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Modifying an Existing Docker Image

To install a custom package or modify an existing docker image we need to

1. run a docker a container from the image we wish to modify
2. modify the docker container
3. commit the changes to the container as a docker image
4. test changes made to image

1.) Running a docker container from an image

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library(plyr)

find_zones <- function(x) {
  x.mean <- mean(x)
  x.sd <- sd(x)
  boundaries <- seq(-4, 4)
  # creates a set of zones for each point in x
  zones <- sapply(boundaries, function(i) {
    i * rep(x.sd, length(x))
  })

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const [widgets, setWidgets] = React.useState<Widget[]>([]);
const [animals, setAnimals] = React.useState<Animal[]>([]);


useSyncToTable<Widget[]>({
  thingToWatch: widgets,
  tableName: "widgets",
  debounce: 500,
});
  

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# function takes the following parameters:
# - func: the function you want to call with a timeout
# - ...: any arguments that should be passed to your function
# - seconds: duration until timeout
# - default: default value to return if timeout occurs
timeout.wrapper <- function(func, ..., seconds=3, default=NULL) {
  tryCatch({
    setTimeLimit(elapsed=seconds, transient=TRUE)
    func(...)
  }, error = function(e) {

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#---from your local---#
ssh -i yhat.pem ubuntu@your-ec2-ip-address

#---on the server---#

# let's add a user
sudo adduser greg

# update
sudo apt-get update

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library(XML)
library(uuid)
library(stringr)
library(plyr)
library(reshape2)
library(ggplot2)

f <- "https://raw.githubusercontent.com/chris-taylor/USElection/master/data/electoral-college-votes.csv"
electoral.college <- read.csv(f, header=FALSE)
names(electoral.college) <- c("state", "electoral_votes")

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import numpy as np
import pylab as pl
import pandas as pd

from sklearn import svm
from sklearn import linear_model
from sklearn import tree

from sklearn.metrics import confusion_matrix

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{
  "location": {
    "name": "Nederland",
    "region": "Colorado",
    "country": "United States of America",
    "lat": 39.96,
    "lon": -105.5,
    "tz_id": "America/Denver",
    "localtime_epoch": 1608142276,
    "localtime": "2020-12-16 11:11"

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import pandas as pd
from ggplot import *
from sklearn.datasets import fetch_20newsgroups
from sklearn.metrics import roc_curve

# vectorizer
from sklearn.feature_extraction.text import HashingVectorizer

# our classifiers
from sklearn.naive_bayes import BernoulliNB, MultinomialNB

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Environment

URL: Stage: <dev/staging/qa/prod> (if applicable)

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