How to deploy a predictive service to Kubernetes with R and the AzureContainers package

December 12, 2018
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It's easy to create a function in R, but what if you want to call that function from a different application, with the scale to support a large number of simultaneous requests? This article shows how you can deploy an R fitted model as a Plumber web service in Kubernetes, using Azure Container Registry (ACR) and Azure Kubernetes Service (AKS). We use the AzureContainers package to create the necessary resources and deploy the service.

Fit the model

We’ll fit a simple model for illustrative purposes, using the Boston housing dataset (which ships with R in the MASS package). To make the deployment process more interesting, the model we fit will be a random forest, using the randomForest package. This is not part of R, so we’ll have to install it from CRAN.

data(Boston, package="MASS")
install.packages("randomForest")
library(randomForest)

# train a model for median house price
bos_rf <- randomForest(medv ~ ., data=Boston, ntree=100)

# save the model
saveRDS(bos.rf, "bos_rf.rds")

Scoring script for plumber

Now that we have the model, we also need a script to obtain predicted values from it given a set of inputs:

# save as bos_rf_score.R

bos_rf <- readRDS("bos_rf.rds")
library(randomForest)

#* @param df data frame of variables
#* @post /score
function(req, df)
{
    df <- as.data.frame(df)
    predict(bos_rf, df)
}

This is fairly straightforward, but the comments may require some explanation. They are plumber annotations that tell it to call the function if the server receives a HTTP POST request with the path /score, and query parameter df. The value of the df parameter is then converted to a data frame, and passed to the randomForest predict method. For a fuller description of how Plumber works, see the Plumber website.

Create a Dockerfile

Let’s package up the model and the scoring script into a Docker image. A Dockerfile to do this is shown below. This uses the base image supplied by Plumber (trestletech/plumber), installs randomForest, and then adds the model and the above scoring script. Finally, it runs the code that will start the server and listen on port 8000.

# example Dockerfile to expose a plumber service

FROM trestletech/plumber

# install the randomForest package
RUN R -e 'install.packages(c("randomForest"))'

# copy model and scoring script
RUN mkdir /data
COPY bos_rf.rds /data
COPY bos_rf_score.R /data
WORKDIR /data

# plumb and run server
EXPOSE 8000
ENTRYPOINT ["R", "-e", \
    "pr <- plumber::plumb('/data/bos_rf_score.R'); \
    pr$run(host='0.0.0.0', port=8000)"]

Build and upload the image

The code to store our image on Azure Container Registry is as follows. This calls AzureRMR to login to Resource Manager, creates an Azure Container Registry resource (a Docker registry hosted in Azure), and then pushes the image to the registry.

If this is the first time you are using AzureRMR, you’ll have to create a service principal first. For more information on how to do this, see the AzureRMR readme.

library(AzureContainers)

az <- AzureRMR::az_rm$new(
    tenant="myaadtenant.onmicrosoft.com",
    app="app_id",
    password="password")

# create a resource group for our deployments
deployresgrp <- az$
    get_subscription("subscription_id")$
    create_resource_group("deployresgrp", location="australiaeast")

# create container registry
deployreg_svc <- deployresgrp$create_acr("deployreg")

# build image 'bos_rf'
call_docker("build -t bos_rf .")

# upload the image to Azure
deployreg <- deployreg_svc$get_docker_registry()
deployreg$push("bos_rf")

If you run this code, you should see a lot of output indicating that R is downloading, compiling and installing randomForest, and finally that the image is being pushed to Azure. (You will see this output even if your machine already has the randomForest package installed. This is because the package is being installed to the R session inside the container, which is distinct from the one running the code shown here.)

All docker calls in AzureContainers, like the one to build the image, return the actual docker commandline as the cmdline attribute of the (invisible) returned value. In this case, the commandline is docker build -t bos_rf . Similarly, the push() method actually involves two Docker calls, one to retag the image, and the second to do the actual pushing; the returned value in this case will be a 2-component list with the command lines being docker tag bos_rf deployreg.azurecr.io/bos_rf and docker push deployreg.azurecr.io/bos_rf.

