marimo-batch

What problem does it solve?

This Skill streamlines the process of preparing Marimo notebooks for scheduled batch execution, making it easier to manage and run ML training jobs.

Core Features & Use Cases

• CLI Argument Integration: Allows parameters defined in Pydantic models to be controlled via command-line arguments, enabling non-interactive batch runs.
• UI-driven Parameterization: Supports interactive parameter tuning through Marimo's UI elements like sliders and forms.
• Weights & Biases Integration: Optionally logs model parameters and training metrics to Weights & Biases for experiment tracking.
• Environment Variable Management: Integrates python-dotenv and provides a UI for managing environment variables, including API keys.
• Use Case: Configure a Marimo notebook for an ML model training job, specifying hyperparameters like learning rate and batch size via CLI arguments for a scheduled run, and automatically logging results to W&B.

Quick Start

Use the marimo-batch skill to configure the attached notebook.py for CLI execution by specifying the learning rate and sample size.