Struct google_api_proto::google::cloud::notebooks::v1::ExecutionTemplate

pub struct ExecutionTemplate {

    pub scale_tier: i32,
    pub master_type: String,
    pub accelerator_config: Option<SchedulerAcceleratorConfig>,
    pub labels: BTreeMap<String, String>,
    pub input_notebook_file: String,
    pub container_image_uri: String,
    pub output_notebook_folder: String,
    pub params_yaml_file: String,
    pub parameters: String,
    pub service_account: String,
    pub job_type: i32,
    pub kernel_spec: String,
    pub tensorboard: String,
    pub job_parameters: Option<JobParameters>,
}

The description a notebook execution workload.

Fields

scale_tier: i32

👎Deprecated

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

master_type: 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.

accelerator_config: Option<SchedulerAcceleratorConfig>

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

labels: BTreeMap<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.

input_notebook_file: 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

container_image_uri: 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

output_notebook_folder: 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

params_yaml_file: 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

parameters: String

Parameters used within the ‘input_notebook_file’ notebook.

service_account: 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.

job_type: i32

The type of Job to be used on this execution.

kernel_spec: 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.

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}

job_parameters: Option<JobParameters>

Parameters for an execution type. NOTE: There are currently no extra parameters for VertexAI jobs.

Implementations

impl ExecutionTemplate

pub fn scale_tier(&self) -> ScaleTier

Returns the enum value of scale_tier, or the default if the field is set to an invalid enum value.

pub fn set_scale_tier(&mut self, value: ScaleTier)

Sets scale_tier to the provided enum value.

pub fn job_type(&self) -> JobType

Returns the enum value of job_type, or the default if the field is set to an invalid enum value.

pub fn set_job_type(&mut self, value: JobType)

Sets job_type to the provided enum value.

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() -> Self

Returns the “default value” for a type.

impl Message for ExecutionTemplate

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for ExecutionTemplate

fn eq(&self, other: &ExecutionTemplate) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ExecutionTemplate