Model Parallelism
Model parallelism is a distributed computing technique used in machine learning and deep learning to split a large neural network model across multiple devices (e.g., GPUs or TPUs) to overcome memory limitations and enable training of models that are too large to fit on a single device. It involves partitioning the model's layers, parameters, or operations so that different parts of the model are processed on different hardware units, with communication between devices to pass activations and gradients during training or inference. This approach is essential for scaling up model size and complexity in fields like natural language processing and computer vision.
Developers should learn and use model parallelism when training or deploying very large neural network models that exceed the memory capacity of a single GPU or TPU, such as transformer-based models with billions of parameters (e.g., GPT, BERT). It is crucial for enabling state-of-the-art research and applications in AI, allowing for more accurate and capable models by distributing computational load and memory usage across multiple devices. Use cases include training large language models, high-resolution image generation, and complex reinforcement learning agents where model size is a bottleneck.