Understanding `last_epoch` in PyTorch Optimizer Schedulers for Resuming Training

• The last_epoch parameter is crucial for resuming training in PyTorch when you're using a learning rate scheduler.
• It allows the scheduler to pick up where it left off in the previous training session.

How it Works:

• PyTorch learning rate schedulers typically adjust the learning rate based on the number of epochs (or iterations) that have passed.
• The last_epoch parameter keeps track of this internal counter.
• By default, last_epoch is set to -1, indicating that the scheduler starts from the beginning (epoch 0).

Resuming Training:

• If you interrupt training and want to resume later, you can provide the last completed epoch's index as the last_epoch argument when creating the scheduler.
• This ensures that the scheduler continues the learning rate adjustments based on the progress made in the previous training run.

Example:

import torch.optim as optim
from torch.optim.lr_scheduler import StepLR

model = ...  # Your PyTorch model
optimizer = optim.SGD(model.parameters(), lr=0.1)  # Create optimizer

# Train for 3 epochs, then interrupt training
for epoch in range(3):
    # ... your training loop

# Resume training later
last_epoch = 2  # Assuming you completed 2 epochs before interruption
scheduler = StepLR(optimizer, step_size=10, gamma=0.1, last_epoch=last_epoch)

for epoch in range(last_epoch + 1, 10):  # Start from epoch 3 (last_epoch + 1)
    # ... your training loop with learning rate adjustments from scheduler

Important Note:

• While last_epoch is traditionally used for epochs, some schedulers might interpret it as the number of batches processed (depending on the scheduler's implementation). Refer to the specific scheduler documentation for clarification.

This scheduler reduces the learning rate by a factor of gamma every step_size epochs.

import torch.optim as optim
from torch.optim.lr_scheduler import StepLR

model = ...  # Your PyTorch model
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Train for 3 epochs, then interrupt training
for epoch in range(3):
    # ... your training loop

# Resume training later (assuming 2 epochs completed)
last_epoch = 2
scheduler = StepLR(optimizer, step_size=10, gamma=0.1, last_epoch=last_epoch)

for epoch in range(last_epoch + 1, 10):  # Start from epoch 3 (last_epoch + 1)
    # ... your training loop with learning rate adjustments by scheduler

ReduceLROnPlateau:

This scheduler reduces the learning rate when a monitored metric (e.g., validation loss) stops improving for a specific number of epochs (patience).

import torch.optim as optim
from torch.optim.lr_scheduler import ReduceLROnPlateau

model = ...  # Your PyTorch model
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Train for some epochs
for epoch in range(10):
    # ... your training loop
    # ... calculate and log validation loss

# Resume training later with validation loss history
last_epoch = 9
validation_losses = [0.2, 0.19, 0.185, 0.18, 0.182, 0.182, 0.183]  # Example history
scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=2, verbose=True, last_epoch=last_epoch)

for epoch in range(last_epoch + 1, 20):  # Start from epoch 10 (last_epoch + 1)
    # ... your training loop
    # ... calculate and log validation loss
    scheduler.step(validation_losses[epoch])  # Update scheduler with validation loss

LambdaLR:

This scheduler allows you to define a custom learning rate function. You can use last_epoch within the lambda function.

import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR

def lr_lambda(epoch):
    return 0.95 ** epoch  # Example custom learning rate function

model = ...  # Your PyTorch model
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Train for some epochs
for epoch in range(5):
    # ... your training loop

# Resume training later (assuming 3 epochs completed)
last_epoch = 3
scheduler = LambdaLR(optimizer, lr_lambda=lr_lambda, last_epoch=last_epoch)

for epoch in range(last_epoch + 1, 10):  # Start from epoch 4 (last_epoch + 1)
    # ... your training loop
    scheduler.step()  # Update scheduler with current epoch

• Save the entire model state, optimizer state (including learning rate and momentum), and training epoch information periodically during training.
• When resuming, load the checkpoint and continue training from the saved epoch.

Pros:

• More flexible, as it captures the complete training state.
• Can be used with any type of scheduler, even custom ones.

Cons:

• Requires additional code for checkpointing and loading.
• Can consume more storage space depending on the model size.

Early Stopping Callbacks:

• Utilize libraries like PyTorch Lightning or custom callbacks.
• These callbacks track training metrics and potentially save checkpoints based on predefined conditions.
• When resuming, load the saved checkpoint or manually adjust hyperparameters.

• Integrates with training pipelines for better control over stopping and resuming.
• Can be used for hyperparameter tuning based on past training behavior.

• Adds complexity to training code compared to last_epoch.
• Might still require manual intervention to adjust hyperparameters upon resuming.

TensorBoard Logging and Visualization:

• Log relevant training data (learning rate, losses, metrics) using TensorBoard.
• Visualize the training history to understand learning rate behavior and progress.
• When resuming, analyze the visualizations and potentially adjust the learning rate manually.

• Provides valuable insights into the training process.
• Can be helpful for debugging and understanding convergence issues.

• Requires manual analysis of visualizations, which can be time-consuming.
• May not offer perfect control over learning rate adjustments as in last_epoch.

Choosing the Best Method:

• If simplicity and ease of use are priorities, last_epoch is a great option for common schedulers.
• For more complex training pipelines or custom schedulers, checkpointing or early stopping callbacks offer greater control.
• Consider TensorBoard logging if visualization and insights are valuable for understanding your training process.