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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.
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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. 📦 Ships with claude-code-templates
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infrastructure-modal.SKILL.md
---
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]
---
# 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
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
- **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
```bash
pip install modal
modal setup # Opens browser for authentication
```
### Hello World with GPU
```python
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
```python
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
```python
# 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
```python
# 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
```python
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
```python
@app.function()
@modal.fastapi_endpoint(method="POST")
def predict(text: str) -> dict:
return {"result": model.predict(text)}
```
### Full ASGI app
```python
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
```python
@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
```bash
# Create secret
modal secret create huggingface HF_TOKEN=hf_xxx
```
```python
@app.function(secrets=[modal.Secret.from_name("huggingface")])
def download_model():
import os
token = os.environ["HF_TOKEN"]
```
## Scheduling
```python
@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
```python
@app.function(
container_idle_timeout=300, # Keep warm 5 min
allow_concurrent_inputs=10, # Handle concurrent requests
)
def inference():
pass
```
### Model loading best practices
```python
@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
```python
@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
```python
@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
```python
# 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](references/advanced-usage.md)** - Multi-GPU, distributed training, cost optimization
- **[Troubleshooting](references/troubleshooting.md)** - 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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