# Modal Gpu

> Run Python code on cloud GPUs using Modal serverless platform. Use when you need A100/T4/A10G GPU access for training ML models. Covers Modal app setup, GPU selection, data downloading inside functions, and result handling. Use when this capability is needed.

- Skill: `tomevault-io/modal-gpu` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/modal-gpu`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/modal-gpu/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/modal-gpu

---


# Modal GPU Training

## Overview

Modal is a serverless platform for running Python code on cloud GPUs. It provides:

- **Serverless GPUs**: On-demand access to T4, A10G, A100 GPUs
- **Container Images**: Define dependencies declaratively with pip
- **Remote Execution**: Run functions on cloud infrastructure
- **Result Handling**: Return Python objects from remote functions

Two patterns:
- **Single Function**: Simple script with `@app.function` decorator
- **Multi-Function**: Complex workflows with multiple remote calls

## Quick Reference

| Topic | Reference |
|-------|-----------|
| Basic Structure | [Getting Started](references/getting-started.md) |
| GPU Options | [GPU Selection](references/gpu-selection.md) |
| Data Handling | [Data Download](references/data-download.md) |
| Results & Outputs | [Results](references/results.md) |
| Troubleshooting | [Common Issues](references/common-issues.md) |

## Installation

```bash
pip install modal
modal token set --token-id <id> --token-secret <secret>
```

## Minimal Example

```python
import modal

app = modal.App("my-training-app")

image = modal.Image.debian_slim(python_version="3.11").pip_install(
    "torch",
    "einops",
    "numpy",
)

@app.function(gpu="A100", image=image, timeout=3600)
def train():
    import torch
    device = torch.device("cuda")
    print(f"Using GPU: {torch.cuda.get_device_name(0)}")

    # Training code here
    return {"loss": 0.5}

@app.local_entrypoint()
def main():
    results = train.remote()
    print(results)
```

## Common Imports

```python
import modal
from modal import Image, App

# Inside remote function
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
```

## When to Use What

| Scenario | Approach |
|----------|----------|
| Quick GPU experiments | `gpu="T4"` (16GB, cheapest) |
| Medium training jobs | `gpu="A10G"` (24GB) |
| Large-scale training | `gpu="A100"` (40/80GB, fastest) |
| Long-running jobs | Set `timeout=3600` or higher |
| Data from HuggingFace | Download inside function with `hf_hub_download` |
| Return metrics | Return dict from function |

## Running

```bash
# Run script
modal run train_modal.py

# Run in background
modal run --detach train_modal.py
```

## External Resources

- Modal Documentation: https://modal.com/docs
- Modal Examples: https://github.com/modal-labs/modal-examples

---
> Converted and distributed by [TomeVault](https://tomevault.io/claim/benchflow-ai) — claim your Tome and manage your conversions.
<!-- tomevault:4.0:skill_md:2026-04-11 -->