🤖 Deep learning has come a long way since AlexNet won the ImageNet competition in 2012, opening doors to revolutionary advancements in computer vision and natural language processing (NLP). With ever-growing model sizes, modern deep learning tasks demand enormous computational resources. 🚀
In this article, we’ll explore how to leverage PyTorch Distributed Training to scale your models efficiently. By the end, you’ll learn how to set up single-node training pipelines, implement DataParallel, transition to DistributedDataParallel (DDP), and optimize your cloud costs. Let’s dive in! 🌟
As the size of deep learning models continues to grow, training these models on a single GPU has become impractical. For instance:
- Models with billions of parameters require memory for weights, gradients, and batches.
- Training on multiple GPUs (or nodes) accelerates computations and improves resource utilization.
This brings us to distributed training, where workloads are distributed across multiple GPUs or machines. Companies like NVIDIA, AWS, and Google provide high-performance hardware for such tasks, but these can be costly. 💸 That’s why understanding cost-effective strategies is essential.
