{"library":"ob-metaflow","title":"Metaflow","description":"Metaflow is a human-centric framework for building and managing real-life data science projects, from prototyping to production. It enables data scientists and ML engineers to rapidly develop, deploy, and operate ML workflows. The `ob-metaflow` distribution is a specific PyPI package that provides the core Metaflow library. Its current version is 2.19.21.1, and it follows the frequent release cadence of the main Metaflow project.","language":"python","status":"active","last_verified":"Thu Apr 16","install":{"commands":["pip install ob-metaflow"],"cli":{"name":"metaflow","version":"Usage: metaflow [OPTIONS] COMMAND [ARGS]..."}},"imports":["from metaflow import FlowSpec","from metaflow import step","from metaflow import current"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from metaflow import FlowSpec, step, card\nimport os\n\nclass MyFirstMetaflowFlow(FlowSpec):\n    \"\"\"\n    A simple Metaflow flow demonstrating basic steps.\n    \"\"\"\n    @step\n    def start(self):\n        self.message = \"Hello Metaflow!\"\n        print(f\"Starting flow with message: {self.message}\")\n        self.next(self.process_data)\n\n    @step\n    def process_data(self):\n        self.data = [len(self.message), 42]\n        print(f\"Processing data: {self.data}\")\n        self.next(self.end)\n\n    @step\n    def end(self):\n        print(f\"Flow finished. Final data: {self.data}\")\n\nif __name__ == '__main__':\n    # To run: python your_flow_file.py run\n    # For this quickstart, we just instantiate it.\n    # Metaflow typically expects execution via its CLI for full features.\n    flow = MyFirstMetaflowFlow()\n    # Running via the CLI: python this_file.py run","lang":"python","description":"This quickstart defines a simple Metaflow `FlowSpec` with three steps: `start`, `process_data`, and `end`. It prints messages and passes data between steps. Note that Metaflow flows are typically executed via the Metaflow CLI (e.g., `python your_flow_file.py run`) to leverage its full capabilities like artifact tracking, resumption, and distributed execution, rather than just running the Python script directly.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}