required. string
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
cluster This property is required. String
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
cluster This property is required. string
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
cluster This property is required. str
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
cluster This property is required. String
URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}

ExecutionTemplate
, ExecutionTemplateArgs

ScaleTier This property is required. Pulumi.GoogleNative.Notebooks.V1.ExecutionTemplateScaleTier
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

AcceleratorConfig Pulumi.GoogleNative.Notebooks.V1.Inputs.SchedulerAcceleratorConfig
Configuration (count and accelerator type) for hardware running notebook execution.
ContainerImageUri string
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
DataprocParameters Pulumi.GoogleNative.Notebooks.V1.Inputs.DataprocParameters
Parameters used in Dataproc JobType executions.
InputNotebookFile string
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
JobType Pulumi.GoogleNative.Notebooks.V1.ExecutionTemplateJobType
The type of Job to be used on this execution.
KernelSpec string
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
Labels Dictionary<string, string>
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
MasterType string
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
OutputNotebookFolder string
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
Parameters string
Parameters used within the 'input_notebook_file' notebook.
ParamsYamlFile string
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
ServiceAccount string
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
Tensorboard string
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
VertexAiParameters Pulumi.GoogleNative.Notebooks.V1.Inputs.VertexAIParameters
Parameters used in Vertex AI JobType executions.
ScaleTier This property is required. ExecutionTemplateScaleTier
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

AcceleratorConfig SchedulerAcceleratorConfig
Configuration (count and accelerator type) for hardware running notebook execution.
ContainerImageUri string
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
DataprocParameters DataprocParameters
Parameters used in Dataproc JobType executions.
InputNotebookFile string
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
JobType ExecutionTemplateJobType
The type of Job to be used on this execution.
KernelSpec string
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
Labels map[string]string
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
MasterType string
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
OutputNotebookFolder string
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
Parameters string
Parameters used within the 'input_notebook_file' notebook.
ParamsYamlFile string
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
ServiceAccount string
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
Tensorboard string
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
VertexAiParameters VertexAIParameters
Parameters used in Vertex AI JobType executions.
scaleTier This property is required. ExecutionTemplateScaleTier
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

acceleratorConfig SchedulerAcceleratorConfig
Configuration (count and accelerator type) for hardware running notebook execution.
containerImageUri String
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
dataprocParameters DataprocParameters
Parameters used in Dataproc JobType executions.
inputNotebookFile String
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
jobType ExecutionTemplateJobType
The type of Job to be used on this execution.
kernelSpec String
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
labels Map<String,String>
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
masterType String
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
outputNotebookFolder String
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
parameters String
Parameters used within the 'input_notebook_file' notebook.
paramsYamlFile String
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
serviceAccount String
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
tensorboard String
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
vertexAiParameters VertexAIParameters
Parameters used in Vertex AI JobType executions.
scaleTier This property is required. ExecutionTemplateScaleTier
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

acceleratorConfig SchedulerAcceleratorConfig
Configuration (count and accelerator type) for hardware running notebook execution.
containerImageUri string
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
dataprocParameters DataprocParameters
Parameters used in Dataproc JobType executions.
inputNotebookFile string
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
jobType ExecutionTemplateJobType
The type of Job to be used on this execution.
kernelSpec string
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
labels {[key: string]: string}
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
masterType string
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
outputNotebookFolder string
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
parameters string
Parameters used within the 'input_notebook_file' notebook.
paramsYamlFile string
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
serviceAccount string
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
tensorboard string
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
vertexAiParameters VertexAIParameters
Parameters used in Vertex AI JobType executions.
scale_tier This property is required. ExecutionTemplateScaleTier
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

accelerator_config SchedulerAcceleratorConfig
Configuration (count and accelerator type) for hardware running notebook execution.
container_image_uri str
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
dataproc_parameters DataprocParameters
Parameters used in Dataproc JobType executions.
input_notebook_file str
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
job_type ExecutionTemplateJobType
The type of Job to be used on this execution.
kernel_spec str
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
labels Mapping[str, str]
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
master_type str
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
output_notebook_folder str
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
parameters str
Parameters used within the 'input_notebook_file' notebook.
params_yaml_file str
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
service_account str
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
tensorboard str
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
vertex_ai_parameters VertexAIParameters
Parameters used in Vertex AI JobType executions.
scaleTier This property is required. "SCALE_TIER_UNSPECIFIED" | "BASIC" | "STANDARD_1" | "PREMIUM_1" | "BASIC_GPU" | "BASIC_TPU" | "CUSTOM"
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

