README.md

Spam Filter

This is a Dagster project scaffolded with dagster project scaffold.

It demonstrates how to create a Dagster workflow that builds and deploys a spam identification model to a local API.

Getting started

First, install your Dagster code location as a Python package. By using the --editable flag, pip will install your Python package in "editable mode" so that as you develop, local code changes will automatically apply.

pip install -e ".[dev]"

Create the test database and install the sample data

python database.py
python sample_data.py

Then, start the Dagster UI web server:

dagster dev

Open http://localhost:3000 with your browser to see the project.

You can start writing assets in spam_filter/assets.py. The assets are automatically loaded into the Dagster code location as you define them.

Development

Adding new Python dependencies

You can specify new Python dependencies in setup.py.

Unit testing

Tests are in the spam_filter_tests directory and you can run tests using pytest:

pytest spam_filter_tests

Schedules and sensors

If you want to enable Dagster Schedules or Sensors for your jobs, the Dagster Daemon process must be running. This is done automatically when you run dagster dev.

Once your Dagster Daemon is running, you can start turning on schedules and sensors for your jobs.

Deploy on Dagster Cloud

The easiest way to deploy your Dagster project is to use Dagster Cloud.

Check out the Dagster Cloud Documentation to learn more.