Class Model (1.73.0) 

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

Retrieves the model resource and instantiates its representation.

Parameters
Name Description
model_name str

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. May optionally contain a version ID or version alias in {model_name}@{version} form. See version arg.
project str

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

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

Optional. Version ID or version alias. When set, the specified model version will be targeted unless overridden in method calls. When not set, the model with the "default" alias will be targeted unless overridden in method calls. No behavior change if only one version of a model exists.

Properties

container_spec

The specification of the container that is to be used when deploying this Model. Not present for AutoML Models.

create_time

Time this resource was created.

description

Description of the model.

display_name

Display name of this resource.

encryption_spec

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

gca_resource

The underlying resource proto representation.

labels

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

name

Name of this resource.

predict_schemata

The schemata that describe formats of the Model's predictions and explanations, if available.

preview

Return a Model instance with preview features enabled.

resource_name

Full qualified resource name, without any version ID.

supported_deployment_resources_types

List of deployment resource types accepted for this Model.

When this Model is deployed, its prediction resources are described by the prediction_resources field of the objects returned by Endpoint.list_models(). 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 (Endpoint.predict() or Endpoint.explain()). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in Model.supported_input_storage_formats and Model.supported_output_storage_formats.

supported_export_formats

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>]}

supported_input_storage_formats

The formats this Model supports in the input_config field of a BatchPredictionJob. If Model.predict_schemata.instance_schema_uri exists, the instances should be given as per that schema.

Read the docs for more on batch prediction formats

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 Endpoint.predict() or Endpoint.explain().

supported_output_storage_formats

The formats this Model supports in the output_config field of a BatchPredictionJob.

If both Model.predict_schemata.instance_schema_uri and Model.predict_schemata.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).

Read the docs for more on batch prediction formats

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 Endpoint.predict() or Endpoint.explain().

training_job

The TrainingJob that uploaded this Model, if any.

Exceptions
Type Description
api_core.exceptions.NotFound If the Model's training job resource cannot be found on the Vertex service.

update_time

Time this resource was last updated.

uri

Path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models.

version_aliases

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.

version_create_time

Timestamp when this version was created.

version_description

The description of this version.

version_id

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.

version_update_time

Timestamp when this version was updated.

versioned_resource_name

The fully-qualified resource name, including the version ID. For example, projects/{project}/locations/{location}/models/{model_id}@{version_id}

versioning_registry

The registry of model versions associated with this Model instance.

Methods

batch_predict

batch_predict(
    job_display_name: typing.Optional[str] = None,
    gcs_source: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    bigquery_source: typing.Optional[str] = None,
    instances_format: str = "jsonl",
    gcs_destination_prefix: typing.Optional[str] = None,
    bigquery_destination_prefix: typing.Optional[str] = None,
    predictions_format: str = "jsonl",
    model_parameters: typing.Optional[typing.Dict] = None,
    machine_type: typing.Optional[str] = None,
    accelerator_type: typing.Optional[str] = None,
    accelerator_count: typing.Optional[int] = None,
    starting_replica_count: typing.Optional[int] = None,
    max_replica_count: typing.Optional[int] = None,
    generate_explanation: typing.Optional[bool] = False,
    explanation_metadata: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
    ] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    sync: bool = True,
    create_request_timeout: typing.Optional[float] = None,
    batch_size: typing.Optional[int] = None,
    service_account: typing.Optional[str] = None,
) -> google.cloud.aiplatform.jobs.BatchPredictionJob

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" )

Parameters
Name Description
job_display_name str

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.
gcs_source typing.Union[str, typing.Sequence[str], NoneType]

Optional[Sequence[str]] = None Google Cloud Storage URI(-s) to your instances to run batch prediction on. They must match instances_format.
bigquery_source typing.Optional[str]

Optional[str] = None BigQuery URI to a table, up to 2000 characters long. For example: bq://projectId.bqDatasetId.bqTableId
instances_format str

str = "jsonl" The format in which instances are provided. Must be one of the formats listed in Model.supported_input_storage_formats. Default is "jsonl" when using gcs_source. If a bigquery_source is provided, this is overridden to "bigquery".
gcs_destination_prefix typing.Optional[str]

