marimo-batch

You can prepare Marimo notebooks for scheduled batch runs by defining a ModelParams schema, configuring parameters via UI or CLI, and executing a grid-based sampler to orchestrate reproducible, parameterized runs.

Grid-based hyperparameter exploration in Marimo notebooks uses a sampler to generate multiple parameterized runs from a defined ModelParams schema, allowing systematic testing of variable combinations for ML experimentation.

You run a grid search for ML hyperparameters from the command line by configuring ModelParams in the CLI and executing uv run grid.py, which generates parameterized notebook runs for your experimentation pipeline.

Yes, the Marimo batch preparation pattern integrates with Weights and Biases (WandB) optionally, allowing you to configure environment variables and track grid-based experimentation runs within your ML pipelines.

Yes, you can submit Marimo notebook batch jobs to external providers by using the included grid launcher, which is designed to submit generated parameterized runs to platforms like Hugging Face for remote execution.

You need a structured pattern for Marimo batch notebook execution because scheduled runs require careful setup and repeatable configuration to ensure reproducible, parameterized jobs across automated ML experimentation pipelines.