Distributed Training in Large Deep Learning models with PyTorch Model Parallelism on a ResNet50
Model parallelism is a technique utilized for training large deep neural networks (DNNs) that enables scaling beyond the memory capacity of a single GPU. By dividing the model into smaller sub-models and assigning each to a different GPU for training, model parallelism enables the training of very large models that would not fit in the memory of a single GPU, as well as fine-grained parallelism for optimal utilization of available hardware. This technique is commonly used in various deep learning applications, such as computer vision, natural language processing, and reinforcement learning. However, it is worth noting that while model parallelism has disadvantages, it may not always be the optimal solution for all problems, as it can be more complex to implement than data parallelism and may result in higher communication overhead.
To Achieve this goal different approaches can be used including:
- Layer-wise parallelism with Pytorch: The goal is to split the model into multiple sub-networks using the nn.Sequential(layers).to(device) snippet, each running on its own device, and then to orchestrate the communication between devices to perform the forward pass.
- Tensor parallelism with SageMaker: A library that splits individual layers…
