Train and deploy a random forest with vetiver + plumber

1-train-deploy.R

# R session 1: model training and deployment ------------------------------

library(tidymodels)
library(vetiver)
library(plumber)

# Not the way you'd actually split data - for demo only
cars_train <- mtcars[1:24,]
cars_test <- mtcars[25:32,]

# Random forest using C++ implementation (ranger>>>RandomForest)
rf_spec <-
  rand_forest(trees=1000) %>%
  set_mode("regression") %>%
  set_engine("ranger")

# Simple workflow
rf_wflow <-
  workflow(mpg~., rf_spec)

# Fit the model
rf_fit <- fit(rf_wflow, cars_train)

# Create a vetiver model object
v <- vetiver_model(rf_fit, "mtcars_mpg")

# Create a plumber API
pr <- pr() %>% vetiver_api(v)

# Run the API server (pick a port number or let plumber pick one for you)
pr_run(pr, port=5678)

2-predict-from-endpoint.R

# R session 2: predict from the model endpoint ----------------------------

library(vetiver)

# Use the same IP/port from above
endpoint <- vetiver_endpoint("http://127.0.0.1:5678/predict")

# same split from above
cars_test <- mtcars[25:32,]

# Predict from the endpoint (returns a 1-col tibble)
predict(endpoint, cars_test)

# Stick this onto the original data, plot, etc.
cbind(cars_test, predict(endpoint, cars_test))

Thank you, this was very helpful, particularly, the second piece, predicting from the endpoint. Best, Tural.