qtangs

AWSTemplateFormatVersion: 2010-09-09
Description: >
  Template for creating S3 bucket and DynamoDB table to hold Terraform state and locks
  Validate: aws cloudformation validate-template --template-body file://terraform_state.yml
  Deploy: aws cloudformation create-stack --region us-east-1 --stack-name Terraform-State-Resources --enable-termination-protection --template-body file://terraform_state.yml --parameters ParameterKey=TerraformStateBucketPrefix,ParameterValue=terraform-state ParameterKey=TerraformStateLockTableName,ParameterValue=terraform-state-locks
Parameters:
  TerraformStateBucketPrefix:
    Type: String
    Default: terraform-state
    Description: A prefix for S3 bucket name, account id will be added to ensure global uniqueness

qtangs

#!/bin/bash

export PKG_DIR="python"

rm -rf ${PKG_DIR} && mkdir -p ${PKG_DIR}

docker run --rm -v $(pwd):/foo -w /foo lambci/lambda:build-python3.6 \
    pip install -r requirements.txt --no-deps -t ${PKG_DIR}

qtangs

Problem #1 (Kubernetes)

We will be using kubernetes to manage our containers, and we need to know how basic kubernetes objects work, and how to provision them.

Challenge

Write the k8 config required to deploy a basic nginx webserver with this image nginxdemos/hello, using minikube to provision your cluster control plane.

Your resulting created objects should look similar to the likes of the below:

NAME                                     READY   STATUS    RESTARTS   AGE
pod/nginx-hello-world-84c67f76c4-4g5sd   1/1     Running   0          6s

qtangs

python -c '''
import dash; import dash_html_components as html; app = dash.Dash(); app.layout=html.H1("Yay! It works!"); app.run_server()
'''

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cd covid-19-dashboard
python -m venv venv  # or python3 -m venv venv
source venv/bin/activate
pip list  # ensure that only pip and setuptools are listed
echo "dash==1.9.1" > requirements.txt
pip install -r requirements.txt

qtangs

covid-19-dashboard
|_ dashboard
   |_ assets
   |_ data
   |_ model.py
   |_ view.py
   |_ controller.py
   |_ app.py
|_ .gitignore
|_ Procfile

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python app.py
### Output:
 Running on http://127.0.0.1:8050/
 Debugger PIN: 853-611-907
 * Serving Flask app "app" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: on
Running on http://127.0.0.1:8050/

qtangs

import dash
import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div(children=[
    html.H1(children='Hello Dash'),

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import pandas as pd

COUNTRY_COLUMN = "Country/Region"
LOCATION_COLUMN = "Location"
DATE_COLUMN = "Date"
TYPE_COLUMN = "Type"
INFECTED_COLUMN = "Infected"
RECOVERED_COLUMN = "Recovered"
DEATHS_COLUMN = "Deaths"
CURRENT_COLUMN = "Current"

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# i. Download sample data and extract features and label (fraud/nonfraud).
...

# ii. Convert the n-dimensional arrays into RecordIO format (a highly efficient data format).
import sagemaker.amazon.common as smac
buf = io.BytesIO()
smac.write_numpy_to_dense_tensor(buf, features, labels)
...

# iii. Store the RecordIO data into S3 bucket.