Optional. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

Optional. Generate explanation along with the batch prediction results. This will cause the batch prediction output to include explanations based on theprediction_format: -bigquery: output includes a column namedexplanation. The value is a struct that conforms to the [aiplatform.gapic.Explanation] object. -jsonl: The JSON objects on each line include an additional entry keyedexplanation. The value of the entry is a JSON object that conforms to the [aiplatform.gapic.Explanation] object. -csv: Generating explanations for CSV format is not supported.

Optional. Explanation metadata configuration for this BatchPredictionJob. Can be specified only ifgenerate_explanation is set toTrue. This value overrides the value ofModel.explanation_metadata. All fields ofexplanation_metadata are optional in the request. If a field of theexplanation_metadata object is not populated, the corresponding field of theModel.explanation_metadata object is inherited. For more details, seeRef docshttp://tinyurl.com/1igh60kt

Optional. Parameters to configure explaining for Model's predictions. Can be specified only ifgenerate_explanation is set toTrue. This value overrides the value ofModel.explanation_parameters. All fields ofexplanation_parameters are optional in the request. If a field of theexplanation_parameters object is not populated, the corresponding field of theModel.explanation_parameters object is inherited. For more details, seeRef docshttp://tinyurl.com/1an4zake

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form:projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. The timeout for the create request in seconds.

Optional. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.

Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

The destination location to copy the model to.

Optional. The ID to use for the copied 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. Only set this field when copying as a new model. If this field is not set, a numeric model id will be generated.

Optional. The resource name or model ID of an existing model that the newly-copied model will be a version of. Only set this field when copying as a new version of an existing model.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form:projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. The timeout for the copy request in seconds.

Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Optional. Public or private Endpoint to deploy model to. If not specified, endpoint display name will be model display name+'_endpoint'.

Optional. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.

Optional. Desired traffic to newly deployed model. Defaults to 0 if there are pre-existing deployed models. Defaults to 100 if there are no pre-existing deployed models. Negative values should not be provided. Traffic of previously deployed models at the endpoint will be scaled down to accommodate new deployed model's traffic. Should not be provided if traffic_split is provided.

Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided.

Optional. The type of machine. Not specifying machine type will result in model to be deployed with automatic resources.

Optional. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.

Optional. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the smaller value of min_replica_count or 1 will be used.

Optional. Hardware accelerator type. Must also set accelerator_count if used. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4

Optional. The number of accelerators to attach to a worker replica.

The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have theiam.serviceAccounts.actAs permission on this service account.

Optional. Metadata describing the Model's input and output for explanation.explanation_metadata is optional whileexplanation_parameters must be specified when used. For more details, seeRef docshttp://tinyurl.com/1igh60kt

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docshttp://tinyurl.com/1an4zake

Optional. Strings which should be sent along with the request as metadata.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form:projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. The full name of the Compute Engine network to which the Endpoint, if created, will be peered to. E.g. "projects/12345/global/networks/myVPC". Private services access must already be configured for the network. If set or aiplatform.init(network=...) has been set, a PrivateEndpoint will be created. If left unspecified, an Endpoint will be created. Read more about PrivateEndpointsin the documentation.

Optional. The timeout for the deploy request in seconds.

Optional. Target CPU Utilization to use for Autoscaling Replicas. A default value of 60 will be used if not specified.

Optional. Target Accelerator Duty Cycle. Must also set accelerator_type and accelerator_count if specified. A default value of 60 will be used if not specified.

Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Whether to enable endpoint access logging. Defaults to False.

Required. The ID of the format in which the Model must be exported. The list of export formats that this Model supports can be found by callingModel.supported_export_formats.

The Cloud Storage location where the Model artifact is to be written to. Under the directory given as the destination a new one with name "model-export-", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format, will be created. Inside, the Model and any of its supporting files will be written. This field should only be set when, in [Model.supported_export_formats], the value for the key given inexport_format_id containsARTIFACT.

The Google Container Registry or Artifact Registry URI where the Model container image will be copied to. Accepted forms: - Google Container Registry path. For example:gcr.io/projectId/imageName:tag. - Artifact Registry path. For example:us-central1-docker.pkg.dev/projectId/repoName/imageName:tag. This field should only be set when, in [Model.supported_export_formats], the value for the key given inexport_format_id containsIMAGE.

Whether to execute this export synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Optional. The ID of the model evaluation to retrieve.

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields:display_name,create_time,update_time

Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used.

Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used.

Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.

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

The description of the model.

Optional. 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. Seehttps://goo.gl/xmQnxf for more information and examples of labels.

Optional. The URI of the Model serving container. This parameter is required if the parameterlocal_model is not specified.

Optional. The path to the directory containing the Model artifact and any of its supporting files. Leave blank for custom container prediction. Not present for AutoML Models.

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.

Optional. The resource name or model ID of an existing model that the newly-uploaded model will be a version of. Only set this field when uploading a new version of an existing model.

Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of this model without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the newly-uploaded model version will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased.

Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format isa-z][a-zA-Z0-9-]{0,126}[a-z0-9]

Optional. The description of the model version being uploaded.

Optional. An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI.

Optional. An HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by Vertex AI.

The description of the model.

Optional. A LocalModel instance that includes aserving_container_spec. If provided, theserving_container_spec of the LocalModel instance will overwrite the values of all other serving container parameters.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used inPredictRequest.instances,ExplainRequest.instances andBatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. 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.

Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation viaPredictRequest.parameters,ExplainRequest.parameters andBatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it 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.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned viaPredictResponse.predictions,ExplainResponse.explanations, andBatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. 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.

Optional. Metadata describing the Model's input and output for explanation.explanation_metadata is optional whileexplanation_parameters must be specified when used. For more details, seeRef docshttp://tinyurl.com/1igh60kt

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docshttp://tinyurl.com/1an4zake

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

Optional. 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. Seehttps://goo.gl/xmQnxf for more information and examples of labels.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form:projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init.

Optional. The timeout for the upload request in seconds.

Required. Local file path of the model.

Optional. The version of the Scikit-learn serving container. Supported versions: ["0.20", "0.22", "0.23", "0.24", "1.0"]. If the version is not specified, the latest version is used.

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

The description of the model.

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.

Optional. The resource name or model ID of an existing model that the newly-uploaded model will be a version of. Only set this field when uploading a new version of an existing model.

Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of this model without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the newly-uploaded model version will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased.

Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format isa-z][a-zA-Z0-9-]{0,126}[a-z0-9]

Optional. The description of the model version being uploaded.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used inPredictRequest.instances,ExplainRequest.instances andBatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. 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.

Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation viaPredictRequest.parameters,ExplainRequest.parameters andBatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it 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.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned viaPredictResponse.predictions,ExplainResponse.explanations, andBatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. 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.

Optional. Metadata describing the Model's input and output for explanation.explanation_metadata is optional whileexplanation_parameters must be specified when used. For more details, seeRef docshttp://tinyurl.com/1igh60kt

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docshttp://tinyurl.com/1an4zake

Optional. 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. Seehttps://goo.gl/xmQnxf for more information and examples of labels.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form:projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init.

Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Optional. The timeout for the upload request in seconds.

Required. Local directory of the Tensorflow SavedModel.

Optional. The version of the Tensorflow serving container. Supported versions: ["0.15", "2.1", "2.2", "2.3", "2.4", "2.5", "2.6", "2.7"]. If the version is not specified, the latest version is used.

Whether to use GPU for model serving.

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

The description of the model.

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.

Optional. The resource name or model ID of an existing model that the newly-uploaded model will be a version of. Only set this field when uploading a new version of an existing model.

Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of this model without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the newly-uploaded model version will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased.

Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format isa-z][a-zA-Z0-9-]{0,126}[a-z0-9]

Optional. The description of the model version being uploaded.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used inPredictRequest.instances,ExplainRequest.instances andBatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. 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.

Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation viaPredictRequest.parameters,ExplainRequest.parameters andBatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it 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.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned viaPredictResponse.predictions,ExplainResponse.explanations, andBatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. 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.

Optional. Metadata describing the Model's input and output for explanation.explanation_metadata is optional whileexplanation_parameters must be specified when used. For more details, seeRef docshttp://tinyurl.com/1igh60kt

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docshttp://tinyurl.com/1an4zake

Optional. 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. Seehttps://goo.gl/xmQnxf for more information and examples of labels.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form:projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init.

Optional. The timeout for the upload request in seconds.

Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Required. Local file path of the model.

Optional. The version of the XGBoost serving container. Supported versions: ["0.82", "0.90", "1.1", "1.2", "1.3", "1.4"]. If the version is not specified, the latest version is used.

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

The description of the model.

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.

Optional. The resource name or model ID of an existing model that the newly-uploaded model will be a version of. Only set this field when uploading a new version of an existing model.

Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of this model without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the newly-uploaded model version will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased.

Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format isa-z][a-zA-Z0-9-]{0,126}[a-z0-9]

Optional. The description of the model version being uploaded.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used inPredictRequest.instances,ExplainRequest.instances andBatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. 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.

Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation viaPredictRequest.parameters,ExplainRequest.parameters andBatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it 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.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned viaPredictResponse.predictions,ExplainResponse.explanations, andBatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2Schema Objecthttps://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. 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.

Optional. Metadata describing the Model's input and output for explanation.explanation_metadata is optional whileexplanation_parameters must be specified when used. For more details, seeRef docshttp://tinyurl.com/1igh60kt

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docshttp://tinyurl.com/1an4zake

Optional. 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. Seehttps://goo.gl/xmQnxf for more information and examples of labels.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form:projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init.

Optional. The timeout for the upload request in seconds.