Google Cloud Ai Platform V1 Client - Class Model (0.10.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class Model.

A trained machine learning Model.

Generated from protobuf message google.cloud.aiplatform.v1.Model

Constructor.

The resource name of the Model.

Generated from protobuf field string name = 1;

The resource name of the Model.

Generated from protobuf field string name = 1;

Output only. Immutable. The version ID of the model.

A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

Generated from protobuf field string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Immutable. The version ID of the model.

A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

Generated from protobuf field string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];

User provided version aliases so that a model version can be referenced via alias (i.e.

projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}) . The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

Generated from protobuf field repeated string version_aliases = 29;

User provided version aliases so that a model version can be referenced via alias (i.e.

projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}) . The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

Generated from protobuf field repeated string version_aliases = 29;

Output only. Timestamp when this version was created.

Generated from protobuf field .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Timestamp when this version was created.

Generated from protobuf field .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Timestamp when this version was most recently updated.

Generated from protobuf field .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Timestamp when this version was most recently updated.

Generated from protobuf field .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

Required. The display name of the Model.

The name can be up to 128 characters long and can consist of any UTF-8 characters.

Generated from protobuf field string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

Required. The display name of the Model.

The name can be up to 128 characters long and can consist of any UTF-8 characters.

Generated from protobuf field string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

The description of the Model.

Generated from protobuf field string description = 3;

The description of the Model.

Generated from protobuf field string description = 3;

The description of this version.

Generated from protobuf field string version_description = 30;

The description of this version.

Generated from protobuf field string version_description = 30;

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain .

Generated from protobuf field .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain .

Generated from protobuf field .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

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.

Generated from protobuf field string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];

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.

Generated from protobuf field string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];

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.

Generated from protobuf field .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

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.

Generated from protobuf field .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

Generated from protobuf field repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

Generated from protobuf field repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

Generated from protobuf field string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = {

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

Generated from protobuf field string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = {

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.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

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.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

Immutable. The path to the directory containing the Model artifact and any of its supporting files.

Not present for AutoML Models.

Generated from protobuf field string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];

Immutable. The path to the directory containing the Model artifact and any of its supporting files.

Not present for AutoML Models.

Generated from protobuf field string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];

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 .

Generated from protobuf field repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

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 .

Generated from protobuf field repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

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 .

Generated from protobuf field repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

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 .

Generated from protobuf field repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];

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 .

Generated from protobuf field repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

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 .

Generated from protobuf field repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Timestamp when this Model was uploaded into Vertex AI.

Generated from protobuf field .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Timestamp when this Model was uploaded into Vertex AI.

Generated from protobuf field .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Timestamp when this Model was most recently updated.

Generated from protobuf field .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Timestamp when this Model was most recently updated.

Generated from protobuf field .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

Generated from protobuf field repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

Generated from protobuf field repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch 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 by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob .

Generated from protobuf field .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch 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 by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob .

Generated from protobuf field .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

Generated from protobuf field string etag = 16;

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

Generated from protobuf field string etag = 16;

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.

Generated from protobuf field map<string, string> labels = 17;

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.

Generated from protobuf field map<string, string> labels = 17;

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.

Generated from protobuf field .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

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.

Generated from protobuf field .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact} .

Generated from protobuf field string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact} .

Generated from protobuf field string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];