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Google Cloud Native v0.31.1 published on Thursday, Jul 20, 2023 by Pulumi

google-native.notebooks/v1.getExecution

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

Google Cloud Native v0.31.1 published on Thursday, Jul 20, 2023 by Pulumi

    Gets details of executions

    Using getExecution

    Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

    function getExecution(args: GetExecutionArgs, opts?: InvokeOptions): Promise<GetExecutionResult>
    function getExecutionOutput(args: GetExecutionOutputArgs, opts?: InvokeOptions): Output<GetExecutionResult>
    def get_execution(execution_id: Optional[str] = None,
                      location: Optional[str] = None,
                      project: Optional[str] = None,
                      opts: Optional[InvokeOptions] = None) -> GetExecutionResult
    def get_execution_output(execution_id: Optional[pulumi.Input[str]] = None,
                      location: Optional[pulumi.Input[str]] = None,
                      project: Optional[pulumi.Input[str]] = None,
                      opts: Optional[InvokeOptions] = None) -> Output[GetExecutionResult]
    func LookupExecution(ctx *Context, args *LookupExecutionArgs, opts ...InvokeOption) (*LookupExecutionResult, error)
    func LookupExecutionOutput(ctx *Context, args *LookupExecutionOutputArgs, opts ...InvokeOption) LookupExecutionResultOutput

    > Note: This function is named LookupExecution in the Go SDK.

    public static class GetExecution 
    {
        public static Task<GetExecutionResult> InvokeAsync(GetExecutionArgs args, InvokeOptions? opts = null)
        public static Output<GetExecutionResult> Invoke(GetExecutionInvokeArgs args, InvokeOptions? opts = null)
    }
    public static CompletableFuture<GetExecutionResult> getExecution(GetExecutionArgs args, InvokeOptions options)
    // Output-based functions aren't available in Java yet
    
    fn::invoke:
      function: google-native:notebooks/v1:getExecution
      arguments:
        # arguments dictionary

    The following arguments are supported:

    ExecutionId string
    Location string
    Project string
    ExecutionId string
    Location string
    Project string
    executionId String
    location String
    project String
    executionId string
    location string
    project string
    executionId String
    location String
    project String

    getExecution Result

    The following output properties are available:

    CreateTime string

    Time the Execution was instantiated.

    Description string

    A brief description of this execution.

    DisplayName string

    Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.

    ExecutionTemplate Pulumi.GoogleNative.Notebooks.V1.Outputs.ExecutionTemplateResponse

    execute metadata including name, hardware spec, region, labels, etc.

    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}

    OutputNotebookFile string

    Output notebook file generated by this execution

    State string

    State of the underlying AI Platform job.

    UpdateTime string

    Time the Execution was last updated.

    CreateTime string

    Time the Execution was instantiated.

    Description string

    A brief description of this execution.

    DisplayName string

    Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.

    ExecutionTemplate ExecutionTemplateResponse

    execute metadata including name, hardware spec, region, labels, etc.

    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}

    OutputNotebookFile string

    Output notebook file generated by this execution

    State string

    State of the underlying AI Platform job.

    UpdateTime string

    Time the Execution was last updated.

    createTime String

    Time the Execution was instantiated.

    description String

    A brief description of this execution.

    displayName String

    Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.

    executionTemplate ExecutionTemplateResponse

    execute metadata including name, hardware spec, region, labels, etc.

    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}

    outputNotebookFile String

    Output notebook file generated by this execution

    state String

    State of the underlying AI Platform job.

    updateTime String

    Time the Execution was last updated.

    createTime string

    Time the Execution was instantiated.

    description string

    A brief description of this execution.

    displayName string

    Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.

    executionTemplate ExecutionTemplateResponse

    execute metadata including name, hardware spec, region, labels, etc.

    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}

    outputNotebookFile string

    Output notebook file generated by this execution

    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.

    description str

    A brief description of this execution.

    display_name str

    Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.

    execution_template ExecutionTemplateResponse

    execute metadata including name, hardware spec, region, labels, etc.

    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}

    output_notebook_file str

    Output notebook file generated by this execution

    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.

    description String

    A brief description of this execution.

    displayName String

    Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.

    executionTemplate Property Map

    execute metadata including name, hardware spec, region, labels, etc.

    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}

    outputNotebookFile String

    Output notebook file generated by this execution

    state String

    State of the underlying AI Platform job.

    updateTime String

    Time the Execution was last updated.

    Supporting Types

    DataprocParametersResponse

    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}

    ExecutionTemplateResponse

    AcceleratorConfig Pulumi.GoogleNative.Notebooks.V1.Inputs.SchedulerAcceleratorConfigResponse

    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.DataprocParametersResponse

    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 string

    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

    ScaleTier 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 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.VertexAIParametersResponse

    Parameters used in Vertex AI JobType executions.

    AcceleratorConfig SchedulerAcceleratorConfigResponse

    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 DataprocParametersResponse

    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 string

    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

    ScaleTier 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 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 VertexAIParametersResponse

    Parameters used in Vertex AI JobType executions.

    acceleratorConfig SchedulerAcceleratorConfigResponse

    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 DataprocParametersResponse

    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 String

    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

    scaleTier 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 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 VertexAIParametersResponse

    Parameters used in Vertex AI JobType executions.

    acceleratorConfig SchedulerAcceleratorConfigResponse

    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 DataprocParametersResponse

    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 string

    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

    scaleTier 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 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 VertexAIParametersResponse

    Parameters used in Vertex AI JobType executions.

    accelerator_config SchedulerAcceleratorConfigResponse

    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 DataprocParametersResponse

    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 str

    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

    scale_tier 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 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 VertexAIParametersResponse

    Parameters used in Vertex AI JobType executions.

    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 String

    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

    scaleTier 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 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.

    SchedulerAcceleratorConfigResponse

    CoreCount string

    Count of cores of this accelerator.

    Type string

    Type of this accelerator.

    CoreCount string

    Count of cores of this accelerator.

    Type string

    Type of this accelerator.

    coreCount String

    Count of cores of this accelerator.

    type String

    Type of this accelerator.

    coreCount string

    Count of cores of this accelerator.

    type string

    Type of this accelerator.

    core_count str

    Count of cores of this accelerator.

    type str

    Type of this accelerator.

    coreCount String

    Count of cores of this accelerator.

    type String

    Type of this accelerator.

    VertexAIParametersResponse

    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.

    Package Details

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

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

    Google Cloud Native v0.31.1 published on Thursday, Jul 20, 2023 by Pulumi