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Model(
model_name: str,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
Retrieves the model resource and instantiates its representation.
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.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > ModelProperties
description
Description of the model.
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>]}
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" )
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 |
explanation_metadata |
explain.ExplanationMetadata
Optional. Explanation metadata configuration for this BatchPredictionJob. Can be specified only if |
explanation_parameters |
explain.ExplanationParameters
Optional. Parameters to configure explaining for Model's predictions. Can be specified only if |
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: |
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: int = 1,
max_replica_count: int = 1,
accelerator_type: Optional[str] = None,
accelerator_count: Optional[int] = None,
service_account: Optional[str] = None,
explanation_metadata: Optional[
google.cloud.aiplatform_v1beta1.types.explanation_metadata.ExplanationMetadata
] = None,
explanation_parameters: Optional[
google.cloud.aiplatform_v1beta1.types.explanation.ExplanationParameters
] = None,
metadata: Optional[Sequence[Tuple[str, str]]] = (),
encryption_spec_key_name: Optional[str] = None,
sync=True,
)
Deploys model to endpoint. Endpoint will be created if unspecified.
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 |
accelerator_count |
int
Optional. The number of accelerators to attach to a worker replica. |
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 |
explanation_metadata |
explain.ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation. Both |
explanation_parameters |
explain.ExplanationParameters
Optional. Parameters to configure explaining for Model's predictions. For more details, see |
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: |
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. |
Type | Description |
endpoint ("Endpoint") | Endpoint with the deployed model. |
export_model
export_model(
export_format_id: str,
artifact_destination: Optional[str] = None,
image_destination: Optional[str] = None,
sync: bool = True,
)
Exports a trained, exportable Model to a location specified by the user.
A Model is considered to be exportable if it has at least one supported_export_formats
.
Either artifact_destination
or image_destination
must be provided.
Usage: my_model.export( export_format_id='tf-saved-model' artifact_destination='gs://my-bucket/models/' )
or
my_model.export(
export_format_id='custom-model'
image_destination='us-central1-docker.pkg.dev/projectId/repo/image'
)
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 |
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 " |
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: |
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. |
Type | Description |
ValueErro | if model does not support exporting.: |
ValueErro | if invalid arguments or export formats are provided.: |
Type | Description |
output_info (Dict[str, str]) | Details of the completed export with output destination paths to the artifacts or container image. |
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"', )
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: |
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' )
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 Vertex AI. |
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 Vertex AI. |
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 |
parameters_schema_uri |
str
Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via |
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 |
explanation_metadata |
explain.ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation. Both |
explanation_parameters |
explain.ExplanationParameters
Optional. Parameters to configure explaining for Model's predictions. For more details, see |
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: |
Type | Description |
ValueError | If only `explanation_metadata` or `explanation_parameters` is specified. |
Type | Description |
model | Instantiated representation of the uploaded model resource. |