- A seperate controller VM to install fabric - python module and host the files found on my github. 
- Hadoop runs a master and multiple slave node - in the sample code i use three slave nodes in the cluster.
- 4 GB ram on the terminal box(controller) and 4GB ram for the Hadoop master node and 1GB each on the slaves.
- I used Oracle Virtual Box.
- 16 GB ram in total, atleast.

Tests complete on Ubuntu Nodes as of Jan 3rd week - 2017
Next set of updates are set for April 2017.
- Vagrant file pending, Security updates pending

Project was built using Fabric 1.13 Package - Stable version as of Dec 2016
Tested on Ubuntu  ; support for CentOS/RHEL with SE will be added in few months 
Create Terminalbox( install fabric here) to run your script											

All vms should be of same distribution, Currently it works on ubuntu\n
Hadoop related VMs - one MasterNode and ANY number of Slave nodes, use Virtual box. 
On virtual box -create the VMs with one Regular NAT adapter to use ur host os's Internet
And create one other network adapter with Virtual box host only mode
I.3 - > Each VM has one NAT adapter (internet bound) and Virtual box host only mode adapter ( to talk to each other) 
This way you get a pre interconnected Set of VMS, which can talk to each other ; No need for DNS ; No need to set static IPs
Pre-inteconnected nodes or cluster with IP addresses on virtual adapter which we use.
Use the Private IPs that are generated and add it to the env values in the scripts
Pre-requisites for the script to function ->  properly install fabric as root
Use command -> pip install fabric==1.13    
If its a new server(terminal-box) run the yum /apt upgrade before starting this script even the script calls it.

	Vagrantfile for the intial VM setups left to be added
	As the script runs as HDUSER, vagrant for the VMs is the best solution 

if you never used the fabric library, read up a little :)
For advanced users: with understanding of Hadoop and fabric:
Ready made Hadoop_config_files ( from my github) needs to stored in the terminal-box(where fabric is installed)  
You can locate this function, if you are not sure where it is stored
these files will be copied over to the Hadoop Master & Slave nodes by the script
Until the VagrantFile is added
Set up VMs and install fabric 1.13 latest version ( Only on the terminal box) which is outside the Hadoop cluster
Set the host names like -> master, slave1, slave2 on the vms that belong to the Hadoop cluster
Update the master and slave names in the slaves file of the hadoop config folder ( that you downloaded from my git)(
Edit the ips address on the script( in roledefs and add to host file variable
Set up openssh-server on the vms, 
Sset permitlogin as yes and strictmode to no in the sshd config file in the etc folder
Disable firewall with the fab disable_firewall , (temporary work around)
Functions for appropriate firewall settings will be added
Important: For Intial user creation, comment out the env.user variable in script and run fab create_hduser
Enter root password of the VMs, Once
Once the Users are created ( u will need the credentials to run the script as hduser)
Once HDUSER is created, uncomment the env.user variable and start the deployement with the command -->> fab deploy
Script will install,start and test  Hadoop, on the IPs you set on  the roledef variables in script
script will prompt u for password for intital ssh configs and before the cluster starts

If you understand a little of the Fabric library, All the comments should get you going in the direction for your project.
Keep all the Program logic in Python; Thats the magic here ! Reason I love this tool
Let Fabric be the wrapper to carry the commands to all the host & the host groups defined;
you can define which user you want fabric to run your commands in the login shell.
The reason why we run the script as HDUSER; 
The ERROR handling functionality is basic, Observe the tasks with and without warn_only as true
I have used the classic task method ; not using @task decorator  ; going for single flat namespace to get the job done easy.