Optional[str] = None The Google Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction-, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has ``google.rpc.Status __ containing only code and message fields.
bigquery_destination_prefix typing.Optional[str]

Optional[str] = None The BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: bq://projectId or bq://projectId.bqDatasetId. If no Dataset is specified, a new one is created with the name prediction_ where the table name is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has ``google.rpc.Status _ represented as a STRUCT, and containing only code and message.
predictions_format str

str = "jsonl" Required. The format in which Vertex AI outputs the predictions, must be one of the formats specified in Model.supported_output_storage_formats. Default is "jsonl" when using gcs_destination_prefix. If a bigquery_destination_prefix is provided, this is overridden to "bigquery".
model_parameters typing.Optional[typing.Dict]

Optional[Dict] = None Optional. The parameters that govern the predictions. The schema of the parameters may be specified via the Model's parameters_schema_uri.
machine_type typing.Optional[str]

Optional[str] = None Optional. The type of machine for running batch prediction on dedicated resources. Not specifying machine type will result in batch prediction job being run with automatic resources.
accelerator_type typing.Optional[str]

Optional[str] = None Optional. The type of accelerator(s) that may be attached to the machine as per accelerator_count. Only used if machine_type is set.
accelerator_count typing.Optional[int]

Optional[int] = None Optional. The number of accelerators to attach to the machine_type. Only used if machine_type is set.
starting_replica_count typing.Optional[int]

Optional[int] = None The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count. Only used if machine_type is set.
max_replica_count typing.Optional[int]

Optional[int] = None The maximum number of machine replicas the batch operation may be scaled to. Only used if machine_type is set. Default is 10.
generate_explanation bool

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.
explanation_metadata aiplatform.explain.ExplanationMetadata

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
explanation_parameters aiplatform.explain.ExplanationParameters

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
labels typing.Optional[typing.Dict[str, str]]

Optional[Dict[str, str]] = None Optional. The labels with user-defined metadata to organize your BatchPredictionJobs. 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.
credentials typing.Optional[google.auth.credentials.Credentials]

Optional[auth_credentials.Credentials] = None Optional. Custom credentials to use to create this batch prediction job. Overrides credentials set in aiplatform.init.
encryption_spec_key_name Optional[str]

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.
create_request_timeout float

Optional. The timeout for the create request in seconds.
batch_size int

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.
service_account str

Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.
Returns
Type Description
job (jobs.BatchPredictionJob) Instantiated representation of the created batch prediction job.

copy

copy(
    destination_location: str,
    destination_model_id: typing.Optional[str] = None,
    destination_parent_model: typing.Optional[str] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    copy_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.models.Model

Copys a model and returns a Model representing the copied Model resource. This method is a blocking call.

Example usage: copied_model = my_model.copy( destination_location="us-central1" )

Parameters
Name Description
destination_location str

The destination location to copy the model to.
destination_model_id str

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.
destination_parent_model str

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.
encryption_spec_key_name Optional[str]

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.
copy_request_timeout float

Optional. The timeout for the copy request in seconds.
Exceptions
Type Description
ValueError If both destination_model_id and destination_parent_model are set.
Returns
Type Description
model (aiplatform.Model) Instantiated representation of the copied model resource.

