# Install
pip install modal

# Authenticate
modal token new

import modal

app = modal.App("my-app")

@app.function()
def hello():
    return "Hello from Modal!"

# Run with: modal run script.py

# Build image with dependencies
image = (
    modal.Image.debian_slim(python_version="3.12")
    .pip_install("torch", "transformers", "numpy")
)

app = modal.App("ml-app", image=image)

@app.function(gpu="H100")
def train_model():
    import torch
    assert torch.cuda.is_available()
    # GPU code here

# Available GPUs: T4, L4, A10, A100, L40S, H100, H200, B200
# Multi-GPU: gpu="H100:8"

@app.function()
@modal.web_endpoint(method="POST")
def predict(data: dict):
    result = model.predict(data["input"])
    return {"prediction": result}

# Deploy: modal deploy script.py

@app.function(schedule=modal.Cron("0 2 * * *"))  # Daily at 2 AM
def daily_backup():
    pass

@app.function(schedule=modal.Period(hours=4))  # Every 4 hours
def refresh_cache():
    pass

@app.function()
def process_item(item_id: int):
    return analyze(item_id)

@app.local_entrypoint()
def main():
    items = range(1000)
    # Automatically parallelized across containers
    results = list(process_item.map(items))

volume = modal.Volume.from_name("my-data", create_if_missing=True)

@app.function(volumes={"/data": volume})
def save_results(data):
    with open("/data/results.txt", "w") as f:
        f.write(data)
    volume.commit()  # Persist changes

@app.function(secrets=[modal.Secret.from_name("huggingface")])
def download_model():
    import os
    token = os.environ["HF_TOKEN"]

@app.cls(gpu="L40S")
class Model:
    @modal.enter()
    def load_model(self):
        from transformers import pipeline
        self.pipe = pipeline("text-classification", device="cuda")

    @modal.method()
    def predict(self, text: str):
        return self.pipe(text)

@app.local_entrypoint()
def main():
    model = Model()
    result = model.predict.remote("Modal is great!")

@app.function(
    cpu=8.0,              # 8 CPU cores
    memory=32768,         # 32 GiB RAM
    ephemeral_disk=10240, # 10 GiB disk
    timeout=3600          # 1 hour timeout
)
def memory_intensive_task():
    pass