modal-compute

Apps & Automation

Run explicitly chosen research benchmark or replication jobs on Modal's serverless infrastructure. Use when a Feynman research workflow needs burst remote GPU compute and the Modal CLI is available.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/companion-inc/feynman/blob/HEAD/skills/modal-compute/SKILL.md

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Modal Compute

Use the modal CLI for bounded research experiments that need burst GPU compute. No pod lifecycle to manage; write a decorated Python script, run it, and save raw outputs back into the research artifact folder. Do not use this skill to deploy services or unrelated batch jobs.

Setup

pip install modal
modal setup

Commands

Command	Description
`modal run script.py`	Run one research experiment script on Modal
`modal run --detach script.py`	Run a long research experiment and record the returned app/run identifier
`modal shell --gpu a100`	Open an interactive GPU shell for research environment debugging

GPU types

T4, L4, A10G, L40S, A100, A100-80GB, H100, H200, B200

Multi-GPU: "H100:4" for 4x H100s.

Script pattern

import modal

app = modal.App("experiment")
image = modal.Image.debian_slim(python_version="3.11").pip_install("torch==2.8.0")

@app.function(gpu="A100", image=image, timeout=600)
def train():
    import torch
    # training code here

@app.local_entrypoint()
def main():
    train.remote()

When to use

Bounded replication or benchmark jobs that need burst GPU
No persistent state needed between runs
Check availability: command -v modal