---
title: Deploy models into production (advanced)
description: >-
  Audience: Machine learning engineers optimizing models for enterprise-scale
  production environments.
canonical: >-
  https://lightning.ai/docs/pytorch/stable/reference/deploy/production_advanced_2
---
# Deploy models into production (advanced) {#deploy-models-into-production-advanced}

**Audience**: Machine learning engineers optimizing models for enterprise-scale production environments.

## Compile your model to TorchScript {#compile-your-model-to-torchscript}

[TorchScript](https://pytorch.org/docs/stable/jit.html) allows you to serialize your models in a way that it can be loaded in non-Python environments.
The `LightningModule` has a handy method `to_torchscript` that returns a scripted module which you
can save or directly use.

```python
class SimpleModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.l1 = torch.nn.Linear(in_features=64, out_features=4)

    def forward(self, x):
        return torch.relu(self.l1(x.view(x.size(0), -1)))

# create the model
model = SimpleModel()
script = model.to_torchscript()

# save for use in production environment
torch.jit.save(script, "model.pt")
```

It is recommended that you install the latest supported version of PyTorch to use this feature without limitations.

Once you have the exported model, you can run it in PyTorch or C++ runtime:

```python
inp = torch.rand(1, 64)
scripted_module = torch.jit.load("model.pt")
output = scripted_module(inp)
```

If you want to script a different method, you can decorate the method with `torch.jit.export`:

```python
class LitMCdropoutModel(L.LightningModule):
    def __init__(self, model, mc_iteration):
        super().__init__()
        self.model = model
        self.dropout = nn.Dropout()
        self.mc_iteration = mc_iteration

    @torch.jit.export
    def predict_step(self, batch, batch_idx):
        # enable Monte Carlo Dropout
        self.dropout.train()

        # take average of `self.mc_iteration` iterations
        pred = [self.dropout(self.model(x)).unsqueeze(0) for _ in range(self.mc_iteration)]
        pred = torch.vstack(pred).mean(dim=0)
        return pred

model = LitMCdropoutModel(...)
script = model.to_torchscript(file_path="model.pt", method="script")
```

---

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- Parent: [Deploy](/docs/pytorch/2.5.5/reference/deploy.md)