maedoc/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.
tools
Updated Apr 21, 2026
$ install --global
skillsauthnpx skillsauth add maedoc/tvb-wiki modal-computeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
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OWASP Dep-Check
50%
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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