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Model environments help configure behavior based on deployment stage (for example, production vs. staging). You can access the environment details through kwargs in the Model class.

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Retrieve the environment

Access the environment in __init__:
model/model.py
def __init__(self, **kwargs):
    self._environment = kwargs["environment"]

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Configure behavior per environment

Use the environment in your load() method to set up environment-specific behavior:
model/model.py
def load(self):
    if self._environment.get("name") == "production":
        self.setup_sentry()
        self.setup_logging(level="INFO")
        self.load_production_weights()
    else:
        self.setup_logging(level="DEBUG")
        self.load_default_weights()
This lets you:
  • Customize logging levels.
  • Load environment-specific model weights.
  • Enable monitoring tools (for example, Sentry).
When you promote a deployment without re-deploying, load() doesn’t re-run, so environment-specific configuration from the original deployment persists. You can configure an environment to create a fresh deployment on every promotion. See Re-deploy on promotion for details.

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Next steps

  • The Model class: Read configuration, secrets, and runtime information in __init__ and load.
  • Environments: Promote deployments across stages and configure re-deploy on promotion.