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On master and nodes

Pull images form internet access laptop

docker pull gcr.io/google_containers/kube-apiserver-amd64:v1.5.0
docker pull gcr.io/google_containers/kube-controller-manager-amd64:v1.5.0
docker pull gcr.io/google_containers/kube-proxy-amd64:v1.5.0
docker pull gcr.io/google_containers/kube-scheduler-amd64:v1.5.0
docker pull weaveworks/weave-npc:1.8.2
docker pull weaveworks/weave-kube:1.8.2

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############################# Support Vector Machine ############################
library(kernlab, quietly=TRUE)

# Build a Support Vector Machine model.
set.seed(crv$seed)
crs$ksvm <- ksvm(C=0.5, as.factor(bResult) ~ .,
                 data=crs$dataset[crs$train,c(crs$input, crs$target)],
                 kernel="rbfdot", kpar=list(sigma = 0.01),
                 prob.model=TRUE)

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################ Alternative hyper-parameter tuning method using Caret ##################################

library(caret)
library(dplyr)

# load the dataset
matches = crs$dataset # the following code uses "matches" as the data frame, just assign the value
# set default seed value to 42 (Rattle default seed value)
seed_value = 42
set.seed(seed_value)

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from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

from surprise import KNNWithMeans
from surprise import Dataset
from surprise import accuracy
from surprise.model_selection import train_test_split

# Load the movielens-100k dataset  UserID::MovieID::Rating::Timestamp
data = Dataset.load_builtin('ml-100k')

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from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

from surprise import KNNWithMeans
from surprise import Dataset
from surprise import accuracy
from surprise.model_selection import train_test_split

# Load the movielens-100k dataset  UserID::MovieID::Rating::Timestamp
data = Dataset.load_builtin('ml-100k')

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state_size = 10
num_layers = 3

X = tf.placeholder(tf.float32, [None, 100, 10])

# the second dimension is size 2 and represents
# c, m ( the cell and hidden state ) 
# set the batch_size to None
state_placeholder = tf.placeholder(tf.float32, [num_layers, 2, 
                                    None, state_size])

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from __future__ import print_function
import boto3
import os
import sys
import uuid
from PIL import Image
import PIL.Image
     
s3_client = boto3.client('s3')
     

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from __future__ import print_function

def lambda_handler(event, context):
    for record in event['Records']:
        print(record['eventID'])
        print(record['eventName'])       
    print('Successfully processed %s records.' % str(len(event['Records'])))

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source ~/shrink_venv/bin/activate
cd $VIRTUAL_ENV/lib/python3.6/site-packages
zip -r9 ~/CreateThumbnail.zip *
cd ~
zip -g CreateThumbnail.zip CreateThumbnail.py
aws lambda update-function-code --function-name CreateThumbnail --zip-file fileb:///home/ec2-user/CreateThumbnail.zip

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from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

from surprise import SVDpp
from surprise import SVD
from surprise import Dataset
from surprise import accuracy
from surprise.model_selection import train_test_split
from surprise.model_selection import GridSearchCV