torch.utils.tensorboard

torch.utils.tensorboard is a module in PyTorch that provides integration with TensorBoard, a visualization tool for machine learning experiments. TensorBoard allows users to visualize and track various aspects of their training process, such as loss, accuracy, and gradients, in real-time.

The module provides a SummaryWriter class that allows users to log data to TensorBoard. Users can log scalar values, images, histograms, and more. The SummaryWriter class takes care of writing the log files to disk and updating the TensorBoard UI.

To use torch.utils.tensorboard, users must first launch TensorBoard in a separate terminal window. They can then create a SummaryWriter object and use its methods to log data to TensorBoard. For example, to log the loss value for each epoch of a training loop, users can write:

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter()
for epoch in range(num_epochs):
    # training code
    loss = train(model, dataloader, optimizer)
    writer.add_scalar('Loss/train', loss, epoch)

This will create a scalar plot in TensorBoard showing the training loss over time. Users can also log other types of data, such as images or histograms, using the appropriate methods of the SummaryWriter class.