A local development data science workbench for integrating production-like workflows
This is a data scientist workbench for locally tracking data and model artifacts in a production-like way. This code is only intended to give you better tools for local workflow management, and this should not be used in production. To read more about my motivation, read my blog post about it.
You will need Docker installed.
Clone the repo. Edit default.env, or create a new env file, with your own preferred secrets.
Run the services using docker-compose --env-file default.env up -d, substituting if necessary your custom environment file.
This is not warranted nor actively maintained. Please feel free to adjust this to your own use cases.