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docs/source/production_inference.rst

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+Inference in Production
+=======================
+PyTorch Lightning eases the process of deploying models into production.
+
+
+Exporting to ONNX
+-----------------
+PyTorch Lightning provides a handy function to quickly export your model to ONNX format, which allows the model to be independent of PyTorch and run on an ONNX Runtime.
+
+To export your model to ONNX format call the `to_onnx` function on your Lightning Module with the filepath and input_sample.
+
+.. code-block:: python
+    filepath = 'model.onnx'
+    model = SimpleModel()
+    input_sample = torch.randn((1, 64))
+    model.to_onnx(filepath, input_sample, export_params=True)
+
+You can also skip passing the input sample if the `example_input_array` property is specified in your LightningModule.
+
+Once you have the exported model, you can run it on your ONNX runtime in the following way:
+
+.. code-block:: python
+    ort_session = onnxruntime.InferenceSession(filepath)
+    input_name = ort_session.get_inputs()[0].name
+    ort_inputs = {input_name: np.random.randn(1, 64).astype(np.float32)}
+    ort_outs = ort_session.run(None, ort_inputs)