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Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/endman100/skill-router-nexus --skill modal-serverless-gpu命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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| name | modal-serverless-gpu |
| description | Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling. |
| version | 1.0.0 |
| author | Orchestra Research |
| license | MIT |
| tags | ["Infrastructure","Serverless","GPU","Cloud","Deployment","Modal"] |
| dependencies | ["modal>=0.64.0"] |
Comprehensive guide to running ML workloads on Modal's serverless GPU cloud platform.
Use Modal when:
Key features:
Use alternatives instead:
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())
Run: 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"))
| Component | Purpose |
|---|---|
App | Container for functions and resources |
Function | Serverless function with compute specs |
Cls | Class-based functions with lifecycle hooks |
Image | Container image definition |
Volume | Persistent storage for models/data |
Secret | Secure credential storage |
| Command | Description |
|---|---|
modal run script.py | Execute and exit |
modal serve script.py | Development with live reload |
modal deploy script.py | Persistent cloud deployment |
| GPU | VRAM | Best For |
|---|---|---|
T4 | 16GB | Budget inference, small models |
L4 | 24GB | Inference, Ada Lovelace arch |
A10G | 24GB | Training/inference, 3.3x faster than T4 |
L40S | 48GB | Recommended for inference (best cost/perf) |
A100-40GB | 40GB | Large model training |
A100-80GB | 80GB | Very large models |
H100 | 80GB | Fastest, FP8 + Transformer Engine |
H200 | 141GB | Auto-upgrade from H100, 4.8TB/s bandwidth |
B200 | Latest | Blackwell architecture |
# 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
| Decorator | Use Case |
|---|---|
@modal.fastapi_endpoint() | Simple function → API |
@modal.asgi_app() | Full FastAPI/Starlette apps |
@modal.wsgi_app() | Django/Flask apps |
@modal.web_server(port) | Arbitrary HTTP servers |
@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
| Issue | Solution |
|---|---|
| Cold start latency | Increase container_idle_timeout, use @modal.enter() |
| GPU OOM | Use larger GPU (A100-80GB), enable gradient checkpointing |
| Image build fails | Pin dependency versions, check CUDA compatibility |
| Timeout errors | Increase timeout, add checkpointing |