Training deep learning models on AWS EC2 Spot Instances
Project description
Spotty drastically simplifies training of deep learning models on AWS:
- it makes training on AWS GPU instances as simple as training on your local computer
- it automatically manages all necessary AWS resources including AMIs, volumes, snapshots and SSH keys
- it makes your model trainable on AWS by everyone with a couple of commands
- it uses tmux to easily detach remote processes from their terminals
- it saves you up to 70% of the costs by using Spot Instances
Documentation
- See the documentation page.
- Read this article on Medium for a real-world example.
Installation
Requirements:
- Python >=3.5
- AWS CLI (see Installing the AWS Command Line Interface)
Use pip to install or upgrade Spotty:
$ pip install -U spotty
Get Started
-
Prepare a
spotty.yaml
file and put it to the root directory of your project: -
Create an AMI. Run the following command from the root directory of your project:
$ spotty aws create-ami
In several minutes you will have an AMI with NVIDIA Docker that Spotty will use 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: "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
Ctrl + b
, thend
combination of keys. To be attached to that session later, just use thespotty ssh
command again.
Contributions
Any feedback or contributions are welcome! Please check out the guidelines.
License
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