Class Model (0.7.1) 

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

Retrieves the model resource and instantiates its representation.

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.
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.

Inheritance

builtins.object > google.cloud.aiplatform.base.AiPlatformResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.AiPlatformResourceNounWithFutureManager > Model

Properties

description

Description of the model.

uri

Uri of the model.

Methods

batch_predict

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

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

Example usage:

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

Parameters
Name Description
job_display_name str

Required. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.
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 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 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>
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.
Returns
Type Description
(jobs.BatchPredictionJob) Instantiated representation of the created batch prediction job.

deploy

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

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

Parameters
Name Description
endpoint "Endpoint"

Optional. 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, TPU_V2, TPU_V3
accelerator_count int

Optional. The number of accelerators to attach to a worker replica.
explanation_metadata explain.ExplanationMetadata

Optional. Metadata describing the Model's input and output for explanation. Both explanation_metadata and explanation_parameters must be passed together when used. For more details, see Ref docs <http://tinyurl.com/1igh60kt>
explanation_parameters 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 Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name 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.
Returns
Type Description
endpoint ("Endpoint") Endpoint with the deployed model.

list

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

List all Model resource instances.

Example Usage:

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

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.

upload

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

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

Example usage:

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

Parameters
Name Description
display_name str

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

Required. The URI of the Model serving container.
artifact_uri str

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.
serving_container_predict_route str

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 AI Platform.
serving_container_health_route str

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 AI Platform.
description str

The description of the model.
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 explain.ExplanationMetadata

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

Optional. Parameters to configure explaining for Model's predictions. For more details, see Ref docs <http://tinyurl.com/1an4zake>
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.
Exceptions
Type Description
ValueErro if only `explanation_metadata` or `explanation_parameters`: is specified.
Returns
Type Description
model Instantiated representation of the uploaded model resource.