Awesome

Augury

Jupyter Notebooks and machine-learning service for the Tipresias app

Running things

Setup

To manage environemnt variables:
- Install direnv
- Add eval "$(direnv hook bash)" to the bottom of ~/.bashrc
- Run direnv allow . inside the project directory
To build and run the app: docker-compose up --build

Run the app

docker-compose up
Navigate to localhost:8008.

Run Jupyter notebook in Docker

If it's not already running, run Jupyter with docker-compose up notebook.
The terminal will display something like the following message:

notebook_1  | [I 03:01:38.909 NotebookApp] The Jupyter Notebook is running at:
notebook_1  | [I 03:01:38.909 NotebookApp] http://(ea7b71b85805 or 127.0.0.1):8888/?token=dhf7674ururrtuuf8968lhhjdrfjghicty57t69t85e6dhfj
notebook_1  | [I 03:01:38.909 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

Copy the URL given and paste it into your browser.
Alternatively, copy just the token from the terminal, navigate your browser to http://localhost:8888, and paste the token into the given field.

Testing

Run Python tests

docker-compose run --rm data_science kedro test --no-cov
- Note: Remove --no-cov to generate a test-coverage report in the terminal.
Linting: docker-compose run --rm <python service> pylint --disable=R <python modules you want to lint>
- Note: -d=R disables refactoring checks for quicker, less-opinionated linting. Remove that option if you want to include those checks.

Deploy

augury is deployed to Google Cloud via Travis CI. See scripts/deploy.sh for specific commands.

Troubleshooting

When working with some of the larger data sets (e.g. player stats comprise over 600,000 rows), your process might mysteriously die without completing. This is likely due to Docker running out of memory, because the default 2GB isn't enough. At least 4GB is the recommended limit, but you'll want more if you plan on having multiple processes running or multiple Jupyter notebooks open at the same time.