Never forget your AWS instances!

anti_forgetful is a simple and handy tool for launch a single AWS instance from the terminal and tying it's lifetime to the lifetime of the process on your machine that launched it. This helps to avoid situations where you forget your instance and leave it running for a month. That could be thousands of dollars!

Please let me know if you have issues!

Just tell me how to use it.

First, if you haven't used AWS before:

1. Set up your AWS account.
2. Follow the first two steps ("Install the AWS CLI" and "Configure the AWS CLI") here.

Next, install anti_forgetful:

pip install anti_forgetful

Now, check out the example folder for an example of how to launch a Jupyter Lab server. To start building your instance, move to that directory and run:

anti_forgetful awscfg

This tells the launcher to use awscfg.py as your configuration file and starts to build your instance. It'll take a few minutes on a free t2.micro instance and will eventually launch a Jupyter Lab that is viewable on your local machine at http://localhost:8888/ via a ssh tunnel.

(Note: you specify configuration files without the .py at the end.)

Once you're done playing with the instance, just go to the terminal where you ran anti_forgetful and hit Ctrl-C. It'll kill the Jupyter Lab process and then disconnect and shutdown the instance. The instance will not be terminated, so you can launch it again later without losing data.

So how do I write one of these configuration files?

The configuration is specified as a Python file:

# The name of the public/private key pair and the security group created for
# your instance. If this key already exists, it won't be recreated.
key_pair_name = 'tutorial_key_pair' 
group_name = 'tutorial_group'

# What instance type do you want? https://aws.amazon.com/ec2/instance-types/
instance_type = 't2.micro'

# This option turns off strict host checking in SSH. This can be handy if you
# aren't worried about security and want to avoid some manual interaction 
# launching your instance.
no_strict_host_checking = True

# Metric monitoring will track the total amount of resources your instance has 
# consumed.  This option is False by default, set to True, to monitor resource
# usage
metric_monitoring = False

# The base image to build from. You probably shouldn't change this. 
base_image_id = 'ami-428aa838'

# The disk size requested from AWS EBS. In GB.
root_volume_size = 30  

# Your instance will be given a name so that it can be started and stopped!
# Two instances with the same name could get messy... You've been warned.
instance_name = 'tutorial_instance'

# This function is run once when your instance is built. Build your docker
# images here or install any packages you might want.
def setup_images(s):
    # Copy a file from the local machine to the instance. Accepts an optional
    # parameter "dest_filepath" for remote destination.
    s.copy_to_remote('docker-compose.yml')
    # Run a shell command on the remote instance.
    s.run_cmd('docker-compose pull')

# This function is run every time your instance boots up. 
def start_containers(s):
    # Forward a port from the remote machine to the local machine through an
    # ssh tunnel.
    s.ssh_port_forward(8888, 8888)
    # Star the docker containers!
    s.run_cmd('docker-compose up')

Just in case you still need to terminate some instances.

The awsterminate command will list all the instances you currently have running and give you the option of terminating them.

Miscellaneous

At the moment, this is pretty completely integrated with Docker. That could easily be changed.

I've only tried this on Ubuntu with Python 3.5 and Python 3.6.