Struct google_notebooks1::api::ExecutionTemplate

pub struct ExecutionTemplate {

    pub accelerator_config: Option<SchedulerAcceleratorConfig>,
    pub container_image_uri: Option<String>,
    pub dataproc_parameters: Option<DataprocParameters>,
    pub input_notebook_file: Option<String>,
    pub job_type: Option<String>,
    pub kernel_spec: Option<String>,
    pub labels: Option<HashMap<String, String>>,
    pub master_type: Option<String>,
    pub output_notebook_folder: Option<String>,
    pub parameters: Option<String>,
    pub params_yaml_file: Option<String>,
    pub scale_tier: Option<String>,
    pub service_account: Option<String>,
    pub tensorboard: Option<String>,
    pub vertex_ai_parameters: Option<VertexAIParameters>,
}

The description a notebook execution workload.

This type is not used in any activity, and only used as part of another schema.

Fields

accelerator_config: Option<SchedulerAcceleratorConfig>

Configuration (count and accelerator type) for hardware running notebook execution.

container_image_uri: Option<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

dataproc_parameters: Option<DataprocParameters>

Parameters used in Dataproc JobType executions.

input_notebook_file: Option<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

job_type: Option<String>

The type of Job to be used on this execution.

kernel_spec: Option<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: Option<HashMap<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.

master_type: Option<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.

output_notebook_folder: Option<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: Option<String>

Parameters used within the ‘input_notebook_file’ notebook.

params_yaml_file: Option<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

scale_tier: Option<String>

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

service_account: Option<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: Option<String>

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: Option<VertexAIParameters>

Parameters used in Vertex AI JobType executions.

Trait Implementations

impl Clone for ExecutionTemplate

fn clone(&self) -> ExecutionTemplate

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ExecutionTemplate

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ExecutionTemplate

fn default() -> ExecutionTemplate

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for ExecutionTemplate

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for ExecutionTemplate

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Part for ExecutionTemplate