Struct TrainingPipeline

#[non_exhaustive]
pub struct TrainingPipeline {

    pub name: String,
    pub display_name: String,
    pub input_data_config: Option<InputDataConfig>,
    pub training_task_definition: String,
    pub training_task_inputs: Option<Value>,
    pub training_task_metadata: Option<Value>,
    pub model_to_upload: Option<Model>,
    pub model_id: String,
    pub parent_model: String,
    pub state: PipelineState,
    pub error: Option<Status>,
    pub create_time: Option<Timestamp>,
    pub start_time: Option<Timestamp>,
    pub end_time: Option<Timestamp>,
    pub update_time: Option<Timestamp>,
    pub labels: HashMap<String, String>,
    pub encryption_spec: Option<EncryptionSpec>,
    /* private fields */
}

The TrainingPipeline orchestrates tasks associated with training a Model. It always executes the training task, and optionally may also export data from Vertex AI’s Dataset which becomes the training input, upload the Model to Vertex AI, and evaluate the Model.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

name: String

Output only. Resource name of the TrainingPipeline.

display_name: String

Required. The user-defined name of this TrainingPipeline.

input_data_config: Option<InputDataConfig>

Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline’s training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.

training_task_definition: String

Required. A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

training_task_inputs: Option<Value>

Required. The training task’s parameter(s), as specified in the training_task_definition’s inputs.

training_task_metadata: Option<Value>

Output only. The metadata information as specified in the training_task_definition’s metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline’s training_task_definition contains metadata object.

model_to_upload: Option<Model>

Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline’s training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline’s state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload’s resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.

model_id: String

Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name.

This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

parent_model: String

Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.

state: PipelineState

Output only. The detailed state of the pipeline.

error: Option<Status>

Output only. Only populated when the pipeline’s state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

create_time: Option<Timestamp>

Output only. Time when the TrainingPipeline was created.

start_time: Option<Timestamp>

Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.

end_time: Option<Timestamp>

Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.

update_time: Option<Timestamp>

Output only. Time when the TrainingPipeline was most recently updated.

labels: HashMap<String, String>

The labels with user-defined metadata to organize TrainingPipelines.

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.

See https://goo.gl/xmQnxf for more information and examples of labels.

encryption_spec: Option<EncryptionSpec>

Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key.

Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.

Implementations

impl TrainingPipeline

pub fn new() -> Self

pub fn set_name<T: Into<String>>(self, v: T) -> Self

Sets the value of name.

pub fn set_display_name<T: Into<String>>(self, v: T) -> Self

Sets the value of display_name.

pub fn set_input_data_config<T: Into<Option<InputDataConfig>>>( self, v: T, ) -> Self

Sets the value of input_data_config.

pub fn set_training_task_definition<T: Into<String>>(self, v: T) -> Self

Sets the value of training_task_definition.

pub fn set_training_task_inputs<T: Into<Option<Value>>>(self, v: T) -> Self

Sets the value of training_task_inputs.

pub fn set_training_task_metadata<T: Into<Option<Value>>>(self, v: T) -> Self

Sets the value of training_task_metadata.

pub fn set_model_to_upload<T: Into<Option<Model>>>(self, v: T) -> Self

Sets the value of model_to_upload.

pub fn set_model_id<T: Into<String>>(self, v: T) -> Self

Sets the value of model_id.

pub fn set_parent_model<T: Into<String>>(self, v: T) -> Self

Sets the value of parent_model.

pub fn set_state<T: Into<PipelineState>>(self, v: T) -> Self

Sets the value of state.

pub fn set_error<T: Into<Option<Status>>>(self, v: T) -> Self

Sets the value of error.

pub fn set_create_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of create_time.

pub fn set_start_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of start_time.

pub fn set_end_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of end_time.

pub fn set_update_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of update_time.

pub fn set_labels<T, K, V>(self, v: T) -> Self

where T: IntoIterator<Item = (K, V)>, K: Into<String>, V: Into<String>,

Sets the value of labels.

pub fn set_encryption_spec<T: Into<Option<EncryptionSpec>>>(self, v: T) -> Self

Sets the value of encryption_spec.

Trait Implementations

impl Clone for TrainingPipeline

fn clone(&self) -> TrainingPipeline

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingPipeline

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

Formats the value using the given formatter.

impl Default for TrainingPipeline

fn default() -> TrainingPipeline

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for TrainingPipeline

where TrainingPipeline: Default,

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Message for TrainingPipeline

fn typename() -> &'static str

The typename of this message.

impl PartialEq for TrainingPipeline

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl Serialize for TrainingPipeline

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for TrainingPipeline