acceleratorConfig Property Map
Configuration (count and accelerator type) for hardware running notebook execution.
containerImageUri String
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
dataprocParameters Property Map
Parameters used in Dataproc JobType executions.
inputNotebookFile String
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
jobType "JOB_TYPE_UNSPECIFIED" | "VERTEX_AI" | "DATAPROC"
The type of Job to be used on this execution.
kernelSpec String
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
labels Map<String>
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
masterType String
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
outputNotebookFolder String
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
parameters String
Parameters used within the 'input_notebook_file' notebook.
paramsYamlFile String
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
serviceAccount String
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
tensorboard String
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
vertexAiParameters Property Map
Parameters used in Vertex AI JobType executions.

ExecutionTemplateJobType
, ExecutionTemplateJobTypeArgs

JobTypeUnspecified
JOB_TYPE_UNSPECIFIEDNo type specified.
VertexAi
VERTEX_AICustom Job in aiplatform.googleapis.com. Default value for an execution.
Dataproc
DATAPROCRun execution on a cluster with Dataproc as a job. https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs
ExecutionTemplateJobTypeJobTypeUnspecified
JOB_TYPE_UNSPECIFIEDNo type specified.
ExecutionTemplateJobTypeVertexAi
VERTEX_AICustom Job in aiplatform.googleapis.com. Default value for an execution.
ExecutionTemplateJobTypeDataproc
DATAPROCRun execution on a cluster with Dataproc as a job. https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs
JobTypeUnspecified
JOB_TYPE_UNSPECIFIEDNo type specified.
VertexAi
VERTEX_AICustom Job in aiplatform.googleapis.com. Default value for an execution.
Dataproc
DATAPROCRun execution on a cluster with Dataproc as a job. https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs
JobTypeUnspecified
JOB_TYPE_UNSPECIFIEDNo type specified.
VertexAi
VERTEX_AICustom Job in aiplatform.googleapis.com. Default value for an execution.
Dataproc
DATAPROCRun execution on a cluster with Dataproc as a job. https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs
JOB_TYPE_UNSPECIFIED
JOB_TYPE_UNSPECIFIEDNo type specified.
VERTEX_AI
VERTEX_AICustom Job in aiplatform.googleapis.com. Default value for an execution.
DATAPROC
DATAPROCRun execution on a cluster with Dataproc as a job. https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs
"JOB_TYPE_UNSPECIFIED"
JOB_TYPE_UNSPECIFIEDNo type specified.
"VERTEX_AI"
VERTEX_AICustom Job in aiplatform.googleapis.com. Default value for an execution.
"DATAPROC"
DATAPROCRun execution on a cluster with Dataproc as a job. https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs

ExecutionTemplateResponse
, ExecutionTemplateResponseArgs

AcceleratorConfig This property is required. Pulumi.GoogleNative.Notebooks.V1.Inputs.SchedulerAcceleratorConfigResponse
Configuration (count and accelerator type) for hardware running notebook execution.
ContainerImageUri This property is required. string
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
DataprocParameters This property is required. Pulumi.GoogleNative.Notebooks.V1.Inputs.DataprocParametersResponse
Parameters used in Dataproc JobType executions.
InputNotebookFile This property is required. string
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
JobType This property is required. string
The type of Job to be used on this execution.
KernelSpec This property is required. string
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
Labels This property is required. Dictionary<string, string>
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
MasterType This property is required. string
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
OutputNotebookFolder This property is required. string
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
Parameters This property is required. string
Parameters used within the 'input_notebook_file' notebook.
ParamsYamlFile This property is required. string
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
ScaleTier This property is required. string
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

ServiceAccount This property is required. string
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
Tensorboard This property is required. string
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
VertexAiParameters This property is required. Pulumi.GoogleNative.Notebooks.V1.Inputs.VertexAIParametersResponse
Parameters used in Vertex AI JobType executions.
AcceleratorConfig This property is required. SchedulerAcceleratorConfigResponse
Configuration (count and accelerator type) for hardware running notebook execution.
ContainerImageUri This property is required. string
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
DataprocParameters This property is required. DataprocParametersResponse
Parameters used in Dataproc JobType executions.
InputNotebookFile This property is required. string
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
JobType This property is required. string
The type of Job to be used on this execution.
KernelSpec This property is required. string
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
Labels This property is required. map[string]string
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
MasterType This property is required. string
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
OutputNotebookFolder This property is required. string
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
Parameters This property is required. string
Parameters used within the 'input_notebook_file' notebook.
ParamsYamlFile This property is required. string
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
ScaleTier This property is required. string
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

