61 lines
2.1 KiB
ReStructuredText
61 lines
2.1 KiB
ReStructuredText
########################################
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Deploy models into production (advanced)
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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 ONNX
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**************************
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`ONNX <https://pytorch.org/docs/stable/onnx.html>`_ is a package developed by Microsoft to optimize inference. ONNX allows the model to be independent of PyTorch and run on any ONNX Runtime.
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To export your model to ONNX format call the :meth:`~pytorch_lightning.core.module.LightningModule.to_onnx` function on your :class:`~pytorch_lightning.core.module.LightningModule` with the ``filepath`` and ``input_sample``.
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.. code-block:: 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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filepath = "model.onnx"
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input_sample = torch.randn((1, 64))
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model.to_onnx(filepath, input_sample, export_params=True)
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You can also skip passing the input sample if the ``example_input_array`` property is specified in your :class:`~pytorch_lightning.core.module.LightningModule`.
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.. code-block:: 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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self.example_input_array = torch.randn(7, 64)
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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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filepath = "model.onnx"
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model.to_onnx(filepath, export_params=True)
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Once you have the exported model, you can run it on your ONNX runtime in the following way:
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.. code-block:: python
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import onnxruntime
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ort_session = onnxruntime.InferenceSession(filepath)
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input_name = ort_session.get_inputs()[0].name
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ort_inputs = {input_name: np.random.randn(1, 64)}
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ort_outs = ort_session.run(None, ort_inputs)
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