Multi-GPU Training
Multi-GPU training is a distributed computing technique that leverages multiple graphics processing units (GPUs) to accelerate the training of machine learning models, particularly deep neural networks. It involves splitting the computational workload—such as data batches or model layers—across several GPUs to reduce training time and handle larger models or datasets. This approach is essential for scaling up training processes in high-performance computing environments.
Developers should use multi-GPU training when working with large-scale deep learning models, such as those in natural language processing (e.g., transformers) or computer vision (e.g., convolutional networks), where single-GPU training is too slow or memory-constrained. It is crucial for reducing training times from days to hours, enabling faster experimentation and deployment in research and production settings, such as in autonomous vehicles or recommendation systems.