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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
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google-native.notebooks/v1.getSchedule

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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
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    Gets details of schedule

    Using getSchedule

    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 getSchedule(args: GetScheduleArgs, opts?: InvokeOptions): Promise<GetScheduleResult>
function getScheduleOutput(args: GetScheduleOutputArgs, opts?: InvokeOptions): Output<GetScheduleResult>
    def get_schedule(location: Optional[str] = None,
                 project: Optional[str] = None,
                 schedule_id: Optional[str] = None,
                 opts: Optional[InvokeOptions] = None) -> GetScheduleResult
def get_schedule_output(location: Optional[pulumi.Input[str]] = None,
                 project: Optional[pulumi.Input[str]] = None,
                 schedule_id: Optional[pulumi.Input[str]] = None,
                 opts: Optional[InvokeOptions] = None) -> Output[GetScheduleResult]
    func LookupSchedule(ctx *Context, args *LookupScheduleArgs, opts ...InvokeOption) (*LookupScheduleResult, error)
func LookupScheduleOutput(ctx *Context, args *LookupScheduleOutputArgs, opts ...InvokeOption) LookupScheduleResultOutput

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

    public static class GetSchedule 
{
    public static Task<GetScheduleResult> InvokeAsync(GetScheduleArgs args, InvokeOptions? opts = null)
    public static Output<GetScheduleResult> Invoke(GetScheduleInvokeArgs args, InvokeOptions? opts = null)
}
    public static CompletableFuture<GetScheduleResult> getSchedule(GetScheduleArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

    fn::invoke:
  function: google-native:notebooks/v1:getSchedule
  arguments:
    # arguments dictionary

    The following arguments are supported:

    Location string
    ScheduleId string
    Project string
    Location string
    ScheduleId string
    Project string
    location String
    scheduleId String
    project String
    location string
    scheduleId string
    project string
    location String
    scheduleId String
    project String

    getSchedule Result

    The following output properties are available:

    CreateTime string
    Time the schedule was created.
    CronSchedule string
    Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
    Description string
    A brief description of this environment.
    DisplayName string
    Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens -, and underscores _.
    ExecutionTemplate Pulumi.GoogleNative.Notebooks.V1.Outputs.ExecutionTemplateResponse
    Notebook Execution Template corresponding to this schedule.
    Name string
    The name of this schedule. Format: projects/{project_id}/locations/{location}/schedules/{schedule_id}
    RecentExecutions List<Pulumi.GoogleNative.Notebooks.V1.Outputs.ExecutionResponse>
    The most recent execution names triggered from this schedule and their corresponding states.
    State string
    TimeZone string
    Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
    UpdateTime string
    Time the schedule was last updated.
    CreateTime string
    Time the schedule was created.
    CronSchedule string
    Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
    Description string
    A brief description of this environment.
    DisplayName string
    Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens -, and underscores _.
    ExecutionTemplate ExecutionTemplateResponse
    Notebook Execution Template corresponding to this schedule.
    Name string
    The name of this schedule. Format: projects/{project_id}/locations/{location}/schedules/{schedule_id}
    RecentExecutions []ExecutionResponse
    The most recent execution names triggered from this schedule and their corresponding states.
    State string
    TimeZone string
    Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
    UpdateTime string
    Time the schedule was last updated.
    createTime String
    Time the schedule was created.
    cronSchedule String
    Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
    description String
    A brief description of this environment.
    displayName String
    Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens -, and underscores _.
    executionTemplate ExecutionTemplateResponse
    Notebook Execution Template corresponding to this schedule.
    name String
    The name of this schedule. Format: projects/{project_id}/locations/{location}/schedules/{schedule_id}
    recentExecutions List<ExecutionResponse>
    The most recent execution names triggered from this schedule and their corresponding states.
    state String
    timeZone String
    Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
    updateTime String
    Time the schedule was last updated.
    createTime string
    Time the schedule was created.
    cronSchedule string
    Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
    description string
    A brief description of this environment.
    displayName string
    Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens -, and underscores _.
    executionTemplate ExecutionTemplateResponse
    Notebook Execution Template corresponding to this schedule.
    name string
    The name of this schedule. Format: projects/{project_id}/locations/{location}/schedules/{schedule_id}
    recentExecutions ExecutionResponse[]
    The most recent execution names triggered from this schedule and their corresponding states.
    state string
    timeZone string
    Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
    updateTime string
    Time the schedule was last updated.
    create_time str
    Time the schedule was created.
    cron_schedule str
    Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
    description str
    A brief description of this environment.
    display_name str
    Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens -, and underscores _.
    execution_template ExecutionTemplateResponse
    Notebook Execution Template corresponding to this schedule.
    name str
    The name of this schedule. Format: projects/{project_id}/locations/{location}/schedules/{schedule_id}
    recent_executions Sequence[ExecutionResponse]
    The most recent execution names triggered from this schedule and their corresponding states.
    state str
    time_zone str
    Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
    update_time str
    Time the schedule was last updated.
    createTime String
    Time the schedule was created.
    cronSchedule String
    Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
    description String
    A brief description of this environment.
    displayName String
    Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens -, and underscores _.
    executionTemplate Property Map
    Notebook Execution Template corresponding to this schedule.
    name String
    The name of this schedule. Format: projects/{project_id}/locations/{location}/schedules/{schedule_id}
    recentExecutions List<Property Map>
    The most recent execution names triggered from this schedule and their corresponding states.
    state String
    timeZone String
    Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
    updateTime String
    Time the schedule 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}

    ExecutionResponse

    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.Inputs.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.

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