endpoints

Overview

Name	endpoints
Type	Resource
Id	google.aiplatform.endpoints

Fields

Name	Datatype	Description
name	string	Output only. The resource name of the Endpoint.
description	string	The description of the Endpoint.
createTime	string	Output only. Timestamp when this Endpoint was created.
deployedModels	array	Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
displayName	string	Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
enablePrivateServiceConnect	boolean	Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
encryptionSpec	object	Represents a customer-managed encryption key spec that can be applied to a top-level resource.
etag	string	Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	object	The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
modelDeploymentMonitoringJob	string	Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
network	string	Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
predictRequestResponseLoggingConfig	object	Configuration for logging request-response to a BigQuery table.
trafficSplit	object	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 a moment.
updateTime	string	Output only. Timestamp when this Endpoint was last updated.

Methods

Name	Accessible by	Required Params	Description
get	SELECT	endpointsId, locationsId, projectsId	Gets an Endpoint.
list	SELECT	locationsId, projectsId	Lists Endpoints in a Location.
create	INSERT	locationsId, projectsId	Creates an Endpoint.
delete	DELETE	endpointsId, locationsId, projectsId	Deletes an Endpoint.
_list	EXEC	locationsId, projectsId	Lists Endpoints in a Location.
deploy_model	EXEC	endpointsId, locationsId, projectsId	Deploys a Model into this Endpoint, creating a DeployedModel within it.
explain	EXEC	endpointsId, locationsId, projectsId	Perform an online explanation. If deployed_model_id is specified, the corresponding DeployModel must have explanation_spec populated. If deployed_model_id is not specified, all DeployedModels must have explanation_spec populated.
mutate_deployed_model	EXEC	endpointsId, locationsId, projectsId	Updates an existing deployed model. Updatable fields include min_replica_count, max_replica_count, autoscaling_metric_specs, disable_container_logging (v1 only), and enable_container_logging (v1beta1 only).
patch	EXEC	endpointsId, locationsId, projectsId	Updates an Endpoint.
predict	EXEC	endpointsId, locationsId, projectsId	Perform an online prediction.
raw_predict	EXEC	endpointsId, locationsId, projectsId	Perform an online prediction with an arbitrary HTTP payload. The response includes the following HTTP headers: X-Vertex-AI-Endpoint-Id: ID of the Endpoint that served this prediction. X-Vertex-AI-Deployed-Model-Id: ID of the Endpoint's DeployedModel that served this prediction.
server_streaming_predict	EXEC	endpointsId, locationsId, projectsId	Perform a server-side streaming online prediction request for Vertex LLM streaming.
undeploy_model	EXEC	endpointsId, locationsId, projectsId	Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it's using.