Struct google_api_proto::google::cloud::aiplatform::v1::TrainingPipeline

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: i32,
    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: BTreeMap<String, String>,
    pub encryption_spec: Option<EncryptionSpec>,
}

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][google.cloud.aiplatform.v1.ModelService.UploadModel] the Model to Vertex AI, and evaluate the Model.

Fields

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][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]’s inputs.

training_task_metadata: Option<Value>

Output only. The metadata information as specified in the [training_task_definition][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition] contains metadata object.

model_to_upload: Option<Model>

Describes the Model that may be uploaded (via [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel]) by this TrainingPipeline. The TrainingPipeline’s [training_task_definition][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.Model.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: i32

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: BTreeMap<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][google.cloud.aiplatform.v1.TrainingPipeline.encryption_spec] is not set separately.

Implementations

impl TrainingPipeline

pub fn state(&self) -> PipelineState

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

pub fn set_state(&mut self, value: PipelineState)

Sets state to the provided enum value.

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() -> Self

Returns the “default value” for a type.

impl Message for TrainingPipeline

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(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where 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( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where 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(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

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

where Self: Default,

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

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

where Self: Sized,

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

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

where Self: Sized,

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

impl PartialEq for TrainingPipeline

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