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. |