Aerostat

A simple CLI tool to deploy your Machine Learning models to cloud, with public API and template connections ready to go.

Get started

Installation

The name Aerostat has been used by another PyPI project, please install this package with:

pip install aerostat-launcher

Once installed, it can be used directly via aerostat. If it doesn't work, add python -m prefix to all commands, i.e. python -m aerostat deploy.

Only three commands needed for deploying your model: install, login, and deploy.

Setup

1. Run the following command to install all the dependencies needed to run Aerostat. Please allow installation in the pop-up window to continue.

aerostat install

2. To login to Aerostat, you need to run the following command:

aerostat login

You will be prompted to choose an existing AWS credentials, or enter a new one. The AWS account used needs to have AdministratorAccess.

Deploy

To deploy your model, you need to dump your model to a file with pickle, and run the following command:

aerostat deploy

You will be prompted to enter:

• the path to your model file
• the input columns of your model
• the ML library used for your model
• the name of your project

Or you can provide these information as command line options like:

aerostat deploy --model-path /path/to/model --input-columns "['col1','col2','col3']" --python-dependencies scikit-learn --project-name my-project

Connections

Aerostat provides connection templates to use your model in various applications once it is deployed. Currently, it includes templates for:

• Microsoft Excel
• Google Sheets
• Python / Jupyter Notebook

Visit the URL produced by the aerostat deploy command to test your model on cloud, and get the connection templates.

Other Commands

List

To list all the projects you have deployed, run:

aerostat ls

Info

To find deployment information of a specific project, such as API endpoint, run:

aerostat info

then choose the project from the list. You can also provide the project name as a command line option like:

aerostat info my-project

Future Roadmap

• Improve user interface, including rewrite prompts with Rich, use more colors and emojis
• Add unit tests
• Adopt Semantic Versioning once reach v0.1.0 and add CI/CD
• Support SSO login
• Support deploying to GCP