inst/doc/train-and-deploy-first-model.R

In azuremlsdk: Interface to the 'Azure Machine Learning' 'SDK'

## ----eval=FALSE---------------------------------------------------------------
#  library(azuremlsdk)

## ----load_workpace, eval=FALSE------------------------------------------------
#  ws <- load_workspace_from_config()

## ----create_experiment, eval=FALSE--------------------------------------------
#  experiment_name <- "accident-logreg"
#  exp <- experiment(ws, experiment_name)

## ----create_cluster, eval=FALSE-----------------------------------------------
#  cluster_name <- "rcluster"
#  compute_target <- get_compute(ws, cluster_name = cluster_name)
#  if (is.null(compute_target)) {
#    vm_size <- "STANDARD_D2_V2"
#    compute_target <- create_aml_compute(workspace = ws,
#                                         cluster_name = cluster_name,
#                                         vm_size = vm_size,
#                                         min_nodes = 1,
#                                         max_nodes = 2)
#  
#    wait_for_provisioning_completion(compute_target, show_output = TRUE)
#  }

## ----load_data, eval=FALSE----------------------------------------------------
#  nassCDS <- read.csv("train-and-deploy-first-model/nassCDS.csv",
#                       colClasses=c("factor","numeric","factor",
#                                    "factor","factor","numeric",
#                                    "factor","numeric","numeric",
#                                    "numeric","character","character",
#                                    "numeric","numeric","character"))
#  
#  accidents <- na.omit(nassCDS[,c("dead","dvcat","seatbelt","frontal","sex","ageOFocc","yearVeh","airbag","occRole")])
#  accidents$frontal <- factor(accidents$frontal, labels=c("notfrontal","frontal"))
#  accidents$occRole <- factor(accidents$occRole)
#  accidents$dvcat <- ordered(accidents$dvcat,
#                            levels=c("1-9km/h","10-24","25-39","40-54","55+"))
#  
#  saveRDS(accidents, file="accidents.Rd")

## ----upload_data, eval=FALSE--------------------------------------------------
#  ds <- get_default_datastore(ws)
#  
#  target_path <- "accidentdata"
#  upload_files_to_datastore(ds,
#                            list("./accidents.Rd"),
#                            target_path = target_path,
#                            overwrite = TRUE)

## ----create_estimator, eval=FALSE---------------------------------------------
#  est <- estimator(source_directory = "train-and-deploy-first-model",
#                   entry_script = "accidents.R",
#                   script_params = list("--data_folder" = ds$path(target_path)),
#                   compute_target = compute_target
#                   )

## ----submit_job, eval=FALSE---------------------------------------------------
#  run <- submit_experiment(exp, est)

## ----view_run, eval=FALSE-----------------------------------------------------
#  plot_run_details(run)

## ----wait_run, eval=FALSE-----------------------------------------------------
#  wait_for_run_completion(run, show_output = TRUE)

## ----metrics, eval=FALSE------------------------------------------------------
#  metrics <- get_run_metrics(run)
#  metrics

## ----retrieve_model, eval=FALSE-----------------------------------------------
#  download_files_from_run(run, prefix="outputs/")
#  accident_model <- readRDS("outputs/model.rds")
#  summary(accident_model)

## ----manual_predict, eval=FALSE-----------------------------------------------
#  newdata <- data.frame( # valid values shown below
#   dvcat="10-24",        # "1-9km/h" "10-24"   "25-39"   "40-54"   "55+"
#   seatbelt="none",      # "none"   "belted"
#   frontal="frontal",    # "notfrontal" "frontal"
#   sex="f",              # "f" "m"
#   ageOFocc=16,          # age in years, 16-97
#   yearVeh=2002,         # year of vehicle, 1955-2003
#   airbag="none",        # "none"   "airbag"
#   occRole="pass"        # "driver" "pass"
#   )
#  
#  ## predicted probability of death for these variables, as a percentage
#  as.numeric(predict(accident_model,newdata, type="response")*100)

## ----register_model, eval=FALSE-----------------------------------------------
#  model <- register_model(ws,
#                          model_path = "outputs/model.rds",
#                          model_name = "accidents_model",
#                          description = "Predict probablity of auto accident")

## ----create_environment, eval=FALSE-------------------------------------------
#  r_env <- r_environment(name = "basic_env")

## ----create_inference_config, eval=FALSE--------------------------------------
#  inference_config <- inference_config(
#    entry_script = "accident_predict.R",
#    source_directory = "train-and-deploy-first-model",
#    environment = r_env)

## ----create_aci_config, eval=FALSE--------------------------------------------
#  aci_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 0.5)

## ----deploy_service, eval=FALSE-----------------------------------------------
#  aci_service <- deploy_model(ws,
#                              'accident-pred',
#                              list(model),
#                              inference_config,
#                              aci_config)
#  
#  wait_for_deployment(aci_service, show_output = TRUE)

## ----test_deployment, eval=FALSE----------------------------------------------
#  library(jsonlite)
#  
#  newdata <- data.frame( # valid values shown below
#   dvcat="10-24",        # "1-9km/h" "10-24"   "25-39"   "40-54"   "55+"
#   seatbelt="none",      # "none"   "belted"
#   frontal="frontal",    # "notfrontal" "frontal"
#   sex="f",              # "f" "m"
#   ageOFocc=22,          # age in years, 16-97
#   yearVeh=2002,         # year of vehicle, 1955-2003
#   airbag="none",        # "none"   "airbag"
#   occRole="pass"        # "driver" "pass"
#   )
#  
#  prob <- invoke_webservice(aci_service, toJSON(newdata))
#  prob

## ----delete_service, eval=FALSE-----------------------------------------------
#  delete_webservice(aci_service)

## ----delete_model, eval=FALSE-------------------------------------------------
#  delete_model(model)

## ----delete_compute, eval=FALSE-----------------------------------------------
#  delete_compute(compute_target)