#' @title
#' Lists Vertex AI Endpoints within a specific GCP Project
#'
#' @param projectId
#' @param locationId
#'
#' @family Endpoints
#'
#' @export
<- (projectId = (),
locationId = ()) {
parent <- ("projects/%s/locations/%s",
projectId,
locationId)
<- ("https://%s-aiplatform.googleapis.com/v1/%s/endpoints",
locationId,
parent)
parse_ld <- (x) {
x <- x$endpoints
x$createTime <- timestamp_to_r(x$createTime)
x
}
f <- googleAuthR::gar_api_generator(,
"GET",
data_parse_function = parse_ld)
response <- f()
out <- response
out
}
#' @title
#' Get an Endpoint
#' @param locationId
#' @param endpointName
#'
#' @return a Endpoint object
#'
#' @family Endpoints
#'
#' @export
<- (locationId = (),
endpointName){
<- ("https://%s-aiplatform.googleapis.com/v1/%s",
locationId,
endpointName)
f <- googleAuthR::gar_api_generator(,
"GET",
data_parse_function = (x) x,
checkTrailingSlash = )
response <- f()
out <- (response, = "gcva_endpoint")
out
}
#' @title
#' Creates a Vertex AI Endpoint
#'
#' @description
#' Creates an Endpoint Resource. Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.
#'
#' @param projectId
#' @param locationId
#' @param name
#' @param displayName STRING Required. Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
#' @param description
#' @param deployedModels
#' @param trafficSplit
#' @param etag
#' @param labels
#' @param createTime
#' @param updateTime
#' @param encryptionSpecs
#' @param network
#' @param enablePrivateServiceConnect
#' @param modelDeploymentMonitoringJob
#' @param predictRequestResponseLoggingConfig
#' @return Endpoint object
#' https://cloud.google.com/vertex-ai/docs/reference/rest/Shared.Types/ListOperationsResponse#Operation
#'
#' @family Endpoints
#'
#' @export
<- (projectId = (),
locationId = (),
=,
displayName=,
description=,
deployedModels=,
trafficSplit=,
etag=,
=,
createTime=,
updateTime=,
encryptionSpec=,
network=,
enablePrivateServiceConnect=,
modelDeploymentMonitoringJob=,
predictRequestResponseLoggingConfig=){
# build request body
request_body <- (
(
(
= ,
displayName = displayName,
description = description,
deployedModels = deployedModels,
trafficSplit = trafficSplit,
etag = etag,
= ,
createTime = createTime,
updateTime = updateTime,
encryptionSpec = encryptionSpec,
network = network,
enablePrivateServiceConnect = enablePrivateServiceConnect,
modelDeploymentMonitoringJob = modelDeploymentMonitoringJob,
predictRequestResponseLoggingConfig = predictRequestResponseLoggingConfig
)
), = ("gcva_endpoint", "list")
)
# parent for API request
parent <- ("projects/%s/locations/%s",
projectId,
locationId)
<- ("https://%s-aiplatform.googleapis.com/v1/%s/endpoints",
locationId,
parent)
f <- googleAuthR::gar_api_generator(,
"POST",
data_parse_function = (x) x,
checkTrailingSlash = )
# projects.locations.endpoints.create
response <- f(the_body = request_body)
response2 <- (operation = response$)
out <- (endpointName = response2$response$)
out
}
#' @title
#' Deploy a model to an Endpoint
#'
#' @param projectId
#' @param locationId
#' @param model Model object
#' @param endpoint Endpoint object
#' @param machineType STRING
#'
#' @return Endpoint object
#'
#' @family Endpoints
#'
#' @export
<- (projectId = (),
locationId = (),
model=,
modelVersionId=,
endpoint=,
enableAccessLogging=,
trafficSplit=,
machineType="n1-standard-4",
minReplicaCount=1,
maxReplicaCount=) {
## checks
### check if model object
((model, "gcva_model"))
### check if endpoint object
((endpoint, "gcva_endpoint"))
# endpoint name for API request
<- endpoint$
# model name for request body
model <- model$
# request body
request_body <- (
(
(deployedModel = (
model = model,
modelVersionId = modelVersionId,
enableAccessLogging = enableAccessLogging,
