My use case is to deploy a locally trained model - perfectly valid use case AFIK. For reproducibility I trained this simple model:

library(datasets)
library(caret)

setwd("C:/Data")

index <- createDataPartition(iris$Species, p=0.80, list=FALSE)
testset <- iris[-index,]
trainset <- iris[index,]

model = train(Species ~ ., 
                  data=trainset, 
                  method="rpart", 
                  trControl = trainControl(method = "cv"))

saveRDS(model, "model.rds")

The model can be deployed using code, which is great:

I try to use this scoring script:

library(jsonlite)

init <- function()
{
  model_path <- Sys.getenv("AZUREML_MODEL_DIR")
  model <- readRDS(file.path(model_path, "model.rds"))
  message("iris classfication model loaded")
  
  function(data)
  {
    vars <- as.data.frame(fromJSON(data))
    prediction <- predict(model, newdata=vars)
    toJSON(prediction)
  }
}

and deploy a web service to score the model (or you call it infer these days):

library(azuremlsdk)

interactive_auth <- interactive_login_authentication(tenant_id="xxx")

ws <- get_workspace(
        name = "amazing_work_space", 
        subscription_id = "xxx", 
        resource_group ="xxx", 
        auth = interactive_auth
)

model <- get_model(ws, name = "iris_classification")

r_env <- r_environment(name = 'myr_env',
                       version = '1')

inference_config <- inference_config(
  entry_script = "score.R",
  source_directory = ".",
  environment = r_env)

aci_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 0.5)

aci_service <- deploy_model(ws, 
                            'xxx', 
                            list(model), 
                            inference_config, 
                            aci_config)

wait_for_deployment(aci_service, show_output = TRUE)

I get:

Running.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................. Failed Service deployment polling reached non-successful terminal state, current service state: Failed Operation ID: 6f62fc59-6b42-4b1e-957b-b0460de9e49e More information can be found using '.get_logs()' Error: { "code": "AciDeploymentFailed", "statusCode": 400, "message": "Aci Deployment failed with exception: Error in entry script, RuntimeError: Error in file(filename, \"r\", encoding = encoding) : , please run print(service.get_logs()) to get details.", "details": [ { "code": "CrashLoopBackOff", "message": "Error in entry script, RuntimeError: Error in file(filename, \"r\", encoding = encoding) : , please run print(service.get_logs()) to get details." } ] }

So there is something wrong with my scoring script? Any help would be very much appreciated please! Thanks.