ServiceAccount This property is required. string
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
Tensorboard This property is required. string
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
VertexAiParameters This property is required. VertexAIParametersResponse
Parameters used in Vertex AI JobType executions.
acceleratorConfig This property is required. SchedulerAcceleratorConfigResponse
Configuration (count and accelerator type) for hardware running notebook execution.
containerImageUri This property is required. String
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
dataprocParameters This property is required. DataprocParametersResponse
Parameters used in Dataproc JobType executions.
inputNotebookFile This property is required. String
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
jobType This property is required. String
The type of Job to be used on this execution.
kernelSpec This property is required. String
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
labels This property is required. Map<String,String>
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
masterType This property is required. String
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
outputNotebookFolder This property is required. String
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
parameters This property is required. String
Parameters used within the 'input_notebook_file' notebook.
paramsYamlFile This property is required. String
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
scaleTier This property is required. String
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

serviceAccount This property is required. String
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
tensorboard This property is required. String
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
vertexAiParameters This property is required. VertexAIParametersResponse
Parameters used in Vertex AI JobType executions.
acceleratorConfig This property is required. SchedulerAcceleratorConfigResponse
Configuration (count and accelerator type) for hardware running notebook execution.
containerImageUri This property is required. string
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
dataprocParameters This property is required. DataprocParametersResponse
Parameters used in Dataproc JobType executions.
inputNotebookFile This property is required. string
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
jobType This property is required. string
The type of Job to be used on this execution.
kernelSpec This property is required. string
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
labels This property is required. {[key: string]: string}
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
masterType This property is required. string
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
outputNotebookFolder This property is required. string
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
parameters This property is required. string
Parameters used within the 'input_notebook_file' notebook.
paramsYamlFile This property is required. string
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
scaleTier This property is required. string
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

serviceAccount This property is required. string
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
tensorboard This property is required. string
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
vertexAiParameters This property is required. VertexAIParametersResponse
Parameters used in Vertex AI JobType executions.
accelerator_config This property is required. SchedulerAcceleratorConfigResponse
Configuration (count and accelerator type) for hardware running notebook execution.
container_image_uri This property is required. str
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
dataproc_parameters This property is required. DataprocParametersResponse
Parameters used in Dataproc JobType executions.
input_notebook_file This property is required. str
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
job_type This property is required. str
The type of Job to be used on this execution.
kernel_spec This property is required. str
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
labels This property is required. Mapping[str, str]
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
master_type This property is required. str
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
output_notebook_folder This property is required. str
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
parameters This property is required. str
Parameters used within the 'input_notebook_file' notebook.
params_yaml_file This property is required. str
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
scale_tier This property is required. str
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

service_account This property is required. str
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
tensorboard This property is required. str
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
vertex_ai_parameters This property is required. VertexAIParametersResponse
Parameters used in Vertex AI JobType executions.
acceleratorConfig This property is required. Property Map
Configuration (count and accelerator type) for hardware running notebook execution.
containerImageUri This property is required. String
Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
dataprocParameters This property is required. Property Map
Parameters used in Dataproc JobType executions.
inputNotebookFile This property is required. String
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
jobType This property is required. String
The type of Job to be used on this execution.
kernelSpec This property is required. String
Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
labels This property is required. Map<String>
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
masterType This property is required. String
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. The following types are supported: - n1-standard-4 - n1-standard-8 - n1-standard-16 - n1-standard-32 - n1-standard-64 - n1-standard-96 - n1-highmem-2 - n1-highmem-4 - n1-highmem-8 - n1-highmem-16 - n1-highmem-32 - n1-highmem-64 - n1-highmem-96 - n1-highcpu-16 - n1-highcpu-32 - n1-highcpu-64 - n1-highcpu-96 Alternatively, you can use the following legacy machine types: - standard - large_model - complex_model_s - complex_model_m - complex_model_l - standard_gpu - complex_model_m_gpu - complex_model_l_gpu - standard_p100 - complex_model_m_p100 - standard_v100 - large_model_v100 - complex_model_m_v100 - complex_model_l_v100 Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPU.
outputNotebookFolder This property is required. String
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
parameters This property is required. String
Parameters used within the 'input_notebook_file' notebook.
paramsYamlFile This property is required. String
Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
scaleTier This property is required. String
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Deprecated: Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

