library(tidymodels)
library(vetiver)
library(pins)
car_mod <-
workflow(mpg ~ ., decision_tree(mode = "regression")) %>%
fit(mtcars)
v <- vetiver_model(car_mod, "cars_mpg")
model_board <- board_folder("pins-r", versioned = TRUE)
model_board %>% vetiver_pin_write(v)from vetiver.data import mtcars
from vetiver import VetiverModel, vetiver_pin_write
from sklearn import tree
from pins import board_folder
car_mod = tree.DecisionTreeRegressor().fit(mtcars.drop(columns="mpg"), mtcars["mpg"])
v = VetiverModel(car_mod, model_name = "cars_mpg",
prototype_data = mtcars.drop(columns="mpg"))
model_board = board_folder("pins-py", allow_pickle_read=True)
vetiver_pin_write(model_board, v)You can deploy your model by creating a special Plumber router in R or a FastAPI router in Python, and adding a POST endpoint for making predictions.
library(plumber)
pr() %>%
vetiver_api(v)# Plumber router with 2 endpoints, 4 filters, and 1 sub-router.
# Use `pr_run()` on this object to start the API.
├──[queryString]
├──[body]
├──[cookieParser]
├──[sharedSecret]
├──/logo
│ │ # Plumber static router serving from directory: /home/runner/work/_temp/Library/vetiver
├──/ping (GET)
└──/predict (POST)To start a server using this object, pipe (%>%) to pr_run(port = 8080) or your port of choice.
from vetiver import VetiverAPI
app = VetiverAPI(v, check_prototype=True)To start a server using this object, use app.run(port = 8080) or your port of choice.
You can interact with your vetiver API via automatically generated, detailed visual documentation.
FastAPI and Plumber APIs such as these can be hosted in a variety of ways. Let’s walk through two options: deploying to Posit Connect or with Docker.
For Posit Connect, you can deploy your versioned model with a single function.
# authenticates via environment variables:
vetiver_deploy_rsconnect(model_board, "user.name/cars_mpg")from rsconnect.api import RSConnectServer
connect_server = RSConnectServer(
url=server_url, # load from an .env file
api_key=api_key # load from an .env file
)
vetiver.deploy_rsconnect(
connect_server = connect_server,
board = model_board,
pin_name = "user.name/cars_mpg",
)In this case, you probably want model_board to be a Connect pins board (board_connect()). For more on deploying to Connect, see the Connect documentation for using vetiver.
For deploying a vetiver API to infrastructure other than Posit Connect, such as Google Cloud Run, AWS, or Azure, you likely will want to build a Docker container.
You can use any pins board with Docker, like board_folder() or board_connect(), as long as your Docker container can authenticate to your pins board.
vetiver_prepare_docker(model_board, "cars_mpg")# Generated by the vetiver package; edit with care
FROM rocker/r-ver:4.2.2
ENV RENV_CONFIG_REPOS_OVERRIDE https://packagemanager.rstudio.com/cran/latest
RUN apt-get update -qq && apt-get install -y --no-install-recommends \
libcurl4-openssl-dev \
libicu-dev \
libsodium-dev \
libssl-dev \
make \
zlib1g-dev \
&& apt-get clean
COPY vetiver_renv.lock renv.lock
RUN Rscript -e "install.packages('renv')"
RUN Rscript -e "renv::restore()"
COPY plumber.R /opt/ml/plumber.R
EXPOSE 8000
ENTRYPOINT ["R", "-e", "pr <- plumber::plumb('/opt/ml/plumber.R'); pr$run(host = '0.0.0.0', port = 8000)"]When you run vetiver_prepare_docker(), you generate three files needed to build a Docker image: the Dockerfile itself, a Plumber file serving your REST API, and the vetiver_renv.lock file to capture your model dependencies.
vetiver.prepare_docker(model_board, "cars_mpg")# # Generated by the vetiver package; edit with care
# start with python base image
FROM python:3.8
# create directory in container for vetiver files
WORKDIR /vetiver
# copy and install requirements
COPY vetiver_requirements.txt /vetiver/requirements.txt
#
RUN pip install --no-cache-dir --upgrade -r /vetiver/requirements.txt
# copy app file
COPY app.py /vetiver/app/app.py
# expose port
EXPOSE 8080
# run vetiver API
CMD ["uvicorn", "app.app:api", "--host", "0.0.0.0", "--port", "8080"]When you run vetiver.prepare_docker(), you generate three files needed to build a Docker image: the Dockerfile itself, a FastAPI app file serving your REST API, and a requirements.txt file to capture your model dependencies.
When you build such a Docker container with docker build, all the packages needed to make a prediction with your model are installed into the container.
When you run the Docker container, you can pass in environment variables (for authentication to your pins board, for example) with docker run --env-file .Renviron.
Learn more about deploying with Docker.
A model deployed via vetiver can be treated as a special vetiver_endpoint() object.
endpoint <- vetiver_endpoint("http://127.0.0.1:8080/predict")
endpoint
── A model API endpoint for prediction:
http://127.0.0.1:8080/predictfrom vetiver.server import predict, vetiver_endpoint
endpoint = vetiver_endpoint("http://127.0.0.1:8080/predict")
endpoint'http://127.0.0.1:8080/predict'If such a deployed model endpoint is running via one process (either remotely on a server or locally, perhaps via Docker or a background job in the RStudio IDE), you can make predictions with that deployed model and new data in another, separate process1.
new_car <- tibble(cyl = 4, disp = 200,
hp = 100, drat = 3,
wt = 3, qsec = 17,
vs = 0, am = 1,
gear = 4, carb = 2)
predict(endpoint, new_car)# A tibble: 11 × 1
.pred
<chr>
1 22.3 import pandas as pd
new_car_dict = {"cyl": [4], "disp": [200],
"hp": [100], "drat": [3],
"wt": [3], "qsec": [17],
"vs": [0], "am": [1],
"gear": [4], "carb": [2]}
new_car = pd.DataFrame(new_car_dict)
predict(endpoint, new_car) prediction
0 21.0Being able to predict with a vetiver model endpoint takes advantage of the model’s input data prototype and other metadata that is stored with the model.
Keep in mind that the R and Python models have different values for the decision tree hyperparameters.↩︎