delete

delete(sync: bool = True) -> None

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

deploy

deploy(
    endpoint: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.models.Endpoint,
            google.cloud.aiplatform.models.PrivateEndpoint,
        ]
    ] = None,
    deployed_model_display_name: typing.Optional[str] = None,
    traffic_percentage: typing.Optional[int] = 0,
    traffic_split: typing.Optional[typing.Dict[str, int]] = None,
    machine_type: typing.Optional[str] = None,
    min_replica_count: int = 1,
    max_replica_count: int = 1,
    accelerator_type: typing.Optional[str] = None,
    accelerator_count: typing.Optional[int] = None,
    tpu_topology: typing.Optional[str] = None,
    service_account: typing.Optional[str] = None,
    explanation_metadata: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
    ] = None,
    metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    encryption_spec_key_name: typing.Optional[str] = None,
    network: typing.Optional[str] = None,
    sync=True,
    deploy_request_timeout: typing.Optional[float] = None,
    autoscaling_target_cpu_utilization: typing.Optional[int] = None,
    autoscaling_target_accelerator_duty_cycle: typing.Optional[int] = None,
    enable_access_logging=False,
    disable_container_logging: bool = False,
    private_service_connect_config: typing.Optional[
        google.cloud.aiplatform.models.PrivateEndpoint.PrivateServiceConnectConfig
    ] = None,
    deployment_resource_pool: typing.Optional[
        google.cloud.aiplatform.models.DeploymentResourcePool
    ] = None,
    reservation_affinity_type: typing.Optional[str] = None,
    reservation_affinity_key: typing.Optional[str] = None,
    reservation_affinity_values: typing.Optional[typing.List[str]] = None,
    spot: bool = False,
    fast_tryout_enabled: bool = False,
    system_labels: typing.Optional[typing.Dict[str, str]] = None,
) -> typing.Union[
    google.cloud.aiplatform.models.Endpoint,
    google.cloud.aiplatform.models.PrivateEndpoint,
]

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

Parameters
Name Description
endpoint Union[Endpoint, PrivateEndpoint]

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

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

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.
traffic_split Dict[str, int]

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.
machine_type str

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

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.
max_replica_count int

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.
accelerator_type str

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
accelerator_count int

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

Optional. The TPU topology to use for the DeployedModel. Requireid for CloudTPU multihost deployments.
service_account str

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.
explanation_metadata aiplatform.explain.ExplanationMetadata

Optional. Metadata describing the Model's input and output for explanation. explanation_metadata is optional while explanation_parameters must be specified when used. For more details, see Ref docs http://tinyurl.com/1igh60kt
explanation_parameters aiplatform.explain.ExplanationParameters

Optional. Parameters to configure explaining for Model's predictions. For more details, see Ref docs http://tinyurl.com/1an4zake
metadata Sequence[Tuple[str, str]]

Optional. Strings which should be sent along with the request as metadata.
encryption_spec_key_name Optional[str]

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.
network str

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 PrivateEndpoints in the documentation. Cannot be set together with private_service_connect_config.
deploy_request_timeout float

Optional. The timeout for the deploy request in seconds.
autoscaling_target_cpu_utilization int

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

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.
disable_container_logging bool

If True, container logs from the deployed model will not be written to Cloud Logging. Defaults to False.
private_service_connect_config PrivateEndpoint.PrivateServiceConnectConfig

If true, the endpoint can be accessible via Private Service Connect. Cannot be set together with network.
deployment_resource_pool DeploymentResourcePool

Resource pool where the model will be deployed. All models that are deployed to the same DeploymentResourcePool will be hosted in a shared model server. If provided, will override replica count arguments.
reservation_affinity_type str

Optional. The type of reservation affinity. One of NO_RESERVATION, ANY_RESERVATION, SPECIFIC_RESERVATION, SPECIFIC_THEN_ANY_RESERVATION, SPECIFIC_THEN_NO_RESERVATION
reservation_affinity_key str

Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.
reservation_affinity_values List[str]

Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation. Format: 'projects/{project_id_or_number}/zones/{zone}/reservations/{reservation_name}'
spot bool

Optional. Whether to schedule the deployment workload on spot VMs.
fast_tryout_enabled bool

Optional. Defaults to False. If True, model will be deployed using faster deployment path. Useful for quick experiments. Not for production workloads. Only available for most popular models with certain machine types.
system_labels Dict[str, str]

Optional. System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only.
sync bool

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.
enable_access_logging bool