serviceAccount This property is required. String
The email address of a service account to use when running the execution. You must have the iam.serviceAccounts.actAs permission for the specified service account.
tensorboard This property is required. String
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
vertexAiParameters This property is required. Property Map
Parameters used in Vertex AI JobType executions.

ExecutionTemplateScaleTier
, ExecutionTemplateScaleTierArgs

ScaleTierUnspecified
SCALE_TIER_UNSPECIFIEDUnspecified Scale Tier.
Basic
BASICA single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.
Standard1
STANDARD_1Many workers and a few parameter servers.
Premium1
PREMIUM_1A large number of workers with many parameter servers.
BasicGpu
BASIC_GPUA single worker instance with a K80 GPU.
BasicTpu
BASIC_TPUA single worker instance with a Cloud TPU.
Custom
CUSTOMThe CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines: * You must set ExecutionTemplate.masterType to specify the type of machine to use for your master node. This is the only required setting.
ExecutionTemplateScaleTierScaleTierUnspecified
SCALE_TIER_UNSPECIFIEDUnspecified Scale Tier.
ExecutionTemplateScaleTierBasic
BASICA single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.
ExecutionTemplateScaleTierStandard1
STANDARD_1Many workers and a few parameter servers.
ExecutionTemplateScaleTierPremium1
PREMIUM_1A large number of workers with many parameter servers.
ExecutionTemplateScaleTierBasicGpu
BASIC_GPUA single worker instance with a K80 GPU.
ExecutionTemplateScaleTierBasicTpu
BASIC_TPUA single worker instance with a Cloud TPU.
ExecutionTemplateScaleTierCustom
CUSTOMThe CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines: * You must set ExecutionTemplate.masterType to specify the type of machine to use for your master node. This is the only required setting.
ScaleTierUnspecified
SCALE_TIER_UNSPECIFIEDUnspecified Scale Tier.
Basic
BASICA single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.
Standard1
STANDARD_1Many workers and a few parameter servers.
Premium1
PREMIUM_1A large number of workers with many parameter servers.
BasicGpu
BASIC_GPUA single worker instance with a K80 GPU.
BasicTpu
BASIC_TPUA single worker instance with a Cloud TPU.
Custom
CUSTOMThe CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines: * You must set ExecutionTemplate.masterType to specify the type of machine to use for your master node. This is the only required setting.
ScaleTierUnspecified
SCALE_TIER_UNSPECIFIEDUnspecified Scale Tier.
Basic
BASICA single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.
Standard1
STANDARD_1Many workers and a few parameter servers.
Premium1
PREMIUM_1A large number of workers with many parameter servers.
BasicGpu
BASIC_GPUA single worker instance with a K80 GPU.
BasicTpu
BASIC_TPUA single worker instance with a Cloud TPU.
Custom
CUSTOMThe CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines: * You must set ExecutionTemplate.masterType to specify the type of machine to use for your master node. This is the only required setting.
SCALE_TIER_UNSPECIFIED
SCALE_TIER_UNSPECIFIEDUnspecified Scale Tier.
BASIC
BASICA single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.
STANDARD1
STANDARD_1Many workers and a few parameter servers.
PREMIUM1
PREMIUM_1A large number of workers with many parameter servers.
BASIC_GPU
BASIC_GPUA single worker instance with a K80 GPU.
BASIC_TPU
BASIC_TPUA single worker instance with a Cloud TPU.
CUSTOM
CUSTOMThe CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines: * You must set ExecutionTemplate.masterType to specify the type of machine to use for your master node. This is the only required setting.
"SCALE_TIER_UNSPECIFIED"
SCALE_TIER_UNSPECIFIEDUnspecified Scale Tier.
"BASIC"
BASICA single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.
"STANDARD_1"
STANDARD_1Many workers and a few parameter servers.
"PREMIUM_1"
PREMIUM_1A large number of workers with many parameter servers.
"BASIC_GPU"
BASIC_GPUA single worker instance with a K80 GPU.
"BASIC_TPU"
BASIC_TPUA single worker instance with a Cloud TPU.
"CUSTOM"
CUSTOMThe CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines: * You must set ExecutionTemplate.masterType to specify the type of machine to use for your master node. This is the only required setting.

