Multiple loggers support visualizing the model topology. Here’s an example that tracks the model topology using Tensorboard.

Sep 24, 2024 · Using PyTorch Lightning and TensorBoard together has multiple benefits: Automated Logging : PyTorch Lightning automatically logs metrics, making it easier to monitor …

Aug 10, 2020 · There are two ways to generate beautiful and powerful TensorBoard plots in PyTorch Lightning. Let’s see both one by one. Lightning gives us the provision to return logs …

Sep 20, 2024 · To log metrics for TensorBoard, we can utilize the self.log() method provided by PyTorch Lightning. Metrics are logged at the end of each training step by default, but we can …

You will now see a “PR Curves” tab that contains the precision-recall curves for each class. Go ahead and poke around; you’ll see that on some classes the model has nearly 100% “area …

Apr 4, 2021 · You can use self.logger.log_hyperparams method to log hyperparameters and metrics in tensorboard. (see pytorch lightning tensorboard docs ) The values you added by …

Here’s an example that tracks the model topology using Tensorboard. def any_lightning_module_function_or_hook ( self ): tensorboard_logger = self . logger . …

To track a metric, simply use the self.log method available inside the LightningModule. To log multiple metrics at once, use self.log_dict. show plot of metric changing over time. To view …

May 2, 2025 · To effectively visualize model performance using TensorBoard in PyTorch Lightning, you can leverage the built-in TensorBoardLogger. This logger allows you to track …

Jul 25, 2024 · In this blog post, I will demonstrate an effective approach to using TensorBoard alongside Lightning to simplify logging and effortlessly visualize multiple metrics from different …