spotty: A Python repository from ZohaibAhmed

Spotty simplifies training of Deep Learning models on AWS:

it makes training on AWS GPU instances as simple as a training on your local computer
it automatically manages all necessary AWS resources including AMIs, volumes and snapshots
it makes your model trainable on AWS by everyone with a couple of commands
it detaches remote processes from SSH sessions
it saves you up to 70% of the costs by using Spot Instances

Documentation

Requirements:

Use pip to install or upgrade Spotty:

$ pip install -U spotty

Prepare a spotty.yaml file for your project.
- See the file specification here.
- Read this article for a real-world example.
Create an AMI with NVIDIA Docker. Run the following command from the root directory of your project (where the spotty.yaml file is located):
```
$ spotty create-ami
```
In several minutes you will have an AMI that can be used for all your projects within the AWS region.
Start an instance:
```
$ spotty start
```
It will run a Spot Instance, restore snapshots if any, synchronize the project with the running instance and start the Docker container with the environment.
Train a model or run notebooks.

You can run custom scripts inside the Docker container using the spotty run <SCRIPT_NAME> command. Read more about custom scripts in the documentation: Configuration File: "scripts" section.

To connect to the running container via SSH, use the following command:
```
$ spotty ssh
```
It runs a tmux session, so you can always detach this session using Crtl + b, then d combination of keys. To be attached to that session later, just use the spotty ssh command again.