v4.0

Model Image for R

This doc shows how to package a model into a format-valid docker image for the PrimeHub model deployment feature.

The PrimeHub model deployment feature is based on Seldon. This doc takes reference from Seldon official documentations and other resources which are listed in the last part.

Prerequisites

Prepare the Model and Code (R)

  • Create a install.R file and write down all required packages (it could be empty if there was no any required packages).

    install.packages('pls')
install.packages('Rcpp')

  • Create a Dockerfile with the following content.

    FROM rocker/r-apt:bionic

RUN apt-get update && \
    apt-get install -y -qq \
        r-cran-plumber \
        r-cran-jsonlite \
        r-cran-optparse \
        r-cran-stringr \
        r-cran-urltools \
        r-cran-caret \
        curl

ENV MODEL_NAME mnist.R
ENV API_TYPE REST
ENV SERVICE_TYPE MODEL
ENV PERSISTENCE 0

RUN mkdir microservice
COPY . /microservice
WORKDIR /microservice

RUN curl -OL https://raw.githubusercontent.com/SeldonIO/seldon-core/master/incubating/wrappers/s2i/R/microservice.R > /microservice/microservice.R
RUN Rscript install.R

EXPOSE 5000
CMD Rscript microservice.R --model $MODEL_NAME --api $API_TYPE --service $SERVICE_TYPE --persistence $PERSISTENCE

  • Create a mnist.R file with the following example template.

    library(methods)

predict.mnist <- function(mnist,newdata=list()) {
    cn <- 1:784
    for (i in seq_along(cn)){cn[i] <- paste("X",cn[i],sep = "")}
    colnames(newdata) <- cn
    predict(mnist$model, newdata = newdata, type='prob')
}

new_mnist <- function(filename) {
    model <- readRDS(filename)
    structure(list(model=model), class = "mnist")
}

initialise_seldon <- function(params) {
    new_mnist("model.Rds")
}

    • The file name mnist.R should be the same as MODEL_NAME under Dockerfile

    • Provide a function initialise_seldon to return an S3 class

    • Define a generic predict function for mnist class, this will be called with a newdata field with the data.frame to be predicted

    • model.Rds is a trained model file, it will be used while packaging the image.

    • You can also check the keras example

Build the Image

  • Make sure you are in the folder that includes install.R, Dockerfile, R scripts, and model file.

  • Execute following command to install environment and package our model file into the target image my-model-image.

    docker build . -t my-model-image

  • Then check the image by docker images.

    $ docker images | grep my-model-image
my-model-image                      latest              04b42f702072        24 seconds ago      1.1GB

Test the Image

  • In order to make sure your model image is well packaged, you can run your model as a Docker container locally.

    docker run -p 5000:5000 --rm my-model-image

  • And curl (replace ndarray content in curl example according to your application).

