modal-compute

Original：🇺🇸 English

Translated

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.

6installs

Sourcegetcompanion-ai/feynman

Added on2026-05-09

NPX Install

npx skill4agent add getcompanion-ai/feynman modal-compute

SKILL.md Content

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Modal Compute

Use the

modal

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

Setup

bash

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

python

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
```