{"library":"outerbounds","title":"Outerbounds (Metaflow Distribution)","description":"Outerbounds is an opinionated distribution of Metaflow, designed to streamline machine learning workflows by providing pre-configured cloud infrastructure and a curated set of dependencies. It aims to reduce administrative overhead, allowing data scientists to focus more on model development. The current version is 0.12.28 and it follows a continuous release cadence, often aligning with Metaflow updates.","language":"python","status":"active","last_verified":"Thu Apr 16","install":{"commands":["pip install outerbounds"],"cli":null},"imports":["from metaflow import FlowSpec","from metaflow import step","from metaflow import current","from metaflow import project"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from metaflow import FlowSpec, step\n\nclass MyFirstFlow(FlowSpec):\n    @step\n    def start(self):\n        self.message = 'Hello, Outerbounds!'\n        print(self.message)\n        self.next(self.end)\n\n    @step\n    def end(self):\n        print(f\"Flow finished with message: {self.message}\")\n\nif __name__ == '__main__':\n    MyFirstFlow()","lang":"python","description":"This quickstart demonstrates a basic Metaflow workflow. While `outerbounds` provides underlying configuration for cloud execution, the code itself remains standard Metaflow. To run this, save it as a Python file (e.g., `my_flow.py`) and execute `python my_flow.py run` from your terminal. For cloud execution, you'll need to configure your Outerbounds environment (e.g., `metaflow configure aws`).","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}