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Simple walkthrough of Dockerized Data Science Dev + Deploy pipeline using Iris data

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dushyantkhosla/ds-deploy-walkthru-iris

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Getting Started

Get Data

  • If data exists on Local, copy it to the server with scp
    • If data sits on the cloud, download it inside the server
  • Optional: Copy SSH keys scp -rp ~/.ssh user@remote-server:/tmp/.ssh
    • Also run ssh-add ~/.ssh/id_rsa after adding the .ssh folder to /root

Get Infra

  • SSH into the Server
  • Pull a Docker Container
docker pull <image>
docker images

Get Code

git clone git-url

Start Docker

  • Start a Docker Container using the appropriate image, map ports and mirror directories
docker run -it -v $pwd:/home \
               -p 8080:8080 \
               -p 5000:5000 \
               -p 3128:3128 \
               your-docker-image
  • PS: If Docker commands do not work, run systemctl start docker as root and try again

  • Configure Git

git config --global http.proxy http://proxy-url:port
git config --global user.email 'you@domain.com'
git config --global user.name 'FirstName LastName'

Directory Structure

./
DOCKERFILE      
Jenkinsfile     
README.md       
environment.yml

./data:
  interim                 
  processed
  raw

./figures:

./models:

./notebooks:
  01-Obtain.ipynb    
  02-Scrub.ipynb     
  03-Explore.ipynb   
  04-Model.ipynb     
  05-iNterpret.ipynb

./references:

./reports:

./src:
  __init__.py
  obtain      
  scrub       
  explore     
  model
  interpret   

  tests