# Returning Results from Modal

## Basic Return Values

Modal functions can return Python objects (dicts, lists, etc.):

```python
@app.function(gpu="A100", image=image, timeout=3600)
def train():
    # ... training code ...

    return {
        "final_loss": float(metrics["final_val_loss"]),
        "grad_norm_std": float(metrics["grad_norm_std"]),
        "max_grad_norm": float(metrics["max_grad_norm"]),
        "training_steps": int(metrics["steps"]),
    }

@app.local_entrypoint()
def main():
    results = train.remote()
    print(f"Results: {results}")

    # Save locally
    import json
    with open("results.json", "w") as f:
        json.dump(results, f, indent=2)
```

## Supported Return Types

| Type | Support | Notes |
|------|---------|-------|
| dict | Yes | Most common for metrics |
| list | Yes | For sequences of results |
| tuple | Yes | For multiple return values |
| str, int, float | Yes | Primitive types |
| numpy array | Limited | Convert to list first |
| torch tensor | No | Convert to Python types |

## Converting Tensors

Always convert tensors to Python types before returning:

```python
@app.function(gpu="A100", image=image, timeout=3600)
def train():
    import torch

    # ... training ...

    # WRONG - can't return tensors
    # return {"loss": loss_tensor}

    # CORRECT - convert to float
    return {
        "loss": float(loss_tensor.item()),
        "losses": [float(l) for l in loss_history],
    }
```

## Complete Example

```python
import modal

app = modal.App("model-training")

image = modal.Image.debian_slim(python_version="3.11").pip_install(
    "torch",
    "einops",
    "numpy",
    "huggingface_hub",
)

@app.function(gpu="A100", image=image, timeout=3600)
def train_model():
    # ... training code ...

    return {
        "final_loss": metrics["final_val_loss"],
        "training_steps": total_steps,
        "gpu": torch.cuda.get_device_name(0),
    }

@app.local_entrypoint()
def main():
    results = train_model.remote()
    print(f"\nResults: {results}")

    import json
    with open("results.json", "w") as f:
        json.dump(results, f, indent=2)
```

## Handling Failures

If a function fails, no result is returned. Use try/except to handle errors:

```python
@app.function(gpu="A100", image=image, timeout=3600)
def train():
    try:
        # ... training code ...
        return {"status": "success", "loss": final_loss}
    except Exception as e:
        return {"status": "error", "message": str(e)}
```