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Showing content from https://cloud.google.com/python/docs/reference/aiplatform/1.50.0/google.cloud.aiplatform.Model below:

Class Model (1.50.0) | Python client library

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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.

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.

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.

Optional[auth_credentials.Credentials]=None, Custom credentials to use to upload this model. If not set, credentials set in aiplatform.init will be used.

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.

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

Time this resource was created.

Description of the model.

Display name of this resource.

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.

The underlying resource proto representation.

User-defined labels containing metadata about this resource.

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

Name of this resource.

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

Return a Model instance with preview features enabled.

Full qualified resource name, without any version ID.

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.

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>]}

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().

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().

The TrainingJob that uploaded this Model, if any.

Time this resource was last updated.

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

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.

Timestamp when this version was created.

The description of this version.

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.

Timestamp when this version was updated.

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

The registry of model versions associated with this Model instance.

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" )

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

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

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

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".

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.

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.

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".

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.

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.

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.

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

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.

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.

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[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.

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

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

Optional. The timeout for the create request in seconds.

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

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

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" )

The destination location to copy the model to.

Optional. The ID to use for the copied Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen. Only set this field when copying as a new model. If this field is not set, a numeric model id will be generated.

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

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

Optional. The timeout for the copy request in seconds.

delete(sync: bool = True) -> None

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

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

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,
) -> typing.Union[
    google.cloud.aiplatform.models.Endpoint,
    google.cloud.aiplatform.models.PrivateEndpoint,
]

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

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

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

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

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

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

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

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

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

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

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

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. explanation_metadata is optional while explanation_parameters must be specified 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 Endpoint and all sub-resources of this Endpoint will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

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

Optional. The timeout for the deploy request in seconds.

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

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

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

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

Whether to enable endpoint access logging. Defaults to False.

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,
) -> 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
```

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

Required. The column name of the field containing the label for this prediction task.

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.

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.

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.

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.

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

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

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().

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.

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

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.

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

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.

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.

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.

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"
)

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-", 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.

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()

Optional. The ID of the model evaluation to retrieve.

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"', )

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.

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()

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

Returns the resource proto as a dictionary.

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'}, )

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

The description of the model.

Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

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

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" )

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

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

Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

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

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

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

Optional. The description of the model version being uploaded.

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

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

The description of the model.

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

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used 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. explanation_metadata is optional while explanation_parameters must be specified 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 display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

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

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

Optional. The timeout for the upload request in seconds.

Optional. Deployment timeout in seconds.

Optional. The amount of the VM memory to reserve as the shared memory for the model in megabytes.

Optional. Exec specifies the action to take. Used by startup probe. An example of this argument would be ["cat", "/tmp/healthy"]

Optional. How often (in seconds) to perform the startup probe. Default to 10 seconds. Minimum value is 1.

Optional. Number of seconds after which the startup probe times out. Defaults to 1 second. Minimum value is 1.

Optional. Exec specifies the action to take. Used by health probe. An example of this argument would be ["cat", "/tmp/healthy"]

Optional. How often (in seconds) to perform the health probe. Default to 10 seconds. Minimum value is 1.

Optional. Number of seconds after which the health probe times out. Defaults to 1 second. Minimum value is 1.

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" )

Required. Local file path of the model.

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

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

The description of the model.

Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

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

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

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

Optional. The description of the model version being uploaded.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used 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. explanation_metadata is optional while explanation_parameters must be specified 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 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.

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

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

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

Optional. The timeout for the upload request in seconds.

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" )

Required. Local directory of the Tensorflow SavedModel.

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

Whether to use GPU for model serving.

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

The description of the model.

Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

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

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

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

Optional. The description of the model version being uploaded.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used 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. explanation_metadata is optional while explanation_parameters must be specified 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[str]=None, Project to upload this model to. Overrides project set in aiplatform.init.

Optional[str]=None, Location to upload this model to. Overrides location set in aiplatform.init.

Optional[auth_credentials.Credentials]=None, Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init.

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.

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

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

Optional. The timeout for the upload request in seconds.

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

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" )

Required. Local file path of the model.

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

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

The description of the model.

Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

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

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

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

Optional. The description of the model version being uploaded.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used 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. explanation_metadata is optional while explanation_parameters must be specified 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 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.

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

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

Optional. The timeout for the upload request in seconds.

Helper method that blocks until all futures are complete.

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Last updated 2025-08-14 UTC.

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