In justinjm/googleCloudVertexAIR: Google Cloud Vertex AI API Into R

knitr::opts_chunk$set(echo = TRUE)

library(withr) # for gcloud sdk: https://stackoverflow.com/a/64381848/1812363 
library(glue) 

sh <- function(cmd, args = c(), intern = FALSE) {
  with_path(path.expand("~/google-cloud-sdk/bin/"), {
    if (is.null(args)) {
      cmd <- glue(cmd)
      s <- strsplit(cmd, " ")[[1]]
      cmd <- s[1]
      args <- s[2:length(s)]
    }
    ret <- system2(cmd, args, stdout = TRUE, stderr = TRUE)
    if ("errmsg" %in% attributes(attributes(ret))$names) cat(attr(ret, "errmsg"), "\n")
    if (intern) return(ret) else cat(paste(ret, collapse = "\n"))
  }
  )
}

library(googleAuthR)
library(googleCloudVertexAIR)

options(googleAuthR.scopes.selected = "https://www.googleapis.com/auth/cloud-platform")
# options(googleAuthR.verbose = 0) # set when debugging

gar_auth_service(json_file = Sys.getenv("GAR_SERVICE_JSON"))

Set global arguements

projectId <- Sys.getenv("GCVA_DEFAULT_PROJECT_ID")
gcva_region_set(region = "us-central1")
gcva_project_set(projectId = projectId)

Create Endpoint

Doc: Overview of getting predictions on Vertex AI | Google Cloud API: https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.endpoints/create

endpoint <- gcva_create_endpoint(
    displayName = "CaliforniaHousingEndpoint"
)
endpoint

List Endpoints

endpoints <- gcva_list_endpoints()
endpoints

Deploy Model to Endpoint

[DEV]

# manually set model object (to fasttrack for testing)
model <- gcva_model(model = Sys.getenv("GCVA_TEST_MODEL_NAME_CUSTOM"))
model

Doc: https://cloud.google.com/vertex-ai/docs/predictions/get-predictions#api API: https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.endpoints/deployModel

model_deployed <- gcva_deploy(
  model = model,
  endpoint = endpoint,
  machineType = "n1-standard-4"
)
model_deployed

2023-03-18 20:45:18> Deploying model to endpoint...
2023-03-18 20:45:28> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:45:38> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:45:48> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:45:58> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:46:08> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:46:19> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:46:29> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:46:39> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:46:49> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:46:59> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:47:09> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:47:19> Model deployment in progress. Waiting 10 seconds before checking operation status.
2023-03-18 20:47:20> Model deployment completed successfully.
$name
[1] "projects/442003009360/locations/us-central1/endpoints/1203107613345054720/operations/4971509788551675904"

$metadata
$metadata$`@type`
[1] "type.googleapis.com/google.cloud.aiplatform.v1.DeployModelOperationMetadata"

$metadata$genericMetadata
$metadata$genericMetadata$createTime
[1] "2023-03-19T00:45:18.085624Z"

$metadata$genericMetadata$updateTime
[1] "2023-03-19T00:47:13.271833Z"



$done
[1] TRUE

$response
$response$`@type`
[1] "type.googleapis.com/google.cloud.aiplatform.v1.DeployModelResponse"

$response$deployedModel
$response$deployedModel$id
[1] "9074185901151617024"



attr(,"class")
[1] "gcva_endpoint"

Predict

Build sample request

data_uri <- "gs://cloud-samples-data/ai-platform-unified/datasets/tabular/california-housing-tabular-regression.csv"

## create example data for prediction request 
data_raw <- read.csv(text=sh("gsutil cat {data_uri}", intern = TRUE))

## convert all to string values to build correct prediction request
data <- as.data.frame(lapply(data_raw, as.character))

## build list of 5 examples/rows only for testing
instances <- list(instances=head(data[, names(data) != "median_house_value"], 5))

## convert to required format of JSON
library(jsonlite)
instances_json <- toJSON(instances, auto_unbox = TRUE)

instances_json

gcva_predict(
  endpoint = endpoint,
  instances = instances
)

library(glue)
## set parameters for prediction request here so easier to understand 
url <- glue(
  "https://{gcva_region_get()}-aiplatform.googleapis.com/v1/{endpoint$name}:predict"
)
access_token <- sh("gcloud auth print-access-token", intern = TRUE)

## make prediction request 
sh(
  "curl",
  c(
    "--tr-encoding",
    "-s",
    "-X POST",
    glue("-H 'Authorization: Bearer {access_token}'"),
    "-H 'Content-Type: application/json'",
    url,
    glue("-d '{instances_json}'")
  )
)

Undeploy

Single

TODO

gcva_undeploy(endpoint = endpoint)

All

TODO

gcva_endpoint_undeploy_all(endpoint = endpoint)

Delete Endpoint

gcva_delete_endpoint(endpoint = endpoint)

List Endpoints (again)

to confirm deleted

endpoints <- gcva_list_endpoints()
endpoints

justinjm/googleCloudVertexAIR documentation built on April 17, 2025, 5:04 p.m.