GetTrainingPipelineResult class

Result data returned by getTrainingPipeline.

Constructors

GetTrainingPipelineResult({required String createTime, required String displayName, required GoogleCloudAiplatformV1EncryptionSpecResponse encryptionSpec, required String endTime, required GoogleRpcStatusResponse error, required GoogleCloudAiplatformV1InputDataConfigResponse inputDataConfig, required Map<String, String> labels, required String modelId, required GoogleCloudAiplatformV1ModelResponse modelToUpload, required String name, required String parentModel, required String startTime, required String state, required String trainingTaskDefinition, required dynamic trainingTaskInputs, required dynamic trainingTaskMetadata, required String updateTime})

Creates a new GetTrainingPipelineResult. createTime Time when the TrainingPipeline was created. displayName The user-defined name of this TrainingPipeline. 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. endTime Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED. error Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED. 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 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. modelId 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. modelToUpload 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 Resource name of the TrainingPipeline. parentModel 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. startTime Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state. state The detailed state of the pipeline. trainingTaskDefinition 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. trainingTaskInputs The training task's parameter(s), as specified in the training_task_definition's inputs. trainingTaskMetadata 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. updateTime Time when the TrainingPipeline was most recently updated.

const

GetTrainingPipelineResult.fromMap(Map<String, dynamic> map)

factory

Properties

createTime → String

Time when the TrainingPipeline was created.

final

displayName → String

The user-defined name of this TrainingPipeline.

final

encryptionSpec → GoogleCloudAiplatformV1EncryptionSpecResponse

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.

final

endTime → String

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

final

error → GoogleRpcStatusResponse

Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

final

hashCode → int

The hash code for this object.

no setter, inherited

inputDataConfig → GoogleCloudAiplatformV1InputDataConfigResponse

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.

final

labels → 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.

final

modelId → 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.

final

modelToUpload → GoogleCloudAiplatformV1ModelResponse

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.

final

name → String

Resource name of the TrainingPipeline.

final

parentModel → 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.

final

runtimeType → Type

A representation of the runtime type of the object.

no setter, inherited

startTime → String

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

final

state → String

The detailed state of the pipeline.

final

trainingTaskDefinition → String

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.

final

trainingTaskInputs → dynamic

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

final

trainingTaskMetadata → dynamic

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.

final

updateTime → String

Time when the TrainingPipeline was most recently updated.

final

Methods

noSuchMethod(Invocation invocation) → dynamic

Invoked when a nonexistent method or property is accessed.

inherited

toMap() → Map<String, dynamic>

toString() → String

A string representation of this object.

inherited

Operators

operator ==(Object other) → bool

The equality operator.

inherited