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README.md
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@ -4,31 +4,6 @@ Seed for ML research
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## Usage
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To use lightning, define a model that implements these 10 functions:
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#### Model definition
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| Name | Description | Input | Return |
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|---|---|---|---|
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| training_step | Called with a batch of data during training | data from your dataloaders | tuple: scalar, dict |
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| validation_step | Called with a batch of data during validation | data from your dataloaders | tuple: scalar, dict |
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| validation_end | Collate metrics from all validation steps | outputs: array where each item is the output of a validation step | dict: for logging |
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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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#### Model training
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| Name | Description | Input | Return |
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|---|---|---|---|
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| configure_optimizers | called during training setup | None | list: optimizers you want to use |
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| tng_dataloader | called during training | None | pytorch dataloader |
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| val_dataloader | called during validation | None | pytorch dataloader |
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| test_dataloader | called during testing | None | pytorch dataloader |
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| add_model_specific_args | called with args you defined in your main. This lets you tailor args for each model and keep main the same | argparse | argparse |
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#### Model Saving/Loading
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| Name | Description | Input | Return |
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|---|---|---|---|
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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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## Example
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```python
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import torch.nn as nn
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@ -102,6 +77,31 @@ class ExampleModel(RootModule):
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parser.add_argument('--out_features', default=20)
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return parser
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```
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### Details
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#### Model definition
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| Name | Description | Input | Return |
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|---|---|---|---|
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| training_step | Called with a batch of data during training | data from your dataloaders | tuple: scalar, dict |
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| validation_step | Called with a batch of data during validation | data from your dataloaders | tuple: scalar, dict |
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| validation_end | Collate metrics from all validation steps | outputs: array where each item is the output of a validation step | dict: for logging |
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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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#### Model training
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| Name | Description | Input | Return |
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|---|---|---|---|
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| configure_optimizers | called during training setup | None | list: optimizers you want to use |
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| tng_dataloader | called during training | None | pytorch dataloader |
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| val_dataloader | called during validation | None | pytorch dataloader |
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| test_dataloader | called during testing | None | pytorch dataloader |
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| add_model_specific_args | called with args you defined in your main. This lets you tailor args for each model and keep main the same | argparse | argparse |
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#### Model Saving/Loading
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| Name | Description | Input | Return |
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|---|---|---|---|
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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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### Add new model
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1. Create a new model under /models.
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