To generate these files, refer to this [onnxblock's README](https://github.com/microsoft/onnxruntime/blob/main/orttraining/orttraining/python/training/onnxblock/README.md)
Once the onnx models are generated, you can use the training APIs to run your training.
### Training Loop
```py
from onnxruntime.training.api import Module, Optimizer, CheckpointState
# Create Checkpoint State.
state = CheckpointState("checkpoint.ckpt")
# Create Module and Optimizer.
model = Module("training_model.onnx", state, "eval_model.onnx")
optimizer = Optimizer("optimizer.onnx", model)
# Data should be a list of numpy arrays.
forward_inputs = ...
# Set model in training mode and run a Train step.
model.train()
model(forward_inputs)
# Optimizer step
optimizer.step()
# Set Model in eval mode and run an Eval step.
model.eval()
loss = model(forward_inputs)
# Assuming that the loss is the first element of the output in our case.
print("Loss : ", loss[0])
# Saving checkpoint.
model.save_checkpoint("checkpoint_export.ckpt")
```
For more detailed information refer to [Module](https://github.com/microsoft/onnxruntime/blob/main/orttraining/orttraining/python/training/api/Module.py) and [Optimizer](https://github.com/microsoft/onnxruntime/blob/main/orttraining/orttraining/python/training/api/Optimizer.py).
