modal-serverless-gpu

SKILL.md

Modal Serverless GPU

Comprehensive guide to running ML workloads on Modal's serverless GPU cloud platform.

When to use Modal

Use Modal when:

Running GPU-intensive ML workloads without managing infrastructure

Deploying ML models as auto-scaling APIs

Running batch processing jobs (training, inference, data processing)

Need pay-per-second GPU pricing without idle costs

Prototyping ML applications quickly

Running scheduled jobs (cron-like workloads)

Key features:

Serverless GPUs: T4, L4, A10G, L40S, A100, H100, H200, B200 on-demand

Python-native: Define infrastructure in Python code, no YAML

Auto-scaling: Scale to zero, scale to 100+ GPUs instantly

Sub-second cold starts: Rust-based infrastructure for fast container launches

Container caching: Image layers cached for rapid iteration

Web endpoints: Deploy functions as REST APIs with zero-downtime updates

Use alternatives instead:

RunPod: For longer-running pods with persistent state

Lambda Labs: For reserved GPU instances

SkyPilot: For multi-cloud orchestration and cost optimization

Kubernetes: For complex multi-service architectures

Quick start

Installation

pip install modal
modal setup  # Opens browser for authentication

Hello World with GPU

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

Basic inference endpoint

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"))

Core concepts

Key components

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

Execution modes

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 configuration

Available GPUs

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

GPU specification patterns

# 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")

Container images

# 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")

Persistent storage

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)

Web endpoints

FastAPI endpoint decorator

@app.function()
@modal.fastapi_endpoint(method="POST")
def predict(text: str) -> dict:
    return {"result": model.predict(text)}

Full ASGI app

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

Web endpoint types

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

Dynamic batching

@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)

Secrets management

# 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"]

Scheduling

@app.function(schedule=modal.Cron("0 0 * * *"))  # Daily midnight
def daily_job():
    pass

@app.function(schedule=modal.Period(hours=1))
def hourly_job():
    pass

Performance optimization

Cold start mitigation

@app.function(
    container_idle_timeout=300,  # Keep warm 5 min
    allow_concurrent_inputs=10,  # Handle concurrent requests
)
def inference():
    pass

Model loading best practices

@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)

Parallel processing

@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

Common configuration

@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

Debugging

# Test locally
if __name__ == "__main__":
    result = my_function.local()

# View logs
# modal app logs my-app

Common issues

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

References

Advanced Usage - Multi-GPU, distributed training, cost optimization

Troubleshooting - Common issues and solutions

Resources

Documentation: https://modal.com/docs

Examples: https://github.com/modal-labs/modal-examples

Pricing: https://modal.com/pricing

Discord: https://discord.gg/modal

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