| 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
@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
/goal Inheritance
This skill inherits the SeaBridgeAI /goal default protocol. Frame the work with a persistent goal, Definition of Done, validation plan, risks, dependencies, expected artifacts, and completion evidence. Do not claim completion until the DoD is validated or a hard blocker is documented.
Canonical protocol: C:\Users\adelm\SeaBridgeAI\everything-claude-code\protocols\GOAL_PROTOCOL.md
