|
30 | 30 | "PredictSchemata",
|
31 | 31 | "ModelContainerSpec",
|
32 | 32 | "Port",
|
| 33 | + "ModelSourceInfo", |
33 | 34 | },
|
34 | 35 | )
|
35 | 36 |
|
@@ -274,6 +275,11 @@ class Model(proto.Message):
|
274 | 275 | Customer-managed encryption key spec for a
|
275 | 276 | Model. If set, this Model and all sub-resources
|
276 | 277 | of this Model will be secured by this key.
|
| 278 | + model_source_info (google.cloud.aiplatform_v1beta1.types.ModelSourceInfo): |
| 279 | + Output only. Source of a model. It can either |
| 280 | + be automl training pipeline, custom training |
| 281 | + pipeline, BigQuery ML, or existing Vertex AI |
| 282 | + Model. |
277 | 283 | """
|
278 | 284 |
|
279 | 285 | class DeploymentResourcesType(proto.Enum):
|
@@ -443,6 +449,11 @@ class ExportableContent(proto.Enum):
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443 | 449 | number=24,
|
444 | 450 | message=gca_encryption_spec.EncryptionSpec,
|
445 | 451 | )
|
| 452 | + model_source_info = proto.Field( |
| 453 | + proto.MESSAGE, |
| 454 | + number=38, |
| 455 | + message="ModelSourceInfo", |
| 456 | + ) |
446 | 457 |
|
447 | 458 |
|
448 | 459 | class PredictSchemata(proto.Message):
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@@ -800,4 +811,26 @@ class Port(proto.Message):
|
800 | 811 | )
|
801 | 812 |
|
802 | 813 |
|
| 814 | +class ModelSourceInfo(proto.Message): |
| 815 | + r"""Detail description of the source information of the model. |
| 816 | +
|
| 817 | + Attributes: |
| 818 | + source_type (google.cloud.aiplatform_v1beta1.types.ModelSourceInfo.ModelSourceType): |
| 819 | + Type of the model source. |
| 820 | + """ |
| 821 | + |
| 822 | + class ModelSourceType(proto.Enum): |
| 823 | + r"""Source of the model.""" |
| 824 | + MODEL_SOURCE_TYPE_UNSPECIFIED = 0 |
| 825 | + AUTOML = 1 |
| 826 | + CUSTOM = 2 |
| 827 | + BQML = 3 |
| 828 | + |
| 829 | + source_type = proto.Field( |
| 830 | + proto.ENUM, |
| 831 | + number=1, |
| 832 | + enum=ModelSourceType, |
| 833 | + ) |
| 834 | + |
| 835 | + |
803 | 836 | __all__ = tuple(sorted(__protobuf__.manifest))
|
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