EC2 tasks for Fabric
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A set of tasks for use with Fabric and EC2.


Now available on PyPi at Installation is therefore as simple as:

pip install cloth


Export your EC2 credntials as environment variables.

export AWS_EC2_REGION=eu-west-1
export AWS_ACCESS_KEY_ID=<your-access-key>
export AWS_SECRET_ACCESS_KEY=<your-secret-key>


To use just import some or all of the tasks into your fabric file. Or create a blank with the following contents.

#! /usr/bin/env python
from cloth.tasks import *

This will give you a good few commands.

⚡ fab -l
Available commands:

  all         All nodes
  free        Show memory stats
  list        List EC2 name and public and private ip address
  nodes       Select nodes based on a regular expression
  preview     Preview nodes
  production  Production nodes
  updates     Show package counts needing updates
  upgrade     Upgrade packages with apt-get
  uptime      Show uptime and load

Of most interest should be the 'all' and 'nodes' tasks. These allow you to load EC2 instances for further command running.

⚡ fab all list

The above should list all of your EC2 instances including the name and public and private ip addresses.

⚡ fab nodes:"^production.*" list

The above should list all of your EC2 instances that start with 'production'. This takes a regex as the argument so you can get whatever instances you like.

⚡ fab all uptime

As an example of running a command on a set of EC2 instances try the above. This should show the uptime and load averages for all your EC2 instances. Use -P as well to have that happen in parallel.

Opinionated Tasks

I generally use a convention for the names of my EC2 machines, in particular:


The production and preview tasks simple filter for those with a platform value of production or preview. More interesting is that roles are being set based on the second part of the name. For instance if I have a set of instances called:

  • production-backend-1
  • production-backend-2
  • production-backend-3
  • production-proxy
  • production-database

I could write a task like so:

def passenger():
    "Show details about passenger performance"

The run that task with:

⚡ fab all passenger

That task would only be run on the three backend instances.