Menu
Serverless GPU cloud for ML jobs and model APIs.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
Query and edit a SiYuan knowledge base via its API.
Create, read, edit Excel .xlsx spreadsheets and CSVs.
Create, read, edit Excel .xlsx spreadsheets and CSVs.
Curate LLM training data: dedupe, filter, PII redaction.
Scrape sites with stealth browsing and Cloudflare bypass.
Clean training loops with built-in distributed support.
| name | modal-serverless-gpu |
| description | Serverless GPU cloud for ML jobs and model APIs. |
| version | 1.0.0 |
| author | Orchestra Research |
| license | MIT |
| dependencies | ["modal>=0.64.0"] |
| platforms | ["linux","macos","windows"] |
| metadata | {"hermes":{"tags":["Infrastructure","Serverless","GPU","Cloud","Deployment","Modal"]}} |
关于在Modal的无服务器GPU云平台上运行机器学习工作负载的完整指南。
以下情况适合使用Modal：
核心功能：
如需其他替代方案，请考虑：
pip install modal
modal setup # Opens browser for authentication
import modal
app = modal.App("hello-gpu")
@app.function(gpu="T4")
def gpu_info():
import subprocess
return subprocess.run(["nvidia-smi"], capture_output=True, text=True).stdout
@app.local_entrypoint()
def main():
print(gpu_info.remote())
运行命令：modal run hello_gpu.py
import modal
app = modal.App("text-generation")
image = modal.Image.debian_slim().pip_install("transformers", "torch", "accelerate")
@app.cls(gpu="A10G", image=image)
class TextGenerator:
@modal.enter()
def load_model(self):
from transformers import pipeline
self.pipe = pipeline("text-generation", model="gpt2", device=0)
@modal.method()
def generate(self, prompt: str) -> str:
return self.pipe(prompt, max_length=100)[0]["generated_text"]
@app.local_entrypoint()
def main():
print(TextGenerator().generate.remote("Hello, world"))
| 组件 | 功能 |
|---|---|
App | 函数与资源的容器 |
Function | 具有计算配置的无服务器函数 |
Cls | 带有生命周期钩子的基于类的函数 |
Image | 容器镜像定义 |
Volume | 模型/数据的持久化存储 |
Secret | 安全的凭据存储 |
| 命令 | 描述 |
|---|---|
modal run script.py | 执行后退出 |
modal serve script.py | 支持实时热重载的开发模式 |
modal deploy script.py | 持久化云部署 |
| GPU | VRAM容量 | 最佳适用场景 |
|---|---|---|
T4 | 16GB | 预算型推理任务及小型模型 |
L4 | 24GB | 推理任务，采用Ada Lovelace架构 |
A10G | 24GB | 训练/推理任务，性能是T4的3.3倍 |
L40S | 48GB | 推理任务的推荐选择（性价比最高） |
A100-40GB | 40GB | 大型模型训练 |
A100-80GB | 80GB | 超大型模型 |
H100 | 80GB | 性能最佳，支持FP8格式及Transformer引擎 |
H200 | 141GB | 可由H100自动升级，带宽达4.8TB/s |
B200 | 最新款 | 采用Blackwell架构 |
# Single GPU
@app.function(gpu="A100")
# Specific memory variant
@app.function(gpu="A100-80GB")
# Multiple GPUs (up to 8)
@app.function(gpu="H100:4")
# GPU with fallbacks
@app.function(gpu=["H100", "A100", "L40S"])
# Any available GPU
@app.function(gpu="any")
# Basic image with pip
image = modal.Image.debian_slim(python_version="3.11").pip_install(
"torch==2.1.0", "transformers==4.36.0", "accelerate"
)
# From CUDA base
image = modal.Image.from_registry(
"nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04",
add_python="3.11"
).pip_install("torch", "transformers")
# With system packages
image = modal.Image.debian_slim().apt_install("git", "ffmpeg").pip_install("whisper")
volume = modal.Volume.from_name("model-cache", create_if_missing=True)
@app.function(gpu="A10G", volumes={"/models": volume})
def load_model():
import os
model_path = "/models/llama-7b"
if not os.path.exists(model_path):
model = download_model()
model.save_pretrained(model_path)
volume.commit() # Persist changes
return load_from_path(model_path)
@app.function()
@modal.fastapi_endpoint(method="POST")
def predict(text: str) -> dict:
return {"result": model.predict(text)}
from fastapi import FastAPI
web_app = FastAPI()
@web_app.post("/predict")
async def predict(text: str):
return {"result": await model.predict.remote.aio(text)}
@app.function()
@modal.asgi_app()
def fastapi_app():
return web_app
| 装饰器 | 使用场景 |
|---|---|
@modal.fastapi_endpoint() | 简单函数 → API |
@modal.asgi_app() | 完整的FastAPI/Starlette应用 |
@modal.wsgi_app() | Django/Flask应用 |
@modal.web_server(port) | 任意HTTP服务器 |
@app.function()
@modal.batched(max_batch_size=32, wait_ms=100)
async def batch_predict(inputs: list[str]) -> list[dict]:
# Inputs automatically batched
return model.batch_predict(inputs)
# Create secret
modal secret create huggingface HF_TOKEN=hf_xxx
@app.function(secrets=[modal.Secret.from_name("huggingface")])
def download_model():
import os
token = os.environ["HF_TOKEN"]
@app.function(schedule=modal.Cron("0 0 * * *")) # Daily midnight
def daily_job():
pass
@app.function(schedule=modal.Period(hours=1))
def hourly_job():
pass
@app.function(
container_idle_timeout=300, # Keep warm 5 min
allow_concurrent_inputs=10, # Handle concurrent requests
)
def inference():
pass
@app.cls(gpu="A100")
class Model:
@modal.enter() # Run once at container start
def load(self):
self.model = load_model() # Load during warm-up
@modal.method()
def predict(self, x):
return self.model(x)
@app.function()
def process_item(item):
return expensive_computation(item)
@app.function()
def run_parallel():
items = list(range(1000))
# Fan out to parallel containers
results = list(process_item.map(items))
return results
@app.function(
gpu="A100",
memory=32768, # 32GB RAM
cpu=4, # 4 CPU cores
timeout=3600, # 1 hour max
container_idle_timeout=120,# Keep warm 2 min
retries=3, # Retry on failure
concurrency_limit=10, # Max concurrent containers
)
def my_function():
pass
# Test locally
if __name__ == "__main__":
result = my_function.local()
# View logs
# modal app logs my-app
| 问题 | 解决方案 |
|---|---|
| 冷启动延迟 | 增大 container_idle_timeout 的值，使用 @modal.enter() 方法 |
| GPU 内存不足 | 使用容量更大的 GPU（如 A100-80GB），并启用梯度检查点机制 |
| 图像构建失败 | 固定依赖项的版本，检查 CUDA 兼容性 |
| 超时错误 | 增大 timeout 参数值，加入检查点功能 |