Run Python code on cloud GPUs using Modal serverless platform. Use when you need A100/T4/A10G GPU access for training ML models. Covers Modal app setup, GPU selection, data downloading inside functions, and result handling.
Modal is a serverless platform for running Python code on cloud GPUs. It provides:
Two patterns:
@app.function decorator| Topic | Reference |
|---|---|
| Basic Structure | Getting Started |
| GPU Options | GPU Selection |
| Data Handling | Data Download |
| Results & Outputs | Results |
| Troubleshooting | Common Issues |
pip install modal
modal token set --token-id <id> --token-secret <secret>
import modal
app = modal.App("my-training-app")
image = modal.Image.debian_slim(python_version="3.11").pip_install(
"torch",
"einops",
"numpy",
)
@app.function(gpu="A100", image=image, timeout=3600)
def train():
import torch
device = torch.device("cuda")
print(f"Using GPU: {torch.cuda.get_device_name(0)}")
# Training code here
return {"loss": 0.5}
@app.local_entrypoint()
def main():
results = train.remote()
print(results)
import modal
from modal import Image, App
# Inside remote function
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
| Scenario | Approach |
|---|---|
| Quick GPU experiments | gpu="T4" (16GB, cheapest) |
| Medium training jobs | gpu="A10G" (24GB) |
| Large-scale training | gpu="A100" (40/80GB, fastest) |
| Long-running jobs | Set timeout=3600 or higher |
| Data from HuggingFace | Download inside function with hf_hub_download |
| Return metrics | Return dict from function |
# Run script
modal run train_modal.py
# Run in background
modal run --detach train_modal.py