Resource: Endpoint
Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.
name
string
Output only. The resource name of the Endpoint.
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.
description
string
The description of the Endpoint.
deployedModels[]
object ( DeployedModel
)
Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel
and EndpointService.UndeployModel
respectively.
trafficSplit
map (key: string, value: integer)
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.
etag
string
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels
map (key: string, value: string)
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.
createTime
string ( Timestamp
format)
Output only. timestamp when this Endpoint was created.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z"
and "2014-10-02T15:01:23.045123456Z"
.
updateTime
string ( Timestamp
format)
Output only. timestamp when this Endpoint was last updated.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z"
and "2014-10-02T15:01:23.045123456Z"
.
encryptionSpec
object ( EncryptionSpec
)
Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.
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 enablePrivateServiceConnect
, can be set.
Format
: projects/{project}/global/networks/{network}
. Where {project}
is a project number, as in 12345
, and {network}
is network name.
enablePrivateServiceConnect
(deprecated)
boolean
Deprecated: If true, expose the Endpoint via private service connect.
Only one of the fields, network
or enablePrivateServiceConnect
, can be set.
privateServiceConnectConfig
object ( PrivateServiceConnectConfig
)
Optional. Configuration for private service connect.
network
and privateServiceConnectConfig
are mutually exclusive.
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/{modelDeploymentMonitoringJob}
predictRequestResponseLoggingConfig
object ( PredictRequestResponseLoggingConfig
)
Configures the request-response logging for online prediction.
dedicatedEndpointEnabled
boolean
If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.
dedicatedEndpointDns
string
Output only. DNS of the dedicated endpoint. Will only be populated if dedicatedEndpointEnabled is true. Format: https://{endpointId}.{region}-{projectNumber}.prediction.vertexai.goog
.
satisfiesPzs
boolean
Output only. reserved for future use.
satisfiesPzi
boolean
Output only. reserved for future use.
JSON representation |
---|
{ "name" : string , "displayName" : string , "description" : string , "deployedModels" : [ { object ( |
DeployedModel
A deployment of a Model. endpoints contain one or more DeployedModels.
id
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
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. Example: projects/{project}/locations/{location}/models/{model}@2
or projects/{project}/locations/{location}/models/{model}@golden
if no version is specified, the default version will be deployed.
modelVersionId
string
Output only. The version id of the model that is deployed.
displayName
string
The display name of the DeployedModel. If not provided upon creation, the Model's displayName is used.
createTime
string ( Timestamp
format)
Output only. timestamp when the DeployedModel was created.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z"
and "2014-10-02T15:01:23.045123456Z"
.
explanationSpec
object ( 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 explanationSpec
are optional in the request. If a field of explanationSpec
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 explanationSpec
will be used for the explanation configuration.
disableExplanations
boolean
If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec
or explanationSpec
.
serviceAccount
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.actAs
permission on this service account.
disableContainerLogging
boolean
For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr
and stdout
streams to Cloud 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.
enableAccessLogging
boolean
If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request.
Note that 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.
privateEndpoints
object ( 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.
prediction_resources
. The prediction (for example, the machine) resources that the DeployedModel uses. The user is billed for the resources (at least their minimal amount) even if the DeployedModel receives no traffic. Not all Models support all resources types. See Model.supported_deployment_resources_types
. Required except for Large Model Deploy use cases. prediction_resources
can be only one of the following: dedicatedResources
object ( DedicatedResources
)
A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
automaticResources
object ( AutomaticResources
)
A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
sharedResources
string
The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deploymentResourcePool}
JSON representation |
---|
{ "id" : string , "model" : string , "modelVersionId" : string , "displayName" : string , "createTime" : string , "explanationSpec" : { object ( |
PrivateEndpoints
PrivateEndpoints proto is used to provide paths for users to send requests privately. To send request via private service access, use predictHttpUri, explainHttpUri or healthHttpUri. To send request via private service connect, use serviceAttachment.
predictHttpUri
string
Output only. Http(s) path to send prediction requests.
explainHttpUri
string
Output only. Http(s) path to send explain requests.
healthHttpUri
string
Output only. Http(s) path to send health check requests.
serviceAttachment
string
Output only. The name of the service attachment resource. Populated if private service connect is enabled.
JSON representation |
---|
{ "predictHttpUri" : string , "explainHttpUri" : string , "healthHttpUri" : string , "serviceAttachment" : string } |
PredictRequestResponseLoggingConfig
Configuration for logging request-response to a BigQuery table.
enabled
boolean
If logging is enabled or not.
samplingRate
number
Percentage of requests to be logged, expressed as a fraction in range(0,1].
bigqueryDestination
object ( BigQueryDestination
)
BigQuery table for logging. If only given a project, a new dataset will be created with name logging_<endpoint-display-name>_<endpoint-id>
where request_response_logging
JSON representation |
---|
{
"enabled"
:
boolean
,
"samplingRate"
:
number
,
"bigqueryDestination"
:
{
object (
|
Methods |
|
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Return a list of tokens based on the input text. |
|
Perform a token counting. |
|
Creates an Endpoint. |
|
Deletes an Endpoint. |
|
Deploys a Model into this Endpoint, creating a DeployedModel within it. |
|
Perform an unary online prediction request to a gRPC model server for Vertex first-party products and frameworks. |
|
Perform an unary online prediction request to a gRPC model server for custom containers. |
|
Perform an online explanation. |
|
Generate content with multimodal inputs. |
|
Gets an Endpoint. |
|
Lists Endpoints in a Location. |
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Updates an existing deployed model. |
|
Updates an Endpoint. |
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Perform an online prediction. |
|
Perform an online prediction with an arbitrary HTTP payload. |
|
Perform a server-side streaming online prediction request for Vertex LLM streaming. |
|
Generate content with multimodal inputs with streaming support. |
|
Perform a streaming online prediction with an arbitrary HTTP payload. |
|
Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it's using. |