# Running Models on Spyre

This page explains how to run full PyTorch models on the Spyre device
using `torch.compile` and the Torch-Spyre backend.

## Using `torch.compile`

Torch-Spyre registers itself as an Inductor backend for the `spyre`
device. Any model compiled with `torch.compile` and targeting the
`spyre` device is automatically routed through the Torch-Spyre compiler.

```python
import torch

DEVICE = torch.device("spyre")

model = MyModel().to(DEVICE)
compiled_model = torch.compile(model)

x = torch.rand(1, 3, 224, 224, dtype=torch.float16).to(DEVICE)
output = compiled_model(x)
```

## Supported Operations

For the full list of supported operations, see
[Supported Operations](supported_operations.md).

To add support for a new operation, see
[Adding Operations](../compiler/adding_operations.md).

## Configuration

Work division (core parallelism) is controlled by the `SENCORES`
environment variable:

```bash
SENCORES=32 python my_script.py
```

Valid values: 1–32 (default: 32). See
[Work Division Planning](../compiler/work_division_planning.md) for details.

## Examples

Full working examples are in the
[examples/](https://github.com/torch-spyre/torch-spyre/tree/main/examples)
directory:

- `tensor_allocate.py` — tensor creation and allocation
- `softmax.py` — running softmax on Spyre

## Troubleshooting

> **TODO:** Document common errors and how to resolve them.