Feynman modal-compute

Run GPU workloads on Modal's serverless infrastructure. Use when the user needs remote GPU compute for training, inference, benchmarks, or batch processing and Modal CLI is available.

install

source · Clone the upstream repo

git clone https://github.com/getcompanion-ai/feynman

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/getcompanion-ai/feynman "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/modal-compute" ~/.claude/skills/getcompanion-ai-feynman-modal-compute && rm -rf "$T"

manifest: skills/modal-compute/SKILL.md

source content

Modal Compute

Use the

modal

CLI for serverless GPU workloads. No pod lifecycle to manage — write a decorated Python script and run it.

Setup

pip install modal
modal setup

Commands

Command	Description
`modal run script.py`	Run a script on Modal (ephemeral)
`modal run --detach script.py`	Run detached (background)
`modal deploy script.py`	Deploy persistently
`modal serve script.py`	Serve with hot-reload (dev)
`modal shell --gpu a100`	Interactive shell with GPU
`modal app list`	List deployed apps

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

Stateless burst GPU jobs (training, inference, benchmarks)
No persistent state needed between runs
Check availability:
```
command -v modal
```