Update README.md
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README.md
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README.md
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@ -152,26 +152,9 @@ class ExampleModel(RootModule):
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| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
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| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
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| load_model_specific | called when loading a model | checkpoint: dict you created in get_save_dict | dict: modified in whatever way you want |
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| load_model_specific | called when loading a model | checkpoint: dict you created in get_save_dict | dict: modified in whatever way you want |
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## Optional model hooks.
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### Add new model
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Add these to the model whenever you want to configure training behavior.
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1. Create a new model under /models.
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2. Add model name to trainer_main
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```python
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AVAILABLE_MODELS = {
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'model_1': ExampleModel1
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}
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```
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### Model methods that can be implemented
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| Method | Purpose | Input | Output | Required |
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|---|---|---|---|---|
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| forward() | Forward pass | model_in tuple with your data | model_out tuple to be passed to loss | Y |
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| loss() | calculate model loss | model_out tuple from forward() | A scalar | Y |
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| check_performance() | run a full loop through val data to check for metrics | dataloader, nb_tests | metrics tuple to be tracked | Y |
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| tng_dataloader | Computed option, used to feed tng data | - | Pytorch DataLoader subclass | Y |
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| val_dataloader | Computed option, used to feed tng data | - | Pytorch DataLoader subclass | Y |
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| test_dataloader | Computed option, used to feed tng data | - | Pytorch DataLoader subclass | Y |
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### Model lifecycle hooks
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### Model lifecycle hooks
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Use these hooks to customize functionality
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Use these hooks to customize functionality
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