A simple CLI tool to deploy your Machine Learning models to cloud, with public API and template connections ready to go.
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
.
- 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
- 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.
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
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.
To list all the projects you have deployed, run:
aerostat ls
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
- 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