SchedulerAcceleratorConfig
, SchedulerAcceleratorConfigArgs

CoreCount string
Count of cores of this accelerator.
Type Pulumi.GoogleNative.Notebooks.V1.SchedulerAcceleratorConfigType
Type of this accelerator.
CoreCount string
Count of cores of this accelerator.
Type SchedulerAcceleratorConfigType
Type of this accelerator.
coreCount String
Count of cores of this accelerator.
type SchedulerAcceleratorConfigType
Type of this accelerator.
coreCount string
Count of cores of this accelerator.
type SchedulerAcceleratorConfigType
Type of this accelerator.
core_count str
Count of cores of this accelerator.
type SchedulerAcceleratorConfigType
Type of this accelerator.

SchedulerAcceleratorConfigResponse
, SchedulerAcceleratorConfigResponseArgs

CoreCount This property is required. string
Count of cores of this accelerator.
Type This property is required. string
Type of this accelerator.
CoreCount This property is required. string
Count of cores of this accelerator.
Type This property is required. string
Type of this accelerator.
coreCount This property is required. String
Count of cores of this accelerator.
type This property is required. String
Type of this accelerator.
coreCount This property is required. string
Count of cores of this accelerator.
type This property is required. string
Type of this accelerator.
core_count This property is required. str
Count of cores of this accelerator.
type This property is required. str
Type of this accelerator.
coreCount This property is required. String
Count of cores of this accelerator.
type This property is required. String
Type of this accelerator.

SchedulerAcceleratorConfigType
, SchedulerAcceleratorConfigTypeArgs

SchedulerAcceleratorTypeUnspecified
SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type. Default to no GPU.
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.
TpuV2
TPU_V2TPU v2.
TpuV3
TPU_V3TPU v3.
SchedulerAcceleratorConfigTypeSchedulerAcceleratorTypeUnspecified
SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type. Default to no GPU.
SchedulerAcceleratorConfigTypeNvidiaTeslaK80
NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
SchedulerAcceleratorConfigTypeNvidiaTeslaP100
NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
SchedulerAcceleratorConfigTypeNvidiaTeslaV100
NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
SchedulerAcceleratorConfigTypeNvidiaTeslaP4
NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
SchedulerAcceleratorConfigTypeNvidiaTeslaT4
NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
SchedulerAcceleratorConfigTypeNvidiaTeslaA100
NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
SchedulerAcceleratorConfigTypeTpuV2
TPU_V2TPU v2.
SchedulerAcceleratorConfigTypeTpuV3
TPU_V3TPU v3.
SchedulerAcceleratorTypeUnspecified
SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type. Default to no GPU.
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.
TpuV2
TPU_V2TPU v2.
TpuV3
TPU_V3TPU v3.
SchedulerAcceleratorTypeUnspecified
SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type. Default to no GPU.
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.
TpuV2
TPU_V2TPU v2.
TpuV3
TPU_V3TPU v3.
SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIED
SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type. Default to no GPU.
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.
TPU_V2
TPU_V2TPU v2.
TPU_V3
TPU_V3TPU v3.
"SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIED"
SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type. Default to no GPU.
"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.
"TPU_V2"
TPU_V2TPU v2.
"TPU_V3"
TPU_V3TPU v3.

VertexAIParameters
, VertexAIParametersArgs

Env Dictionary<string, string>
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
Network string
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
Env map[string]string
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
Network string
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
env Map<String,String>
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
network String
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
env {[key: string]: string}
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
network string
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
env Mapping[str, str]
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
network str
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
env Map<String>
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
network String
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.

VertexAIParametersResponse
, VertexAIParametersResponseArgs

Env This property is required. Dictionary<string, string>
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
Network This property is required. string
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
Env This property is required. map[string]string
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
Network This property is required. string
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
env This property is required. Map<String,String>
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
network This property is required. String
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
env This property is required. {[key: string]: string}
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
network This property is required. string
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
env This property is required. Mapping[str, str]
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
network This property is required. str
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
env This property is required. Map<String>
Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
network This property is required. String
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. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.

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