modal-gpu تایید شده

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.

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// نصب مهارت

نصب مهارت

مهارت‌ها کدهای شخص ثالث از مخازن عمومی GitHub هستند. SkillHub الگوهای مخرب شناخته‌شده را اسکن می‌کند اما نمی‌تواند امنیت را تضمین کند. قبل از نصب، کد منبع را بررسی کنید.

نصب سراسری (سطح کاربر):

npx skillhub install benchflow-ai/SkillsBench/modal-gpu

نصب در پروژه فعلی:

npx skillhub install benchflow-ai/SkillsBench/modal-gpu --project

skill.install.customTargetHelp

npx skillhub install benchflow-ai/SkillsBench/modal-gpu --target-dir /path/to/skills

مسیر پیشنهادی: ~/.claude/skills/modal-gpu/

بررسی هوش مصنوعی

76
از ۱۰۰
کیفیت دستورالعمل85
دقت توضیحات65
کاربردی بودن72
صحت فنی82

Scored 76 thanks to comprehensive reference files covering troubleshooting, GPU selection, and data handling. Strong progressive disclosure pattern with clear decision tables. Minor deductions for lack of negative triggers in description and framework-locked generality.

productionmoderateml-engineersdata-scientistsai-researchersgpu-trainingserverless-mlmodel-fine-tuningcloud-compute
بررسی‌شده توسط review-skill-gateway(z-ai/glm-5.2) در تاریخ ۱۴۰۵/۵/۱۱

محتوای SKILL.md

---
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

| 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

مجوز

مجوز اعلام‌شده: Apache-2.0

متن کامل مجوز در مخزن منبع در دسترس است.

مشاهدهٔ مجوز در مخزن منبعنسخهٔ منتشرشده در آن‌جا مرجع است.