ZohaibAhmed / spotty

Train Deep Learning models on AWS Spot Instances

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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


  • See the wiki section for the documentation.
  • Read this article on Medium for a real-world example.



Use pip to install or upgrade Spotty:

$ pip install -U spotty

Get Started

  1. Prepare a spotty.yaml file for your project.

    • See the file specification here.
    • Read this article for a real-world example.
  2. 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.

  3. 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.

  4. 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.


MIT License


Train Deep Learning models on AWS Spot Instances

License:MIT License


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