AgentSkillsCN

modal-gpu

借助 Modal 无服务器平台，在云端 GPU 上运行 Python 代码。当您需要 A100/T4/A10G GPU 来训练机器学习模型时，可使用此功能。涵盖 Modal 应用的搭建、GPU 的选择、函数内部的数据下载以及结果的处理等环节。

SKILL.md
--- frontmatter
name: modal-gpu
description: 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.

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

TopicReference
Basic StructureGetting Started
GPU OptionsGPU Selection
Data HandlingData Download
Results & OutputsResults
TroubleshootingCommon Issues

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

ScenarioApproach
Quick GPU experimentsgpu="T4" (16GB, cheapest)
Medium training jobsgpu="A10G" (24GB)
Large-scale traininggpu="A100" (40/80GB, fastest)
Long-running jobsSet timeout=3600 or higher
Data from HuggingFaceDownload inside function with hf_hub_download
Return metricsReturn dict from function

Running

bash
# Run script
modal run train_modal.py

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

External Resources