Whether to enable endpoint access logging. Defaults to False.
Exceptions
Type Description
ValueError If traffic_split is set for PrivateEndpoint.
Returns
Type Description
endpoint (Union[Endpoint, PrivateEndpoint]) Endpoint with the deployed model.

evaluate

evaluate(
    prediction_type: str,
    target_field_name: str,
    gcs_source_uris: typing.Optional[typing.List[str]] = None,
    bigquery_source_uri: typing.Optional[str] = None,
    bigquery_destination_output_uri: typing.Optional[str] = None,
    class_labels: typing.Optional[typing.List[str]] = None,
    prediction_label_column: typing.Optional[str] = None,
    prediction_score_column: typing.Optional[str] = None,
    staging_bucket: typing.Optional[str] = None,
    service_account: typing.Optional[str] = None,
    generate_feature_attributions: bool = False,
    evaluation_pipeline_display_name: typing.Optional[str] = None,
    evaluation_metrics_display_name: typing.Optional[str] = None,
    network: typing.Optional[str] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    experiment: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.metadata.experiment_resources.Experiment, str
        ]
    ] = None,
    enable_caching: typing.Optional[bool] = None,
) -> google.cloud.aiplatform.model_evaluation.model_evaluation_job._ModelEvaluationJob

Creates a model evaluation job running on Vertex Pipelines and returns the resulting ModelEvaluationJob resource.

Example usage:

```
my_model = Model(
    model_name="projects/123/locations/us-central1/models/456"
)
my_evaluation_job = my_model.evaluate(
    prediction_type="classification",
    target_field_name="type",
    data_source_uris=["gs://sdk-model-eval/my-prediction-data.csv"],
    staging_bucket="gs://my-staging-bucket/eval_pipeline_root",
)
my_evaluation_job.wait()
my_evaluation = my_evaluation_job.get_model_evaluation()
my_evaluation.metrics
```
Parameters
Name Description
prediction_type str

Required. The problem type being addressed by this evaluation run. 'classification' and 'regression' are the currently supported problem types.
target_field_name str

Required. The column name of the field containing the label for this prediction task.
gcs_source_uris List[str]

Optional. A list of Cloud Storage data files containing the ground truth data to use for this evaluation job. These files should contain your model's prediction column. Currently only Google Cloud Storage urls are supported, for example: "gs://path/to/your/data.csv". The provided data files must be either CSV or JSONL. One of gcs_source_uris or bigquery_source_uri is required.
bigquery_source_uri str

Optional. A bigquery table URI containing the ground truth data to use for this evaluation job. This uri should be in the format 'bq://my-project-id.dataset.table'. One of gcs_source_uris or bigquery_source_uri is required.
bigquery_destination_output_uri str

Optional. A bigquery table URI where the Batch Prediction job associated with your Model Evaluation will write prediction output. This can be a BigQuery URI to a project ('bq://my-project'), a dataset ('bq://my-project.my-dataset'), or a table ('bq://my-project.my-dataset.my-table'). Required if bigquery_source_uri is provided.
class_labels List[str]

Optional. For custom (non-AutoML) classification models, a list of possible class names, in the same order that predictions are generated. This argument is required when prediction_type is 'classification'. For example, in a classification model with 3 possible classes that are outputted in the format: [0.97, 0.02, 0.01] with the class names "cat", "dog", and "fish", the value of class_labels should be ["cat", "dog", "fish"] where the class "cat" corresponds with 0.97 in the example above.
prediction_label_column str

Optional. The column name of the field containing classes the model is scoring. Formatted to be able to find nested columns, delimited by .. If not set, defaulted to prediction.classes for classification.
prediction_score_column str

Optional. The column name of the field containing batch prediction scores. Formatted to be able to find nested columns, delimited by .. If not set, defaulted to prediction.scores for a classification problem_type, prediction.value for a regression problem_type.
staging_bucket str

Optional. The GCS directory to use for staging files from this evaluation job. Defaults to the value set in aiplatform.init(staging_bucket=...) if not provided. Required if staging_bucket is not set in aiplatform.init().
service_account str

