Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::TrainingPipeline (v0.51.0)

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::TrainingPipeline.

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.

Inherits

Object

Extended By

Google::Protobuf::MessageExts::ClassMethods

Includes

Google::Protobuf::MessageExts

Methods

#create_time

def create_time() -> ::Google::Protobuf::Timestamp

Returns

(::Google::Protobuf::Timestamp) — Output only. Time when the TrainingPipeline was created.

#display_name

def display_name() -> ::String

Returns

(::String) — Required. The user-defined name of this TrainingPipeline.

#display_name=

def display_name=(value) -> ::String

Parameter

value (::String) — Required. The user-defined name of this TrainingPipeline.

Returns

(::String) — Required. The user-defined name of this TrainingPipeline.

#encryption_spec

def encryption_spec() -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec

Returns

(::Google::Cloud::AIPlatform::V1::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.

#encryption_spec=

def encryption_spec=(value) -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec

Parameter

value (::Google::Cloud::AIPlatform::V1::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.

Returns

(::Google::Cloud::AIPlatform::V1::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.

#end_time

def end_time() -> ::Google::Protobuf::Timestamp

Returns

(::Google::Protobuf::Timestamp) — Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.

#error

def error() -> ::Google::Rpc::Status

Returns

(::Google::Rpc::Status) — Output only. Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

#input_data_config

def input_data_config() -> ::Google::Cloud::AIPlatform::V1::InputDataConfig

Returns

(::Google::Cloud::AIPlatform::V1::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.

#input_data_config=

def input_data_config=(value) -> ::Google::Cloud::AIPlatform::V1::InputDataConfig

Parameter

value (::Google::Cloud::AIPlatform::V1::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.

Returns

(::Google::Cloud::AIPlatform::V1::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.

#labels

def labels() -> ::Google::Protobuf::Map{::String => ::String}

Returns

(::Google::Protobuf::Map{::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.

#labels=

def labels=(value) -> ::Google::Protobuf::Map{::String => ::String}

Parameter

value (::Google::Protobuf::Map{::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.

Returns

(::Google::Protobuf::Map{::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.

#model_id

def model_id() -> ::String

Returns

(::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.

#model_id=

def model_id=(value) -> ::String

Parameter

value (::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.

Returns

(::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.

#model_to_upload

def model_to_upload() -> ::Google::Cloud::AIPlatform::V1::Model

Returns

(::Google::Cloud::AIPlatform::V1::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_to_upload=

def model_to_upload=(value) -> ::Google::Cloud::AIPlatform::V1::Model

Parameter

value (::Google::Cloud::AIPlatform::V1::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.

Returns

(::Google::Cloud::AIPlatform::V1::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.

#name

def name() -> ::String

Returns

(::String) — Output only. Resource name of the TrainingPipeline.

#parent_model

def parent_model() -> ::String

Returns

(::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.

#parent_model=

def parent_model=(value) -> ::String

Parameter

value (::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.

Returns

(::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.

#start_time

def start_time() -> ::Google::Protobuf::Timestamp

Returns

(::Google::Protobuf::Timestamp) — Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.

#state

def state() -> ::Google::Cloud::AIPlatform::V1::PipelineState

Returns

(::Google::Cloud::AIPlatform::V1::PipelineState) — Output only. The detailed state of the pipeline.

#training_task_definition

def training_task_definition() -> ::String

Returns

(::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_definition=

def training_task_definition=(value) -> ::String

Parameter

value (::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.

Returns

(::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

def training_task_inputs() -> ::Google::Protobuf::Value

Returns

(::Google::Protobuf::Value) — Required. The training task's parameter(s), as specified in the training_task_definition's inputs.

#training_task_inputs=

def training_task_inputs=(value) -> ::Google::Protobuf::Value

Parameter

value (::Google::Protobuf::Value) — Required. The training task's parameter(s), as specified in the training_task_definition's inputs.

Returns

(::Google::Protobuf::Value) — Required. The training task's parameter(s), as specified in the training_task_definition's inputs.

#training_task_metadata

def training_task_metadata() -> ::Google::Protobuf::Value

Returns

(::Google::Protobuf::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.

#update_time

def update_time() -> ::Google::Protobuf::Timestamp

Returns

(::Google::Protobuf::Timestamp) — Output only. Time when the TrainingPipeline was most recently updated.