getcompanion-ai/modal-compute
skills/modal-compute/SKILL.md
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 --global
skillsauthnpx skillsauth add getcompanion-ai/feynman modal-computeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 22, 2026, 12:29 AM125.2s1 file scanned
SKILL.md
- name:
- modal-compute
- description:
- 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.
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
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