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TrainingPipeline(mapping=None, *, ignore_unknown_fields=False, **kwargs)
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.
Attributes |
|
---|---|
Name | Description |
name |
str
Output only. Resource name of the TrainingPipeline. |
display_name |
str
Required. The user-defined name of this TrainingPipeline. |
input_data_config |
google.cloud.aiplatform_v1.types.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. |
training_task_definition |
str
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 |
google.protobuf.struct_pb2.Value
Required. The training task's parameter(s), as specified in the training_task_definition's inputs .
|
training_task_metadata |
google.protobuf.struct_pb2.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.
|
model_to_upload |
google.cloud.aiplatform_v1.types.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_id |
str
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 |
str
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 |
google.cloud.aiplatform_v1.types.PipelineState
Output only. The detailed state of the pipeline. |
error |
google.rpc.status_pb2.Status
Output only. Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED .
|
create_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the TrainingPipeline was created. |
start_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
|
end_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED ,
PIPELINE_STATE_FAILED , PIPELINE_STATE_CANCELLED .
|
update_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the TrainingPipeline was most recently updated. |
labels |
MutableMapping[str, str]
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 |
google.cloud.aiplatform_v1.types.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. |
Classes
LabelsEntry
LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
The abstract base class for a message.
Parameters | |
---|---|
Name | Description |
kwargs |
dict
Keys and values corresponding to the fields of the message. |
mapping |
Union[dict,
A dictionary or message to be used to determine the values for this message. |
ignore_unknown_fields |
Optional(bool)
If True, do not raise errors for unknown fields. Only applied if |
Methods
TrainingPipeline
TrainingPipeline(mapping=None, *, ignore_unknown_fields=False, **kwargs)
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.