Create a virtual environment, run pip install -e . and then run pytest. If the test cases pass you should be good to start developing.

The project contains the following core folders:

• credentials/ # The other secrets folder
• blueno/ # Shared code for our ML platform
• dashboard/ # Code for the ELVO App Engine dashboard
• data/ # Contains all downloaded data. Data used in the code should be stored in the cloud.
• docs/ # Documentation for the project.
• etl/ # All data pipeline scripts, code which should be scheduled on Airflow
• logs/ # For storing application logs
• ml/ # All ML specific scripts, contains the blueno ML tookit as well
• models/ # For storing trained ML models (HDF5, etc.)
• notebooks/ # Contains all notebooks.
• secrets/ # Store your secrets in this folder, so they don’t get uploaded to GitHub.

dashboard, etl, and ml should be seen as top-level python projects. This means each folder should contain top level scripts with packages as sub-folders. As our codebase is small, we are keeping them in a single repo but there are plans to separate these in the future.

To contribute, create a pull request. Every PR should be reviewed by at least one other person. See this gist for a guide on how to review a PR.

For developing on GPUs. See the docs folder for more info.