    curl -X POST localhost:5000/predict \
    -H 'Content-Type: application/json' \
    -d 'json={"data":{"ndarray":[[0.44,0.162,0.367,0.011,0.231,0.973,0.675,0.597,0.896,0.936,0.997,0.149,0.836,0.17,0.832,0.365,0.902,0.914,0.645,0.678,0.166,0.933,0.386,0.89,0.854,0.617,0.001,0.454,0.602,0.33,0.857,0.134,0.695,0.335,0.519,0.236,0.389,0.665,0.921,0.266,0.936,0.587,0.295,0.7,0.803,0.452,0.902,0.636,0.063,0.358,0.048,0.289,0.821,0.956,0.605,0.511,0.392,0.522,0.289,0.953,0.488,0.371,0.455,0.552,0.789,0.259,0.064,0.06,0.398,0.11,0.675,0.161,0.698,0.618,0.929,0.782,0.042,0.076,0.579,0.985,0.526,0.078,0.384,0.273,0.387,0.374,0.595,0.673,0.421,0.823,0.733,0.734,0.157,0.37,0.394,0.722,0.011,0.042,0.408,0.0,0.76,0.353,0.497,0.215,0.194,0.795,0.3,0.397,0.094,0.818,0.872,0.976,0.959,0.546,0.537,0.478,0.532,0.829,0.074,0.547,0.774,0.782,0.783,0.029,0.89,0.573,0.379,0.712,0.361,0.616,0.42,0.589,0.622,0.167,0.054,0.552,0.804,0.277,0.238,0.661,0.237,0.773,0.282,0.887,0.605,0.921,0.254,0.723,0.589,0.577,0.519,0.91,0.388,0.757,0.546,0.149,0.55,0.818,0.392,0.205,0.422,0.004,0.542,0.847,0.358,0.103,0.566,0.053,0.812,0.481,0.98,0.921,0.995,0.33,0.276,0.221,0.59,0.982,0.088,0.569,0.488,0.315,0.957,0.169,0.093,0.148,0.219,0.486,0.79,0.005,0.833,0.139,0.765,0.545,0.062,0.863,0.027,0.954,0.419,0.315,0.436,0.896,0.838,0.14,0.389,0.474,0.066,0.459,0.737,0.311,0.965,0.57,0.522,0.8,0.442,0.149,0.918,0.305,0.793,0.576,0.058,0.491,0.693,0.029,0.413,0.15,0.365,0.318,0.536,0.083,0.902,0.072,0.3,0.844,0.263,0.815,0.017,0.313,0.293,0.547,0.934,0.913,0.05,0.171,0.889,0.915,0.716,0.636,0.534,0.984,0.309,0.42,0.471,0.701,0.685,0.057,0.519,0.995,0.002,0.748,0.858,0.149,0.1,0.009,0.989,0.856,0.293,0.856,0.183,0.326,0.933,0.671,0.025,0.836,0.492,0.705,0.99,0.684,0.104,0.375,0.736,0.23,0.697,0.8,0.68,0.905,0.4,0.855,0.128,0.592,0.302,0.796,0.977,0.427,0.063,0.533,0.738,0.206,0.477,0.921,0.316,0.719,0.806,0.517,0.131,0.407,0.92,0.142,0.299,0.304,0.077,0.633,0.822,0.537,0.622,0.424,0.542,0.142,0.972,0.939,0.806,0.511,0.731,0.519,0.873,0.682,0.478,0.008,0.977,0.365,0.124,0.755,0.562,0.228,0.515,0.247,0.262,0.178,0.293,0.376,0.584,0.257,0.092,0.46,0.459,0.614,0.369,0.71,0.041,0.212,0.805,0.349,0.845,0.333,0.834,0.661,0.397,0.796,0.223,0.653,0.379,0.781,0.721,0.345,0.233,0.855,0.876,0.466,0.369,0.948,0.115,0.434,0.18,0.169,0.354,0.378,0.798,0.596,0.28,0.492,0.507,0.451,0.967,0.308,0.624,0.344,0.946,0.278,0.197,0.198,0.27,0.334,0.394,0.016,0.957,0.492,0.908,0.236,0.748,0.824,0.273,0.829,0.055,0.44,0.586,0.999,0.022,0.062,0.441,0.799,0.122,0.209,0.666,0.715,0.966,0.138,0.209,0.29,0.752,0.341,0.055,0.54,0.952,0.337,0.003,0.542,0.961,0.308,0.301,0.741,0.713,0.553,0.957,0.11,0.84,0.122,0.2,0.009,0.397,0.684,0.982,0.963,0.7,0.747,0.223,0.683,0.673,0.994,0.41,0.665,0.475,0.025,0.125,0.879,0.806,0.22,0.563,0.998,0.787,0.313,0.008,0.096,0.716,0.57,0.535,0.05,0.826,0.213,0.567,0.276,0.612,0.202,0.485,0.165,0.777,0.473,0.093,0.999,0.977,0.306,0.896,0.517,0.145,0.786,0.344,0.643,0.214,0.866,0.988,0.188,0.691,0.173,0.592,0.984,0.584,0.221,0.525,0.475,0.185,0.846,0.572,0.68,0.987,0.653,0.828,0.781,0.504,0.309,0.321,0.147,0.45,0.331,0.753,0.457,0.966,0.954,0.872,0.84,0.787,0.056,0.65,0.867,0.946,0.852,0.136,0.93,0.168,0.293,0.145,0.108,0.552,0.472,0.841,0.186,0.005,0.685,0.917,0.813,0.781,0.796,0.871,0.446,0.976,0.874,0.016,0.718,0.344,0.092,0.831,0.992,0.976,0.666,0.786,0.727,0.296,0.319,0.067,0.408,0.593,0.368,0.411,0.122,0.127,0.495,0.647,0.528,0.519,0.798,0.354,0.144,0.38,0.571,0.034,0.912,0.386,0.16,0.236,0.821,0.979,0.07,0.732,0.088,0.119,0.199,0.407,0.687,0.903,0.71,0.276,0.579,0.073,0.748,0.07,0.598,0.721,0.06,0.964,0.805,0.483,0.75,0.702,0.609,0.124,0.873,0.64,0.364,0.114,0.345,0.922,0.941,0.753,0.79,0.878,0.014,0.279,0.482,0.784,0.461,0.77,0.581,0.256,0.287,0.04,0.202,0.82,0.021,0.227,0.304,0.281,0.632,0.412,0.788,0.836,0.767,0.232,0.964,0.798,0.278,0.508,0.18,0.311,0.553,0.521,0.866,0.448,0.523,0.867,0.549,0.938,0.988,0.406,0.896,0.16,0.876,0.055,0.816,0.805,0.117,0.253,0.233,0.906,0.512,0.768,0.438,0.891,0.452,0.211,0.664,0.272,0.358,0.929,0.696,0.339,0.823,0.191,0.583,0.033,0.273,0.718,0.714,0.023,0.198,0.842,0.669,0.417,0.798,0.358,0.793,0.726,0.133,0.689,0.911,0.698,0.753,0.972,0.828,0.599,0.668,0.115,0.83,0.766,0.043,0.754,0.827,0.165,0.695,0.177,0.973,0.429,0.365,0.779,0.735,0.28,0.6,0.679,0.101,0.179,0.997,0.267,0.403,0.943,0.818,0.302,0.984,0.973,0.607,0.783,0.213,0.261,0.034,0.614,0.567,0.514,0.238,0.722,0.353,0.024,0.421,0.304,0.231,0.229,0.478,0.699,0.551,0.837,0.401,0.559,0.69,0.116,0.21,0.811,0.537,0.154,0.206,0.518,0.334,0.739,0.976,0.408,0.655,0.653,0.014,0.917,0.704,0.233,0.92,0.467,0.687,0.247,0.502,0.377,0.078,0.883,0.08,0.297,0.855,0.057,0.012,0.079,0.645,0.072,0.591,0.272,0.902]]}}'

You have successfully built the docker image for the PrimeHub model deployment.

Push the Image

  • Next, push the image into the docker hub (or other docker registries) and check PrimeHub tutorial to serve the model under PrimeHub.

    Tag your docker image.

    docker tag my-model-image test-repo/my-model-image

    Then push to docker registry.

    docker push test-repo/my-model-image

Reference

PreviousModel Image for PythonNextReusable Base Image

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