update samples from Release-38 as a part of SDK release

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/deploy-to-local.R

@@ -0,0 +1,112 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+# Register model and deploy locally
+# This example shows how to deploy a web service in step-by-step fashion:
+#   
+# 1) Register model
+# 2) Deploy the model as a web service in a local Docker container.
+# 3) Invoke web service with SDK or call web service with raw HTTP call.
+# 4) Quickly test changes to your entry script by reloading the local service.
+# 5) Optionally, you can also make changes to model and update the local service.
+
+library(azuremlsdk)
+library(jsonlite)
+
+ws <- load_workspace_from_config()
+
+# Register the model
+model <- register_model(ws, model_path = "project_files/model.rds",
+                        model_name = "model.rds")
+
+# Create environment
+r_env <- r_environment(name = "r_env")
+
+# Create inference config
+inference_config <- inference_config(
+  entry_script = "score.R",
+  source_directory = "project_files",
+  environment = r_env)
+
+# Create local deployment config
+local_deployment_config <- local_webservice_deployment_config()
+
+# Deploy the web service
+# NOTE:
+# The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:
+# # PowerShell command to switch to Linux engine
+# & 'C:\Program Files\Docker\Docker\DockerCli.exe' -SwitchLinuxEngine
+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$port)
+
+# If you encounter any issue in deploying the webservice, please visit
+# https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment
+
+# Inferencing
+# versicolor
+# plant <- data.frame(Sepal.Length = 6.4,
+#                     Sepal.Width = 2.8,
+#                     Petal.Length = 4.6,
+#                     Petal.Width = 1.8)
+# setosa
+plant <- data.frame(Sepal.Length = 5.1,
+                    Sepal.Width = 3.5,
+                    Petal.Length = 1.4,
+                    Petal.Width = 0.2)
+# # virginica
+# plant <- data.frame(Sepal.Length = 6.7,
+#                     Sepal.Width = 3.3,
+#                     Petal.Length = 5.2,
+#                     Petal.Width = 2.3)
+
+#Test the web service
+invoke_webservice(service, toJSON(plant))
+
+## The last few lines of the logs should have the correct prediction and should display -> R[write to console]: "setosa" 
+cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
+
+## Test the web service with a HTTP Raw request
+# 
+# NOTE:
+# To test the service locally use the https://localhost:<local_service$port> URL
+
+# Import the request library
+library(httr)
+# Get the service scoring URL from the service object, its URL is for testing locally
+local_service_url <- service$scoring_uri #Same as https://localhost:<local_service$port>
+
+#POST request to web service
+resp <- POST(local_service_url, body = plant, encode = "json", verbose())
+
+## The last few lines of the logs should have the correct prediction and should display -> R[write to console]: "setosa" 
+cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
+
+
+# Optional, use a new scoring script
+inference_config <- inference_config(
+  entry_script = "score_new.R",
+  source_directory = "project_files",
+  environment = r_env)
+
+## Then reload the service to see the changes made
+reload_local_webservice_assets(service)
+
+## Check reloaded service, you will see the last line will say "this is a new scoring script! I was reloaded"
+invoke_webservice(service, toJSON(plant))
+cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
+
+# Update service
+# If you want to change your model(s), environment, or deployment configuration, call update() to rebuild the Docker image.
+
+# update_local_webservice(service, models = [NewModelObject], deployment_config = deployment_config, wait = FALSE, inference_config = inference_config)
+
+# Delete service
+delete_local_webservice(service)

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/project_files/score.R

@@ -0,0 +1,18 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+library(jsonlite)
+
+init <- function() {
+    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
+    model <- readRDS(file.path(model_path, "model.rds"))
+    message("model is loaded")
+
+    function(data) {
+        plant <- as.data.frame(fromJSON(data))
+        prediction <- predict(model, plant)
+        result <- as.character(prediction)
+        message(result)
+        toJSON(result)
+    }
+}

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/project_files/score_new.R

@@ -0,0 +1,19 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+library(jsonlite)
+
+init <- function() {
+    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
+    model <- readRDS(file.path(model_path, "model.rds"))
+    message("model is loaded")
+
+    function(data) {
+        plant <- as.data.frame(fromJSON(data))
+        prediction <- predict(model, plant)
+        result <- as.character(prediction)
+        message(result)
+        message("this is a new scoring script! I was reloaded")
+        toJSON(result)
+    }
+}