Specifies the service account for workload run-as account for this Model Evaluation PipelineJob. Users submitting jobs must have act-as permission on this run-as account. The service account running this Model Evaluation job needs the following permissions: Dataflow Worker, Storage Admin, Vertex AI Administrator, and Vertex AI Service Agent.
generate_feature_attributions boolean

Optional. Whether the model evaluation job should generate feature attributions. Defaults to False if not specified.
evaluation_pipeline_display_name str

Optional. The display name of your model evaluation job. This is the display name that will be applied to the Vertex Pipeline run for your evaluation job. If not set, a display name will be generated automatically.
evaluation_metrics_display_name str

Optional. The display name of the model evaluation resource uploaded to Vertex from your Model Evaluation pipeline.
network str

The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
encryption_spec_key_name str

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the job. 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 this is set, then all resources created by the PipelineJob for this Model Evaluation will be encrypted with the provided encryption key. If not specified, encryption_spec of original PipelineJob will be used.
experiment Union[str, experiments_resource.Experiment]

Optional. The Vertex AI experiment name or instance to associate to the PipelineJob executing this model evaluation job. Metrics produced by the PipelineJob as system.Metric Artifacts will be associated as metrics to the provided experiment, and parameters from this PipelineJob will be associated as parameters to the provided experiment.
enable_caching bool

Optional. Whether to turn on caching for the run. If this is not set, defaults to the compile time settings, which are True for all tasks by default, while users may specify different caching options for individual tasks. If this is set, the setting applies to all tasks in the pipeline. Overrides the compile time settings.
Exceptions
Type Description
ValueError If staging_bucket was not set in aiplatform.init() and staging_bucket was not provided. If the provided prediction_type is not valid. If the provided data_source_uris don't start with 'gs://'.
Returns
Type Description
model_evaluation.ModelEvaluationJob Instantiated representation of the _ModelEvaluationJob.

export_model

export_model(
    export_format_id: str,
    artifact_destination: typing.Optional[str] = None,
    image_destination: typing.Optional[str] = None,
    sync: bool = True,
) -> typing.Dict[str, str]

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.

Example 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"
)
Parameters
Name Description
export_format_id str

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.
artifact_destination str

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 in export_format_id contains ARTIFACT.
image_destination str

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.
sync bool

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.
Exceptions
Type Description
ValueError If model does not support exporting.
ValueError If invalid arguments or export formats are provided.
Returns
Type Description
output_info (Dict[str, str]) Details of the completed export with output destination paths to the artifacts or container image.

get_model_evaluation

get_model_evaluation(
    evaluation_id: typing.Optional[str] = None,
) -> typing.Optional[
    google.cloud.aiplatform.model_evaluation.model_evaluation.ModelEvaluation
]

Returns a ModelEvaluation resource and instantiates its representation. If no evaluation_id is passed, it will return the first evaluation associated with this model. If the aiplatform.Model resource was instantiated with a version, this will return a Model Evaluation from that version. If no version was specified when instantiating the Model resource, this will return an Evaluation from the default version.

Example usage: my_model = Model( model_name="projects/123/locations/us-central1/models/456" )

my_evaluation = my_model.get_model_evaluation(
    evaluation_id="789"
)

# If no arguments are passed, this method returns the first evaluation for the model
my_evaluation = my_model.get_model_evaluation()
Parameter
Name Description
evaluation_id str

Optional. The ID of the model evaluation to retrieve.
Returns
Type Description
model_evaluation.ModelEvaluation Instantiated representation of the ModelEvaluation resource.

list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.models.Model]

List all Model resource instances.

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

Parameters
Name Description
filter str

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

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
project str

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

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

Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.
Returns
Type Description
List[models.Model] A list of Model resource objects

list_model_evaluations

list_model_evaluations() -> (
    typing.List[
        google.cloud.aiplatform.model_evaluation.model_evaluation.ModelEvaluation
    ]
)

List all Model Evaluation resources associated with this model. If this Model resource was instantiated with a version, the Model Evaluation resources for that version will be returned. If no version was provided when the Model resource was instantiated, Model Evaluation resources will be returned for the default version.

