Required. 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 docs <http://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 docs <http://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. 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. Bothexplanation_metadata andexplanation_parameters must be passed together when used. For more details, seeRef docs <http://tinyurl.com/1igh60kt>

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docs <http://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 Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name 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.

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-<model-display-name>-<timestamp-of-export-call>", 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. 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.

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.

Required. The URI of the Model serving container.

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. 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. 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 Object <https://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 Object <https://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 Object <https://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. Bothexplanation_metadata andexplanation_parameters must be passed together when used. For more details, seeRef docs <http://tinyurl.com/1igh60kt>

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docs <http://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.

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. 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 Object <https://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 Object <https://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 Object <https://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. Bothexplanation_metadata andexplanation_parameters must be passed together when used. For more details, seeRef docs <http://tinyurl.com/1igh60kt>

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docs <http://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.

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. 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 Object <https://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 Object <https://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 Object <https://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. Bothexplanation_metadata andexplanation_parameters must be passed together when used. For more details, seeRef docs <http://tinyurl.com/1igh60kt>

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docs <http://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.

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. 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 Object <https://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 Object <https://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 Object <https://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. Bothexplanation_metadata andexplanation_parameters must be passed together when used. For more details, seeRef docs <http://tinyurl.com/1igh60kt>

Optional. Parameters to configure explaining for Model's predictions. For more details, seeRef docs <http://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.