marimo-batch

Prepare Marimo notebooks for scheduled batch execution with CLI arguments.

2|Updated Nov 24, 2024

Search Tags:#pydantic #mlops #batch processing #marimo #weights and biases #cli arguments

Ready to run

npx skills add https://github.com/DanSnow/dotfiles --skill marimo-batch

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: marimo-batch
Source: https://github.com/DanSnow/dotfiles/tree/main/private_dot_claude/skills/marimo-batch
Command: npx skills add https://github.com/DanSnow/dotfiles --skill marimo-batch

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires marimo, pydantic, python-dotenv, rich, wigglystuff, openai, wandb, and includes scripts (resource) and references (resource) components.

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.

Frequently Asked Questions about marimo-batch

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run Marimo notebooks for batch processing with CLI arguments?▼

To run Marimo notebooks for batch processing, this skill integrates Pydantic models with CLI arguments, allowing you to parameterize and execute notebooks non-interactively via command-line interfaces.

Can I use Pydantic models to parameterize ML training jobs in Marimo?▼

Yes, you can use Pydantic models to parameterize ML training jobs in Marimo by mapping model fields to CLI arguments and UI controls, enabling both interactive tuning and automated batch execution.

How does Marimo integrate with Weights and Biases for experiment tracking?▼

Marimo integrates with Weights and Biases by optionally logging model parameters and training metrics directly from the notebook, streamlining experiment tracking during scheduled batch runs.

What is the best way to manage environment variables and API keys in Marimo notebooks?▼

The best way to manage environment variables in Marimo notebooks is using built-in python-dotenv integration, which provides a UI for managing environment variables and API keys during interactive and automated runs.

Does this approach support both interactive UI tuning and automated non-interactive runs?▼

Yes, this approach supports interactive UI parameter tuning through Marimo UI elements like sliders and forms, alongside automated non-interactive batch execution driven by CLI arguments for ML model training.