3.2

Package a Model Image for R

Enterprise Applicable to Enterprise tier only

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

Check everything is ready to go by running:

s2i usage seldonio/seldon-core-s2i-r:0.1

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')

  • Create a .s2i folder and create a .s2i/environment file with the following content:

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

  • 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 .s2i/environment
    • 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, .s2i/environment, R scripts, and model file...etc.

If this folder is managed by Git, please commit all changes into the git.

We will use seldonio/seldon-core-s2i-r:0.1 as a base image, installing environment and packaging our model file into the target, my-model-image. You can use the following command to package the docker image:

s2i build . seldonio/seldon-core-s2i-r:0.1 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:

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]]}}'

Replace ndarray content in curl example according to your application.

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

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

← Package a Model Image for PythonPackage a Model Image from a Reusable Base Image →