Monitoring and optimizing the training of deep neural networks on multiple GPUs is crucial for efficient machine learning model development, particularly for complex tasks in computer vision and natural language processing. This article explores multi-GPU training using PyTorch Lightning, a popular framework that simplifies scaling models without boilerplate code, and discusses best practices for optimizing the training process. It covers distributed training techniques such as data parallelism, model parallelism, and sharded training, each with its advantages and challenges for handling large datasets and models. Additionally, strategies like mixed precision training, increasing batch size, and using PyTorch DataLoader are recommended to enhance performance and address memory constraints. The article highlights the importance of monitoring GPU usage during training, suggesting tools like neptune.ai for tracking resources and gaining insights into potential bottlenecks or underutilization, ultimately ensuring the efficient use of computational resources while training large-scale models.