Example Usage: my_model = Model( model_name="projects/123/locations/us-central1/models/456@1" )

my_evaluations = my_model.list_model_evaluations()
Returns
Type Description
List[model_evaluation.ModelEvaluation] List of ModelEvaluation resources for the model.

to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

update

update(
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
) -> google.cloud.aiplatform.models.Model

Updates a model.

Example usage: my_model = my_model.update( display_name="my-model", description="my description", labels={'key': 'value'}, )

Parameters
Name Description
display_name str

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

The description of the model.
labels Dict[str, str]

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. See https://goo.gl/xmQnxf for more information and examples of labels.
Exceptions
Type Description
ValueError If labels is not the correct format.
Returns
Type Description
model (aiplatform.Model) Updated model resource.

upload

upload(
    serving_container_image_uri: typing.Optional[str] = None,
    *,
    artifact_uri: typing.Optional[str] = None,
    model_id: typing.Optional[str] = None,
    parent_model: typing.Optional[str] = None,
    is_default_version: bool = True,
    version_aliases: typing.Optional[typing.Sequence[str]] = None,
    version_description: typing.Optional[str] = None,
    serving_container_predict_route: typing.Optional[str] = None,
    serving_container_health_route: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    serving_container_command: typing.Optional[typing.Sequence[str]] = None,
    serving_container_args: typing.Optional[typing.Sequence[str]] = None,
    serving_container_environment_variables: typing.Optional[
        typing.Dict[str, str]
    ] = None,
    serving_container_ports: typing.Optional[typing.Sequence[int]] = None,
    serving_container_grpc_ports: typing.Optional[typing.Sequence[int]] = None,
    local_model: typing.Optional[LocalModel] = None,
    instance_schema_uri: typing.Optional[str] = None,
    parameters_schema_uri: typing.Optional[str] = None,
    prediction_schema_uri: typing.Optional[str] = None,
    explanation_metadata: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
    ] = None,
    display_name: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    staging_bucket: typing.Optional[str] = None,
    sync=True,
    upload_request_timeout: typing.Optional[float] = None,
    serving_container_deployment_timeout: typing.Optional[int] = None,
    serving_container_shared_memory_size_mb: typing.Optional[int] = None,
    serving_container_startup_probe_exec: typing.Optional[typing.Sequence[str]] = None,
    serving_container_startup_probe_period_seconds: typing.Optional[int] = None,
    serving_container_startup_probe_timeout_seconds: typing.Optional[int] = None,
    serving_container_health_probe_exec: typing.Optional[typing.Sequence[str]] = None,
    serving_container_health_probe_period_seconds: typing.Optional[int] = None,
    serving_container_health_probe_timeout_seconds: typing.Optional[int] = None,
    model_garden_source_model_name: typing.Optional[str] = None
) -> Model

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" )

Exceptions
Type Description
ValueError If explanation_metadata is specified while explanation_parameters is not. Also if model directory does not contain a supported model file. If local_model is specified but serving_container_spec.image_uri in the local_model is None. If local_model is not specified and serving_container_image_uri is None.
Returns
Type Description
model (aiplatform.Model) Instantiated representation of the uploaded model resource.

upload_scikit_learn_model_file

upload_scikit_learn_model_file(
    model_file_path: str,
    sklearn_version: typing.Optional[str] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    model_id: typing.Optional[str] = None,
    parent_model: typing.Optional[str] = None,
    is_default_version: typing.Optional[bool] = True,
    version_aliases: typing.Optional[typing.Sequence[str]] = None,
    version_description: typing.Optional[str] = None,
    instance_schema_uri: typing.Optional[str] = None,
    parameters_schema_uri: typing.Optional[str] = None,
    prediction_schema_uri: typing.Optional[str] = None,
    explanation_metadata: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
    ] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    staging_bucket: typing.Optional[str] = None,
    sync=True,
    upload_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.models.Model

