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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::DeployedModel.
A deployment of a Model. Endpoints contain one or more DeployedModels.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#automatic_resources
def automatic_resources() -> ::Google::Cloud::AIPlatform::V1::AutomaticResources- (::Google::Cloud::AIPlatform::V1::AutomaticResources) — A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
#automatic_resources=
def automatic_resources=(value) -> ::Google::Cloud::AIPlatform::V1::AutomaticResources- value (::Google::Cloud::AIPlatform::V1::AutomaticResources) — A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- (::Google::Cloud::AIPlatform::V1::AutomaticResources) — A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
#create_time
def create_time() -> ::Google::Protobuf::Timestamp- (::Google::Protobuf::Timestamp) — Output only. Timestamp when the DeployedModel was created.
#dedicated_resources
def dedicated_resources() -> ::Google::Cloud::AIPlatform::V1::DedicatedResources- (::Google::Cloud::AIPlatform::V1::DedicatedResources) — A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
#dedicated_resources=
def dedicated_resources=(value) -> ::Google::Cloud::AIPlatform::V1::DedicatedResources- value (::Google::Cloud::AIPlatform::V1::DedicatedResources) — A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
- (::Google::Cloud::AIPlatform::V1::DedicatedResources) — A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
#disable_container_logging
def disable_container_logging() -> ::Boolean-
(::Boolean) — For custom-trained Models and AutoML Tabular Models, the container of the
DeployedModel instances will send
stderrandstdoutstreams to Stackdriver Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing.User can disable container logging by setting this flag to true.
#disable_container_logging=
def disable_container_logging=(value) -> ::Boolean-
value (::Boolean) — For custom-trained Models and AutoML Tabular Models, the container of the
DeployedModel instances will send
stderrandstdoutstreams to Stackdriver Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing.User can disable container logging by setting this flag to true.
-
(::Boolean) — For custom-trained Models and AutoML Tabular Models, the container of the
DeployedModel instances will send
stderrandstdoutstreams to Stackdriver Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing.User can disable container logging by setting this flag to true.
#display_name
def display_name() -> ::String- (::String) — The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
#display_name=
def display_name=(value) -> ::String- value (::String) — The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
- (::String) — The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
#enable_access_logging
def enable_access_logging() -> ::Boolean-
(::Boolean) — If true, online prediction access logs are sent to StackDriver
Logging.
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.
#enable_access_logging=
def enable_access_logging=(value) -> ::Boolean-
value (::Boolean) — If true, online prediction access logs are sent to StackDriver
Logging.
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.
-
(::Boolean) — If true, online prediction access logs are sent to StackDriver
Logging.
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.
#explanation_spec
def explanation_spec() -> ::Google::Cloud::AIPlatform::V1::ExplanationSpec-
(::Google::Cloud::AIPlatform::V1::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. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
#explanation_spec=
def explanation_spec=(value) -> ::Google::Cloud::AIPlatform::V1::ExplanationSpec-
value (::Google::Cloud::AIPlatform::V1::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. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
-
(::Google::Cloud::AIPlatform::V1::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. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
#id
def id() -> ::String-
(::String) — Immutable. The ID of the DeployedModel. If not provided upon deployment,
Vertex AI will generate a value for this ID.
This value should be 1-10 characters, and valid characters are /[0-9]/.
#id=
def id=(value) -> ::String-
value (::String) — Immutable. The ID of the DeployedModel. If not provided upon deployment,
Vertex AI will generate a value for this ID.
This value should be 1-10 characters, and valid characters are /[0-9]/.
-
(::String) — Immutable. The ID of the DeployedModel. If not provided upon deployment,
Vertex AI will generate a value for this ID.
This value should be 1-10 characters, and valid characters are /[0-9]/.
#model
def model() -> ::String-
(::String) — Required. The resource name of the Model that this is the deployment of.
Note that the Model may be in a different location than the DeployedModel's
Endpoint.
The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be deployed.
#model=
def model=(value) -> ::String-
value (::String) — Required. The resource name of the Model that this is the deployment of.
Note that the Model may be in a different location than the DeployedModel's
Endpoint.
The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be deployed.
-
(::String) — Required. The resource name of the Model that this is the deployment of.
Note that the Model may be in a different location than the DeployedModel's
Endpoint.
The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be deployed.
#model_version_id
def model_version_id() -> ::String- (::String) — Output only. The version ID of the model that is deployed.
#private_endpoints
def private_endpoints() -> ::Google::Cloud::AIPlatform::V1::PrivateEndpoints- (::Google::Cloud::AIPlatform::V1::PrivateEndpoints) — Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.
#service_account
def service_account() -> ::String-
(::String) — 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.actAspermission on this service account.
#service_account=
def service_account=(value) -> ::String-
value (::String) — 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.actAspermission on this service account.
-
(::String) — 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.actAspermission on this service account.