Ensuring Smooth Resumption in PyTorch Training: Focus on the Data, Not the DataLoader

• DataLoader state: It holds information about the current iteration and other internal variables, not relevant for restarting training.
• Focus on data: The core aspect for training is the underlying dataset, not the loader's state.

Instead, there are better approaches to ensure you can resume training effectively:

import torch

# Custom Dataset class (replace with your data loading logic)
class MyDataset(torch.utils.data.Dataset):
  def __init__(self, data_path):
    # Load your data from the specified path
    self.data = ...

  def __len__(self):
    return len(self.data)

  def __getitem__(self, idx):
    return self.data[idx]

# Load your data
data_path = "path/to/your/data"
dataset = MyDataset(data_path)

# Save the dataset (modify based on your data format)
# For example, with pickle
import pickle
with open("saved_dataset.pkl", "wb") as f:
  pickle.dump(dataset, f)

# When resuming training:
# 1. Load the dataset
with open("saved_dataset.pkl", "rb") as f:
  loaded_dataset = pickle.load(f)

# 2. Recreate the DataLoader with the same parameters (adjust as needed)
dataloader = torch.utils.data.DataLoader(loaded_dataset, batch_size=32, shuffle=True)

# Now you can use the dataloader to iterate over your data for training
for data in dataloader:
  # Your training logic here
  ...

This is a basic example. You'll need to adapt the MyDataset class to handle your specific data loading process and saving method (e.g., pickle, HDF5).

This approach involves creating a custom sampler class that inherits from torch.utils.data.Sampler and manages the logic of iterating over your dataset. The sampler can track its state (e.g., last sampled index) and save/load it during training.

Here's a breakdown:

This approach allows you to potentially resume training from the exact point where it was stopped. Here are some resources to get you started (avoiding URLs):

• Stack Overflow discussion on a resumable sampler implementation: [Stack Overflow - Save PyTorch DataLoader state]

Important Note:

• This method might add complexity compared to saving the dataset. Evaluate if the benefit of resuming from the exact point outweighs the additional code overhead.