Class DeployedModel (0.4.0)

Version 0.4.0

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DeployedModel(mapping=None, *, ignore_unknown_fields=False, **kwargs)

A deployment of a Model. Endpoints contain one or more DeployedModels.

Attributes

Name	Description
dedicated_resources	`.machine_resources.DedicatedResources` A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
automatic_resources	`.machine_resources.AutomaticResources` A description of resources that to large degree are decided by AI Platform, and require only a modest additional configuration.
id	str Output only. The ID of the DeployedModel.
model	str Required. The name of the Model this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.
display_name	str The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
create_time	`.timestamp.Timestamp` Output only. Timestamp when the DeployedModel was created.
explanation_spec	`.explanation.ExplanationSpec` Explanation configuration for this DeployedModel. When deploying a Model using ``EndpointService.DeployModel``, this value overrides the value of ``Model.explanation_spec``. All fields of ``explanation_spec`` are optional in the request. If a field of ``explanation_spec`` is not populated, the value of the same field of ``Model.explanation_spec`` is inherited. The corresponding ``Model.explanation_spec`` must be populated, otherwise explanation for this Model is not allowed.
service_account	str The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the ``iam.serviceAccounts.actAs`` permission on this service account.
enable_container_logging	bool If true, the container of the DeployedModel instances will send ``stderr`` and ``stdout`` streams to Stackdriver Logging. Only supported for custom-trained Models and AutoML Tables Models.
enable_access_logging	bool These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

Inheritance

builtins.object > proto.message.Message > DeployedModel