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Showing content from https://cloud.google.com/python/docs/reference/aiplatform/1.0.1/google.cloud.aiplatform.Model below:

Class Model (1.0.1) | Python client library

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Model(
    model_name: str,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
)

Retrieves the model resource and instantiates its representation.

Required. A fully-qualified model resource name or model ID. Example: "projects/123/locations/us-central1/models/456" or "456" when project and location are initialized or passed.

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

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

Description of the model.

The formats and content types in which this Model may be exported. If empty, this Model is not available for export.

For example, if this model can be exported as a Tensorflow SavedModel and have the artifacts written to Cloud Storage, the expected value would be:

{'tf-saved-model': [<ExportableContent.ARTIFACT: 1>]}

Uri of the model.

batch_predict(
    job_display_name: str,
    gcs_source: Optional[Union[str, Sequence[str]]] = None,
    bigquery_source: Optional[str] = None,
    instances_format: str = "jsonl",
    gcs_destination_prefix: Optional[str] = None,
    bigquery_destination_prefix: Optional[str] = None,
    predictions_format: str = "jsonl",
    model_parameters: Optional[Dict] = None,
    machine_type: Optional[str] = None,
    accelerator_type: Optional[str] = None,
    accelerator_count: Optional[int] = None,
    starting_replica_count: Optional[int] = None,
    max_replica_count: Optional[int] = None,
    generate_explanation: Optional[bool] = False,
    explanation_metadata: Optional[
        google.cloud.aiplatform_v1beta1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationParameters
    ] = None,
    labels: Optional[dict] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: Optional[str] = None,
    sync: bool = True,
)

Creates a batch prediction job using this Model and outputs prediction results to the provided destination prefix in the specified predictions_format. One source and one destination prefix are required.

Example usage:

my_model.batch_predict( job_display_name="prediction-123", gcs_source="gs://example-bucket/instances.csv", instances_format="csv", bigquery_destination_prefix="projectId.bqDatasetId.bqTableId" )

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 the prediction_format: - bigquery: output includes a column named explanation. The value is a struct that conforms to the [aiplatform.gapic.Explanation] object. - jsonl: The JSON objects on each line include an additional entry keyed explanation. 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 if generate_explanation is set to True. This value overrides the value of Model.explanation_metadata. All fields of explanation_metadata are optional in the request. If a field of the explanation_metadata object is not populated, the corresponding field of the Model.explanation_metadata object is inherited. For more details, see Ref docs <http://tinyurl.com/1igh60kt>

Optional. Parameters to configure explaining for Model's predictions. Can be specified only if generate_explanation is set to True. This value overrides the value of Model.explanation_parameters. All fields of explanation_parameters are optional in the request. If a field of the explanation_parameters object is not populated, the corresponding field of the Model.explanation_parameters object is inherited. For more details, see Ref 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.

deploy(
    endpoint: Optional[google.cloud.aiplatform.models.Endpoint] = None,
    deployed_model_display_name: Optional[str] = None,
    traffic_percentage: Optional[int] = 0,
    traffic_split: Optional[Dict[str, int]] = None,
    machine_type: Optional[str] = None,
    min_replica_count: int = 1,
    max_replica_count: int = 1,
    accelerator_type: Optional[str] = None,
    accelerator_count: Optional[int] = None,
    service_account: Optional[str] = None,
    explanation_metadata: Optional[
        google.cloud.aiplatform_v1beta1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationParameters
    ] = None,
    metadata: Optional[Sequence[Tuple[str, str]]] = (),
    encryption_spec_key_name: Optional[str] = None,
    sync=True,
)

Deploys model to endpoint. Endpoint will be created if unspecified.

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 the iam.serviceAccounts.actAs permission on this service account.

Optional. Metadata describing the Model's input and output for explanation. Both explanation_metadata and explanation_parameters must be passed together when used. For more details, see Ref docs <http://tinyurl.com/1igh60kt>

Optional. Parameters to configure explaining for Model's predictions. For more details, see Ref 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.

export_model(
    export_format_id: str,
    artifact_destination: Optional[str] = None,
    image_destination: Optional[str] = None,
    sync: bool = True,
)

Exports a trained, exportable Model to a location specified by the user. A Model is considered to be exportable if it has at least one supported_export_formats. Either artifact_destination or image_destination must be provided.

Usage: my_model.export( export_format_id='tf-saved-model' artifact_destination='gs://my-bucket/models/' )

or

my_model.export(
    export_format_id='custom-model'
    image_destination='us-central1-docker.pkg.dev/projectId/repo/image'
)

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 calling Model.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 in export_format_id contains ARTIFACT.

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 in export_format_id contains IMAGE.

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.

list(
    filter: Optional[str] = None,
    order_by: Optional[str] = None,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
)

List all Model resource instances.

Example Usage:

aiplatform.Model.list( filter='labels.my_label="my_label_value" AND display_name="my_model"', )

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.

upload(
    display_name: str,
    serving_container_image_uri: str,
    *,
    artifact_uri: Optional[str] = None,
    serving_container_predict_route: Optional[str] = None,
    serving_container_health_route: Optional[str] = None,
    description: Optional[str] = None,
    serving_container_command: Optional[Sequence[str]] = None,
    serving_container_args: Optional[Sequence[str]] = None,
    serving_container_environment_variables: Optional[Dict[str, str]] = None,
    serving_container_ports: Optional[Sequence[int]] = None,
    instance_schema_uri: Optional[str] = None,
    parameters_schema_uri: Optional[str] = None,
    prediction_schema_uri: Optional[str] = None,
    explanation_metadata: Optional[
        google.cloud.aiplatform_v1beta1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationParameters
    ] = None,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: Optional[str] = None,
    sync=True
)

Uploads a model and returns a Model representing the uploaded Model resource.

Example usage:

my_model = Model.upload( display_name='my-model', artifact_uri='gs://my-model/saved-model' serving_container_image_uri='tensorflow/serving' )

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 in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema 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 via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema 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 via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema 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. Both explanation_metadata and explanation_parameters must be passed together when used. For more details, see Ref docs <http://tinyurl.com/1igh60kt>

Optional. Parameters to configure explaining for Model's predictions. For more details, see Ref 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.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-14 UTC.

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