70 lines
2.3 KiB
ReStructuredText
70 lines
2.3 KiB
ReStructuredText
|
:orphan:
|
||
|
|
|
||
|
|
########################################
|
||
|
|
Deploy models into production (advanced)
|
||
|
|
########################################
|
||
|
|
**Audience**: Machine learning engineers optimizing models for enterprise-scale production environments.
|
||
|
|
|
||
|
|
----
|
||
|
|
|
||
|
|
*********************************
|
||
|
|
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 :meth:`~pytorch_lightning.core.lightning.LightningModule.to_torchscript` that returns a scripted module which you
|
||
|
|
can save or directly use.
|
||
|
|
|
||
|
|
.. testcode:: 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:
|
||
|
|
|
||
|
|
.. code-block:: 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 :func:`torch.jit.export`:
|
||
|
|
|
||
|
|
.. code-block:: python
|
||
|
|
|
||
|
|
class LitMCdropoutModel(pl.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")
|