Struct google_api_proto::google::cloud::aiplatform::v1::TensorboardRun

pub struct TensorboardRun {
    pub name: String,
    pub display_name: String,
    pub description: String,
    pub create_time: Option<Timestamp>,
    pub update_time: Option<Timestamp>,
    pub labels: BTreeMap<String, String>,
    pub etag: String,
}

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Fields

name: String

Output only. Name of the TensorboardRun. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

display_name: String

Required. User provided name of this TensorboardRun. This value must be unique among all TensorboardRuns belonging to the same parent TensorboardExperiment.

description: String

Description of this TensorboardRun.

create_time: Option<Timestamp>

Output only. Timestamp when this TensorboardRun was created.

update_time: Option<Timestamp>

Output only. Timestamp when this TensorboardRun was last updated.

labels: BTreeMap<String, String>

The labels with user-defined metadata to organize your TensorboardRuns.

This field will be used to filter and visualize Runs in the Tensorboard UI. For example, a Vertex AI training job can set a label aiplatform.googleapis.com/training_job_id=xxxxx to all the runs created within that job. An end user can set a label experiment_id=xxxxx for all the runs produced in a Jupyter notebook. These runs can be grouped by a label value and visualized together in the Tensorboard UI.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one TensorboardRun (System labels are excluded).

See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with “aiplatform.googleapis.com/” and are immutable.

etag: String

Used to perform a consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Trait Implementations

impl Clone for TensorboardRun

fn clone(&self) -> TensorboardRun

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TensorboardRun

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

Formats the value using the given formatter.

impl Default for TensorboardRun

fn default() -> Self

Returns the “default value” for a type.

impl Message for TensorboardRun

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 TensorboardRun

fn eq(&self, other: &TensorboardRun) -> 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 TensorboardRun