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@ -2,33 +2,8 @@
Seed for ML research
## Usage
To use lightning, define a model that implements these 10 functions:
To use lightning, define a model that implements these 10 functions:
#### Model definition
| Name | Description | Input | Return |
|---|---|---|---|
| training_step | Called with a batch of data during training | data from your dataloaders | tuple: scalar, dict |
| validation_step | Called with a batch of data during validation | data from your dataloaders | tuple: scalar, dict |
| validation_end | Collate metrics from all validation steps | outputs: array where each item is the output of a validation step | dict: for logging |
| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
#### Model training
| Name | Description | Input | Return |
|---|---|---|---|
| configure_optimizers | called during training setup | None | list: optimizers you want to use |
| tng_dataloader | called during training | None | pytorch dataloader |
| val_dataloader | called during validation | None | pytorch dataloader |
| test_dataloader | called during testing | None | pytorch dataloader |
| 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 |
#### Model Saving/Loading
| Name | Description | Input | Return |
|---|---|---|---|
| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
| load_model_specific | called when loading a model | checkpoint: dict you created in get_save_dict | dict: modified in whatever way you want |
## Example
```python
import torch.nn as nn
@ -102,7 +77,32 @@ class ExampleModel(RootModule):
parser.add_argument('--out_features', default=20)
return parser
```
### Details
#### Model definition
| Name | Description | Input | Return |
|---|---|---|---|
| training_step | Called with a batch of data during training | data from your dataloaders | tuple: scalar, dict |
| validation_step | Called with a batch of data during validation | data from your dataloaders | tuple: scalar, dict |
| validation_end | Collate metrics from all validation steps | outputs: array where each item is the output of a validation step | dict: for logging |
| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
#### Model training
| Name | Description | Input | Return |
|---|---|---|---|
| configure_optimizers | called during training setup | None | list: optimizers you want to use |
| tng_dataloader | called during training | None | pytorch dataloader |
| val_dataloader | called during validation | None | pytorch dataloader |
| test_dataloader | called during testing | None | pytorch dataloader |
| 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 |
#### Model Saving/Loading
| Name | Description | Input | Return |
|---|---|---|---|
| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
| load_model_specific | called when loading a model | checkpoint: dict you created in get_save_dict | dict: modified in whatever way you want |
### Add new model
1. Create a new model under /models.
2. Add model name to trainer_main