modal-compute

name	modal-compute
description	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.

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