dedicatedResources = (
machineSpec = (
machineType = machineType
),
minReplicaCount = minReplicaCount,
maxReplicaCount = maxReplicaCount
)
),
trafficSplit = trafficSplit
)
)
)
# projects.locations.endpoints.deployModel
<- ("https://%s-aiplatform.googleapis.com/v1/%s:deployModel",
locationId,
)
f <- googleAuthR::gar_api_generator(,
"POST",
data_parse_function = (x) x,
checkTrailingSlash = )
response <- f(the_body = request_body)
response <- (operation = response$)
out <- (endpointName = response$)
out
}
#' @title
#' Wait for an Model to deploy
#'
#' @param locationId locationId of operation
#' @param operation A operation object
#' @param wait the number of seconds to wait between checks
#' Use this function to do a loop to check progress of a operation running
#'
#' @return After a while, a completed model deployment operation
#'
#' @family Endpoints
#'
#' @export
<- (locationId = (),
operation,
wait=10) {
status <-
<- ()
myMessage("Deploying model to endpoint...", level = 3)
(!status){
(wait)
myMessage("Model deployment in progress. Waiting ", wait,
" seconds before checking operation status.", level = 3)
operation_response <- (locationId, operationId = operation)
("done" %in% (operation_response) && operation_response"done"]] == ){
status <-
myMessage("Model deployment completed successfully.", level = 3)
} {
status <-
}
}
operation_response
}
#' @title
#' Predict
#'
#' @param locationId locationId of operation
#' @param endpoint an `Endpoint` resource name "projects/{project}/locations/{location}/endpoints/{endpoint}"
#' @param instances a json object for online prediction request
#'
#' @family Endpoints
#'
#' @export
<- (locationId = (),
endpoint,
instances){
request_body <- (
(instances = instances$instances)
)
# resource name for API request
<- endpoint$
<- ("https://%s-aiplatform.googleapis.com/v1/%s:predict",
locationId,
)
f <- googleAuthR::gar_api_generator(,
"POST",
data_parse_function = (x) x,
checkTrailingSlash = )
response <- f(the_body = request_body)
out <- response
out
}
#' @title
#' Undeploy Model from Endpoint
#'
#' @param projectId
#' @param locationId
#' @param endpoint string Required. Endpoint object `gcva_endpoint` or
#' The name of the Endpoint resource from which to undeploy a Model.
#' Format: projects/{project}/locations/{location}/endpoints/{endpoint}
#'
#' @family Endpoints
#'
#' @export
<- (projectId = (),
locationId = (),
endpoint
){
((endpoint, "gcva_endpoint")){
# if passing trainingPipeine objectm get from object
<- endpoint$
}
# "projects/442003009360/locations/us-central1/endpoints/1203107613345054720"
<- ("https://%s-aiplatform.googleapis.com/v1/%s:undeployModel",
locationId,
)
# POST https://{service-endpoint}/v1/{endpoint}:undeployModel
f <- googleAuthR::gar_api_generator(,
"POST",
data_parse_function = (x) x,
checkTrailingSlash = )
# response <- f(the_body = request_body)
}
#'
#'
#'
# gcva_endpoint_undeploy_all <- function(){
#
# }
#' @title
#' Deletes a Vertex AI Endpoint
#'
#' @family Endpoints
#'
#' @export
<- (projectId = (),
locationId = (),
displayName = ,
endpoint) {
# if not using display name, use endpoint object to delete
((displayName)) {
<- endpoint$
} {
endpoints_list <- (projectId = projectId,
locationId = locationId)
endpoint_display_name <- displayName
<- (endpoints_list,
displayName == endpoint_display_name,
select = ())
if (()[1] == 0) {
(("endpoint %s does not exist. Please check the endpoint displayname is correct and try again.",
displayName))
}
}
<- ("https://%s-aiplatform.googleapis.com/v1/%s",
locationId,
)
f <- googleAuthR::gar_api_generator(,
"DELETE",
data_parse_function = (x) x)
response <- f()
out <- response
(out$done==) {
myMessage("endpoint successfully deleted.", level = 3)
} {
out
}
}
