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.
npxskills add zechenzhangagi/ai-research-skills--skill modal-serverless-gpuLoading…
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 |
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).