deploy web service for registered R model in Azure ML

Question

I have an absolute nightmare to use azureml-sdk-for-r. So I try to achieve everything via the UI (https://ml.azure.com/). I trained a model locally like so in R 4.0.5

library(datasets)
library(caret)

data(iris)

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")

I deployed/registered it via the UI, no issue. The "scoring script" I try to use to remove the model dependency is as follows (the only dependency is really jsonlite).

library(jsonlite)

init <- function()
{
  message("model is loaded")
  
  function(data)
  {
    prediction_data <- as.data.frame(fromJSON(data))
    return('{"result": "Hello world"}')
  }
}

I use the following yml file as my conda dependency file for this screen:

name: scoring_environment
channels:
  - defaults
dependencies:
  - r-base=4.0.5
  #- r-essentials=4.0.5
  # whatever other dependencies you have
  - jsonlite=1.7.2 

But get immediately this:

How can I debug what's going on? Is the conda dependency file wrong? As it stand, Azure ML is absolutely useless for me as an R user with locally trained models )-:

PS:

I also try to deploy this locally like so:

library(azuremlsdk)

interactive_auth <- interactive_login_authentication(tenant_id="296bf094-bdb4-488f-8ebd-92b2dd1464c2")

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

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

r_env <- r_environment(name = "r_env")

# Create inference config
inference_config <- inference_config(
  entry_script = "score1.R",
  source_directory = ".",
  environment = r_env)

local_deployment_config <- local_webservice_deployment_config()

service <- deploy_model(ws, 
                        'rservice-local', 
                        list(model), 
                        inference_config, 
                        local_deployment_config)
# Wait for deployment
wait_for_deployment(service, show_output = TRUE)

# Show the port of local service
message(service$port)

It downloads registred model to my local machine. So this bit works but then there is this error:

/azureml-envs/azureml_da3e97fcb51801118b8e80207f3e01ad/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning:  cannot open file '/var/azureml-app/iris/score1.R': No such file or directory

So I tried to deliberately created a relative folder:

/var/azureml-app/iris/

where the above script lives and place score1.r (see above) there. Still same error. I am lost!