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

Example usage: my_model = Model.upload_scikit_learn_model_file( model_file_path="iris.sklearn_model.joblib" )

Exceptions
Type Description
ValueError If explanation_metadata is specified while explanation_parameters is not. Also if model directory does not contain a supported model file.
Returns
Type Description
model (aiplatform.Model) Instantiated representation of the uploaded model resource.

upload_tensorflow_saved_model

upload_tensorflow_saved_model(
    saved_model_dir: str,
    tensorflow_version: typing.Optional[str] = None,
    use_gpu: bool = False,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    model_id: typing.Optional[str] = None,
    parent_model: typing.Optional[str] = None,
    is_default_version: typing.Optional[bool] = True,
    version_aliases: typing.Optional[typing.Sequence[str]] = None,
    version_description: typing.Optional[str] = None,
    instance_schema_uri: typing.Optional[str] = None,
    parameters_schema_uri: typing.Optional[str] = None,
    prediction_schema_uri: typing.Optional[str] = None,
    explanation_metadata: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
    ] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    staging_bucket: typing.Optional[str] = None,
    sync=True,
    upload_request_timeout: typing.Optional[str] = None,
) -> google.cloud.aiplatform.models.Model

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

Example usage: my_model = Model.upload_scikit_learn_model_file( model_file_path="iris.tensorflow_model.SavedModel" )

Parameters
Name Description
upload_request_timeout float

Optional. The timeout for the upload request in seconds.
saved_model_dir str

Required. Local directory of the Tensorflow SavedModel.
tensorflow_version str

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.
use_gpu bool

Whether to use GPU for model serving.
display_name str

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.
description str

The description of the model.
model_id str

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.
parent_model str

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.
is_default_version bool

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.
version_aliases Sequence[str]

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 is a-z][a-zA-Z0-9-]{0,126}[a-z0-9]
version_description str

Optional. The description of the model version being uploaded.
instance_schema_uri str

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.
parameters_schema_uri str

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.
prediction_schema_uri str

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.
explanation_metadata aiplatform.explain.ExplanationMetadata

Optional. Metadata describing the Model's input and output for explanation. explanation_metadata is optional while explanation_parameters must be specified when used. For more details, see Ref docs http://tinyurl.com/1igh60kt
explanation_parameters aiplatform.explain.ExplanationParameters

Optional. Parameters to configure explaining for Model's predictions. For more details, see Ref docs http://tinyurl.com/1an4zake
labels Dict[str, str]

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. See https://goo.gl/xmQnxf for more information and examples of labels.
encryption_spec_key_name Optional[str]

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.
staging_bucket str

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

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.
Exceptions
Type Description
ValueError If explanation_metadata is specified while explanation_parameters is not. Also if model directory does not contain a supported model file.
Returns
Type Description
model (aiplatform.Model) Instantiated representation of the uploaded model resource.

upload_xgboost_model_file

upload_xgboost_model_file(
    model_file_path: str,
    xgboost_version: typing.Optional[str] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    model_id: typing.Optional[str] = None,
    parent_model: typing.Optional[str] = None,
    is_default_version: typing.Optional[bool] = True,
    version_aliases: typing.Optional[typing.Sequence[str]] = None,
    version_description: typing.Optional[str] = None,
    instance_schema_uri: typing.Optional[str] = None,
    parameters_schema_uri: typing.Optional[str] = None,
    prediction_schema_uri: typing.Optional[str] = None,
    explanation_metadata: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
    ] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    staging_bucket: typing.Optional[str] = None,
    sync=True,
    upload_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.models.Model

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

Example usage: my_model = Model.upload_xgboost_model_file( model_file_path="iris.xgboost_model.bst" )

Exceptions
Type Description
ValueError If model directory does not contain a supported model file.
Returns
Type Description
model (aiplatform.Model) Instantiated representation of the uploaded model resource.

wait

wait()

Helper method that blocks until all futures are complete.