Deploy to a Kubernetes cluster

The code to create an AKS resource (a managed Kubernetes cluster in Azure) is quite simple:

# create a Kubernetes cluster with 2 nodes, running Linux
deployclus_svc <- deployresgrp$create_aks("deployclus",
    agent_pools=aks_pools("pool1", 2))

Creating a Kubernetes cluster can take several minutes. By default, the create_aks() method will wait until the cluster provisioning is complete before it returns.

Having created the cluster, we can deploy our model and create a service. We’ll use a YAML configuration file to specify the details for the deployment and service API.

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: bos-rf
spec:
  replicas: 1
  template:
    metadata:
      labels:
        app: bos-rf
    spec:
      containers:
      - name: bos-rf
        image: deployreg.azurecr.io/bos_rf
        ports:
        - containerPort: 8000
        resources:
          requests:
            cpu: 250m
          limits:
            cpu: 500m
      imagePullSecrets:
      - name: deployreg.azurecr.io
---
apiVersion: v1
kind: Service
metadata:
  name: bos-rf-svc
spec:
  selector:
    app: bos-rf
  type: LoadBalancer
  ports:
  - protocol: TCP
    port: 8000

The following code will obtain the cluster endpoint from the AKS resource and then deploy the image and service to the cluster. The configuration details for the deployclus cluster are stored in a file located in the R temporary directory; all of the cluster’s methods will use this file. Unless told otherwise, AzureContainers does not touch your default Kubernetes configuration (~/kube/config).

# get the cluster endpoint
deployclus <- deployclus_svc$get_cluster()

# pass registry authentication details to the cluster
deployclus$create_registry_secret(deployreg,
    email="[email protected]")

# create and start the service
deployclus$create("bos_rf.yaml")

To check on the progress of the deployment, run the get() methods specifying the type and name of the resource to get information on. As with Docker, these correspond to calls to the kubectl commandline tool, and again, the actual commandline is stored as the cmdline attribute of the returned value.

deployclus$get("deployment bos-rf")
#> Kubernetes operation: get deployment bos-rf  --kubeconfig=".../kubeconfigxxxx"
#> NAME      DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
#> bos-rf    1         1         1            1           5m

deployclus$get("service bos-rf-svc")
#> Kubernetes operation: get service bos-rf-svc  --kubeconfig=".../kubeconfigxxxx"
#> NAME         TYPE           CLUSTER-IP   EXTERNAL-IP     PORT(S)          AGE
#> bos-rf-svc   LoadBalancer   10.0.8.189   52.187.249.58   8000:32276/TCP   5m 

Once the service is up and running, as indicated by the presence of an external IP in the service details, let’s test it with a HTTP request. The response should look like this.

response <- httr::POST("http://52.187.249.58:8000/score",
    body=list(df=MASS::Boston[1:10,]), encode="json")
httr::content(response, simplifyVector=TRUE)
#> [1] 25.9269 22.0636 34.1876 33.7737 34.8081 27.6394 21.8007 22.3577 16.7812 18.9785

Finally, once we are done, we can tear down the service and deployment:

deployclus$delete("service", "bos-rf-svc")
deployclus$delete("deployment", "bos-rf")

And if required, we can also delete all the resources created here, by simply deleting the resource group (AzureContainers will prompt you for confirmation):

deployresgrp$delete()

See also

An alternative to Plumber is the model operationalisation framework found in Microsoft Machine Learning Server. While it is proprietary software, unlike Plumber which is open source, ML Server provides a number of features not available in the latter. These include model management, so that you can easily access multiple versions of a given model; user authentication, so that only authorised users can access your service; and batch (asynchronous) requests. For more information, see the MMLS documentation.

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