Environment management with dbt-fal
Our recommended way of using dbt-fal is to use named environments. They help you define reusable Python environments that are automatically managed by dbt-fal. You can use them by creating a
fal_project.yml file in the same folder as your dbt project, and then use these environments in any Python model.
In your dbt project folder:
$ touch fal_project.yml
# Paste the config below
- name: ml
and then in your dbt model:
$ vi models/orders_forecast.py
def model(dbt, fal):
dbt.config(fal_environment="ml") # Add this line
df: pd.DataFrame = dbt.ref("orders_daily")
dbt.config(fal_environment=“ml”) will give you an isolated clean env to run things in, so you dont pollute your package space.