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