Class Google::Cloud::AIPlatform::V1::Model (v0.1.0)

Version 0.1.0

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A trained machine learning Model.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#artifact_uri

def artifact_uri() -> ::String

Returns

• (::String) — Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models.

#artifact_uri=

def artifact_uri=(value) -> ::String

Parameter

• value (::String) — Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models.

Returns

• (::String) — Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models.

#container_spec

def container_spec() -> ::Google::Cloud::AIPlatform::V1::ModelContainerSpec

Returns

• (::Google::Cloud::AIPlatform::V1::ModelContainerSpec) — Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models.

#container_spec=

def container_spec=(value) -> ::Google::Cloud::AIPlatform::V1::ModelContainerSpec

Parameter

• value (::Google::Cloud::AIPlatform::V1::ModelContainerSpec) — Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models.

Returns

• (::Google::Cloud::AIPlatform::V1::ModelContainerSpec) — Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models.

#create_time

def create_time() -> ::Google::Protobuf::Timestamp

Returns

• (::Google::Protobuf::Timestamp) — Output only. Timestamp when this Model was uploaded into Vertex AI.

#deployed_models

def deployed_models() -> ::Array<::Google::Cloud::AIPlatform::V1::DeployedModelRef>

Returns

• (::Array<::Google::Cloud::AIPlatform::V1::DeployedModelRef>) — Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

#description

def description() -> ::String

Returns

• (::String) — The description of the Model.

#description=

def description=(value) -> ::String

Parameter

• value (::String) — The description of the Model.

Returns

• (::String) — The description of the Model.

#display_name

def display_name() -> ::String

Returns

• (::String) — Required. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

#display_name=

def display_name=(value) -> ::String

Parameter

• value (::String) — Required. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

Returns

• (::String) — Required. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

#encryption_spec

def encryption_spec() -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec

Returns

• (::Google::Cloud::AIPlatform::V1::EncryptionSpec) — Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

#encryption_spec=

def encryption_spec=(value) -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec

Parameter

• value (::Google::Cloud::AIPlatform::V1::EncryptionSpec) — Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

Returns

• (::Google::Cloud::AIPlatform::V1::EncryptionSpec) — Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

#etag

def etag() -> ::String

Returns

• (::String) — Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

#etag=

def etag=(value) -> ::String

Parameter

• value (::String) — Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

Returns

• (::String) — Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

#explanation_spec

def explanation_spec() -> ::Google::Cloud::AIPlatform::V1::ExplanationSpec

Returns

• (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — The default explanation specification for this Model.

  The Model can be used for [requesting explanation][PredictionService.Explain] after being deployed if it is populated. The Model can be used for [batch explanation][BatchPredictionJob.generate_explanation] if it is populated.

  All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

  If the default explanation specification is not set for this Model, this Model can still be used for [requesting explanation][PredictionService.Explain] by setting explanation_spec of DeployModelRequest.deployed_model and for [batch explanation][BatchPredictionJob.generate_explanation] by setting explanation_spec of BatchPredictionJob.

#explanation_spec=

def explanation_spec=(value) -> ::Google::Cloud::AIPlatform::V1::ExplanationSpec

Parameter

• value (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — The default explanation specification for this Model.

  The Model can be used for [requesting explanation][PredictionService.Explain] after being deployed if it is populated. The Model can be used for [batch explanation][BatchPredictionJob.generate_explanation] if it is populated.

  All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

  If the default explanation specification is not set for this Model, this Model can still be used for [requesting explanation][PredictionService.Explain] by setting explanation_spec of DeployModelRequest.deployed_model and for [batch explanation][BatchPredictionJob.generate_explanation] by setting explanation_spec of BatchPredictionJob.

Returns

• (::Google::Cloud::AIPlatform::V1::ExplanationSpec) — The default explanation specification for this Model.

  The Model can be used for [requesting explanation][PredictionService.Explain] after being deployed if it is populated. The Model can be used for [batch explanation][BatchPredictionJob.generate_explanation] if it is populated.

  All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob.

  If the default explanation specification is not set for this Model, this Model can still be used for [requesting explanation][PredictionService.Explain] by setting explanation_spec of DeployModelRequest.deployed_model and for [batch explanation][BatchPredictionJob.generate_explanation] by setting explanation_spec of BatchPredictionJob.

#labels

def labels() -> ::Google::Protobuf::Map{::String => ::String}

Returns

• (::Google::Protobuf::Map{::String => ::String}) — The labels with user-defined metadata to organize your Models.

  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}

Parameter

• value (::Google::Protobuf::Map{::String => ::String}) — The labels with user-defined metadata to organize your Models.

  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.

Returns

• (::Google::Protobuf::Map{::String => ::String}) — The labels with user-defined metadata to organize your Models.

  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.

#metadata

def metadata() -> ::Google::Protobuf::Value

Returns

• (::Google::Protobuf::Value) — Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

#metadata=

def metadata=(value) -> ::Google::Protobuf::Value

Parameter

• value (::Google::Protobuf::Value) — Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

Returns

• (::Google::Protobuf::Value) — Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.

#metadata_schema_uri

def metadata_schema_uri() -> ::String

Returns

• (::String) — Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. 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.

#metadata_schema_uri=

def metadata_schema_uri=(value) -> ::String

Parameter

• value (::String) — Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. 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.

Returns

• (::String) — Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. 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.

#name

def name() -> ::String

Returns

• (::String) — The resource name of the Model.

#name=

def name=(value) -> ::String

Parameter

• value (::String) — The resource name of the Model.

Returns

• (::String) — The resource name of the Model.

#predict_schemata

def predict_schemata() -> ::Google::Cloud::AIPlatform::V1::PredictSchemata

Returns

• (::Google::Cloud::AIPlatform::V1::PredictSchemata) — The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

#predict_schemata=

def predict_schemata=(value) -> ::Google::Cloud::AIPlatform::V1::PredictSchemata

Parameter

• value (::Google::Cloud::AIPlatform::V1::PredictSchemata) — The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

Returns

• (::Google::Cloud::AIPlatform::V1::PredictSchemata) — The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

#supported_deployment_resources_types

def supported_deployment_resources_types() -> ::Array<::Google::Cloud::AIPlatform::V1::Model::DeploymentResourcesType>

Returns

• (::Array<::Google::Cloud::AIPlatform::V1::Model::DeploymentResourcesType>) — Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

#supported_export_formats

def supported_export_formats() -> ::Array<::Google::Cloud::AIPlatform::V1::Model::ExportFormat>

Returns

• (::Array<::Google::Cloud::AIPlatform::V1::Model::ExportFormat>) — Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

#supported_input_storage_formats

def supported_input_storage_formats() -> ::Array<::String>

Returns

• (::Array<::String>) — Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

  The possible formats are:

  • jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource.

  • csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource.

  • tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource.

  • tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource.

  • bigquery Each instance is a single row in BigQuery. Uses BigQuerySource.

  • file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

  If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

#supported_output_storage_formats

def supported_output_storage_formats() -> ::Array<::String>

Returns

• (::Array<::String>) — Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

  The possible formats are:

  • jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination.

  • csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination.

  • bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

  If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

#training_pipeline

def training_pipeline() -> ::String

Returns

• (::String) — Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

#update_time

def update_time() -> ::Google::Protobuf::Timestamp

Returns

• (::Google::Protobuf::Timestamp) — Output only. Timestamp when this Model was most recently updated.