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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
- (::Google::Protobuf::Timestamp) — Output only. Time when the TrainingPipeline was created.
#display_name
def display_name() -> ::String
- (::String) — Required. The user-defined name of this TrainingPipeline.
#display_name=
def display_name=(value) -> ::String
- value (::String) — Required. The user-defined name of this TrainingPipeline.
- (::String) — Required. The user-defined name of this TrainingPipeline.
#encryption_spec
def encryption_spec() -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec
-
(::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
-
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.
-
(::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
-
(::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
-
(::Google::Rpc::Status) — Output only. Only populated when the pipeline's state is
PIPELINE_STATE_FAILED
orPIPELINE_STATE_CANCELLED
.
#input_data_config
def input_data_config() -> ::Google::Cloud::AIPlatform::V1::InputDataConfig
- (::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
- 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.
- (::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}
-
(::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}
-
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.
-
(::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
-
(::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
-
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.
-
(::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
-
(::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
-
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.
-
(::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
- (::String) — Output only. Resource name of the TrainingPipeline.
#parent_model
def parent_model() -> ::String
-
(::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 thisparent_model
.
#parent_model=
def parent_model=(value) -> ::String
-
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 thisparent_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 thisparent_model
.
#start_time
def start_time() -> ::Google::Protobuf::Timestamp
-
(::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
- (::Google::Cloud::AIPlatform::V1::PipelineState) — Output only. The detailed state of the pipeline.
#training_task_definition
def training_task_definition() -> ::String
- (::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
- 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.
- (::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
-
(::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
-
value (::Google::Protobuf::Value) — Required. The training task's parameter(s), as specified in the
training_task_definition's
inputs
.
-
(::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
-
(::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 containsmetadata
object.
#update_time
def update_time() -> ::Google::Protobuf::Timestamp
- (::Google::Protobuf::Timestamp) — Output only. Time when the TrainingPipeline was most recently updated.