70 lines
2.3 KiB
ReStructuredText
70 lines
2.3 KiB
ReStructuredText
:orphan:
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########################################
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Deploy models into production (advanced)
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########################################
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**Audience**: Machine learning engineers optimizing models for enterprise-scale production environments.
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----
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*********************************
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Compile your model to TorchScript
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*********************************
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`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.
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The ``LightningModule`` has a handy method :meth:`~pytorch_lightning.core.module.LightningModule.to_torchscript` that returns a scripted module which you
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can save or directly use.
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.. testcode:: python
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class SimpleModel(LightningModule):
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def __init__(self):
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super().__init__()
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self.l1 = torch.nn.Linear(in_features=64, out_features=4)
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def forward(self, x):
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return torch.relu(self.l1(x.view(x.size(0), -1)))
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# create the model
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model = SimpleModel()
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script = model.to_torchscript()
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# save for use in production environment
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torch.jit.save(script, "model.pt")
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It is recommended that you install the latest supported version of PyTorch to use this feature without limitations.
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Once you have the exported model, you can run it in PyTorch or C++ runtime:
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.. code-block:: python
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inp = torch.rand(1, 64)
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scripted_module = torch.jit.load("model.pt")
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output = scripted_module(inp)
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If you want to script a different method, you can decorate the method with :func:`torch.jit.export`:
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.. code-block:: python
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class LitMCdropoutModel(pl.LightningModule):
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def __init__(self, model, mc_iteration):
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super().__init__()
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self.model = model
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self.dropout = nn.Dropout()
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self.mc_iteration = mc_iteration
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@torch.jit.export
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def predict_step(self, batch, batch_idx):
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# enable Monte Carlo Dropout
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self.dropout.train()
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# take average of `self.mc_iteration` iterations
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pred = [self.dropout(self.model(x)).unsqueeze(0) for _ in range(self.mc_iteration)]
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pred = torch.vstack(pred).mean(dim=0)
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return pred
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model = LitMCdropoutModel(...)
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script = model.to_torchscript(file_path="model.pt", method="script")
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