1. [PyTorch Profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html) is the **built-in** profiler for PyTorch models and allows in-depth analysis of the model's performance and resource utilization. It provides detailed profiling information, such as CPU and GPU utilization, memory consumption, and execution time for each operation. The profiler helps pinpoint bottlenecks and optimize network performance and [combines well with TensorBoard](https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html). 1. [TensorBoard](https://pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html) is the default deep learning visualization tool coming out of [the TensorFlow ecosystem](https://www.tensorflow.org/tensorboard/get_started). TensorBoard lets you monitor training loss, evaluation metrics, visualize the model graph, and much more. It should be part of the default toolchain of any deep learning work, no matter the framework you use. The core idea is to [use the SummaryWriter](https://github.com/christianversloot/machine-learning-articles/blob/main/how-to-use-tensorboard-with-pytorch.md) to write metrics or even the graph for TensorBoard to display. 2. **[PyTorch Lightning](https://github.com/Lightning-AI/lightning)** is a high-level PyTorch wrapper that structures your PyTorch code so it can [abstract the details of training](https://lightning.ai/docs/pytorch/stable/common/trainer.html). Lightning offers built-in [debugging capabilities](https://lightning.ai/docs/pytorch/stable/debug/debugging_basic.html), making it easier to identify and fix problems during training. It provides features like [a rich metrics library](https://lightning.ai/docs/torchmetrics/stable/), automatic checkpointing, distributed training, and easy integration with other PyTorch libraries. It can easily be [combined with TensorBoard](https://vordeck.de/kn/pytorch-lightning-tensorboard). This makes AI research scalable and fast to iterate on. And [Lighting Fabric](https://lightning.ai/docs/fabric/stable/) makes it easy to move across different hardware and take your model development into production. - [PyTorch Ignite](https://github.com/pytorch/ignite) is an alternative to Lightning, but much less opinionated. It offers a range of utilities for training and evaluating neural networks by creating a training loop abstraction that promotes more modularity. It includes a powerful debugging tool called `Engine`, which enables fine-grained control over the training process. With the `Engine`, you can easily add custom debugging logic and monitor network behavior during training, and it, too, [interfaces well with TensorBoard](https://pytorch-ignite.ai/blog/introduction/). 4. [PyTorch Captum](https://github.com/pytorch/captum) is a model interpretability library that provides various techniques for understanding neural network behavior. It offers tools like [Integrated Gradients](https://www.tensorflow.org/tutorials/interpretability/integrated_gradients), [DeepLIFT](https://arxiv.org/abs/1704.02685), and feature ablation, which help identify the most influential features and understand the network's decision boundaries. Captum can be valuable for debugging and gaining insights into the inner workings of a neural network. - [PyTorch gradCAM](https://github.com/jacobgil/pytorch-grad-cam) is a computer vision-specific interpretability library that helps you visualize the areas of an input image that significantly influence the network's predictions. By highlighting the regions that contribute most to the output, it aids in understanding the network's decision-making process. This visualization technique is particularly useful for debugging and interpreting convolutional neural networks.