Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1TrainingPipeline

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Overview

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.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1TrainingPipeline

Returns a new instance of GoogleCloudAiplatformV1TrainingPipeline.



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32227

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#create_time ⇒ String

Output only. Time when the TrainingPipeline was created. Corresponds to the JSON property createTime

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32116

def create_time
  @create_time
end

#display_name ⇒ String

Required. The user-defined name of this TrainingPipeline. Corresponds to the JSON property displayName

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32121

def display_name
  @display_name
end

#encryption_spec ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top- level resource. Corresponds to the JSON property encryptionSpec

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32127

def encryption_spec
  @encryption_spec
end

#end_time ⇒ String

Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED. Corresponds to the JSON property endTime

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32134

def end_time
  @end_time
end

#error ⇒ Google::Apis::AiplatformV1::GoogleRpcStatus

The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide. Corresponds to the JSON property error

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32144

def error
  @error
end

#input_data_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1InputDataConfig

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model. Corresponds to the JSON property inputDataConfig

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32150

def input_data_config
  @input_data_config
end

#labels ⇒ Hash<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. Corresponds to the JSON property labels

Returns:

  • (Hash<String,String>) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32159

def labels
  @labels
end

#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. Corresponds to the JSON property modelId

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32167

def model_id
  @model_id
end

#model_to_upload ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1Model

A trained machine learning Model. Corresponds to the JSON property modelToUpload

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32172

def model_to_upload
  @model_to_upload
end

#name ⇒ String

Output only. Resource name of the TrainingPipeline. Corresponds to the JSON property name

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32177

def name
  @name
end

#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. Corresponds to the JSON property parentModel

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32183

def parent_model
  @parent_model
end

#start_time ⇒ String

Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state. Corresponds to the JSON property startTime

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32189

def start_time
  @start_time
end

#state ⇒ String

Output only. The detailed state of the pipeline. Corresponds to the JSON property state

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32194

def state
  @state
end

#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. Corresponds to the JSON property trainingTaskDefinition

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32205

def training_task_definition
  @training_task_definition
end

#training_task_inputs ⇒ Object

Required. The training task's parameter(s), as specified in the training_task_definition's inputs. Corresponds to the JSON property trainingTaskInputs

Returns:

  • (Object) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32211

def training_task_inputs
  @training_task_inputs
end

#training_task_metadata ⇒ Object

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. Corresponds to the JSON property trainingTaskMetadata

Returns:

  • (Object) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32220

def training_task_metadata
  @training_task_metadata
end

#update_time ⇒ String

Output only. Time when the TrainingPipeline was most recently updated. Corresponds to the JSON property updateTime

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32225

def update_time
  @update_time
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 32232

def update!(**args)
  @create_time = args[:create_time] if args.key?(:create_time)
  @display_name = args[:display_name] if args.key?(:display_name)
  @encryption_spec = args[:encryption_spec] if args.key?(:encryption_spec)
  @end_time = args[:end_time] if args.key?(:end_time)
  @error = args[:error] if args.key?(:error)
  @input_data_config = args[:input_data_config] if args.key?(:input_data_config)
  @labels = args[:labels] if args.key?(:labels)
  @model_id = args[:model_id] if args.key?(:model_id)
  @model_to_upload = args[:model_to_upload] if args.key?(:model_to_upload)
  @name = args[:name] if args.key?(:name)
  @parent_model = args[:parent_model] if args.key?(:parent_model)
  @start_time = args[:start_time] if args.key?(:start_time)
  @state = args[:state] if args.key?(:state)
  @training_task_definition = args[:training_task_definition] if args.key?(:training_task_definition)
  @training_task_inputs = args[:training_task_inputs] if args.key?(:training_task_inputs)
  @training_task_metadata = args[:training_task_metadata] if args.key?(:training_task_metadata)
  @update_time = args[:update_time] if args.key?(:update_time)
end