expand pytorch trainer documentation

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Yinon Polak 2023-03-09 12:45:46 +02:00
parent ba5de0cd00
commit 6f962362f2

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@ -27,6 +27,27 @@ class PyTorchModelTrainer:
):
"""
A class for training PyTorch models.
Implements the training loop logic, load/save methods.
fit method - training loop logic:
- Calculates the predicted output for the batch using the PyTorch model.
- Calculates the loss between the predicted and actual output using a loss function.
- Computes the gradients of the loss with respect to the model's parameters using
backpropagation.
- Updates the model's parameters using an optimizer.
save method:
called by DataDrawer
- Saving any nn.Module state_dict
- Saving model_meta_data, this dict should contain any additional data that the
user needs to store. e.g class_names for classification models.
load method:
currently DataDrawer is responsible for the actual loading.
when using continual_learning the DataDrawer will load the dict
(saved by self.save(path)). and this class will populate the necessary
state_dict of the self.model & self.optimizer and self.model_meta_data.
:param model: The PyTorch model to be trained.
:param optimizer: The optimizer to use for training.
@ -34,10 +55,11 @@ class PyTorchModelTrainer:
:param device: The device to use for training (e.g. 'cpu', 'cuda').
:param batch_size: The size of the batches to use during training.
:param max_iters: The number of training iterations to run.
iteration here refers to the number of times we call
self.optimizer.step() . used to calculate n_epochs.
iteration here refers to the number of times we call self.optimizer.step().
used to calculate n_epochs.
:param eval_iters: The number of iterations used to estimate the loss.
:param init_model: A dictionary containing the initial model parameters.
:param init_model: A dictionary containing the initial model/optimizer
state_dict and model_meta_data saved by self.save() method.
:param model_meta_data: Additional metadata about the model (optional).
"""
self.model = model