What is distributed training in neural networks?

Distributed training in neural networks is a method to accelerate model training by splitting the workload across multiple devices, such as GPUs or machines. Instead of relying on a single device to handle all computations, distributed training divides tasks like data processing, gradient calculation, or model parameter updates across multiple workers. This approach reduces training time, especially for large models or datasets, by leveraging parallelism. The two primary strategies are data parallelism (splitting data batches across devices) and model parallelism (splitting the model itself across devices). For example, training a vision model on 8 GPUs with data parallelism could process 8 batches of images simultaneously, then synchronize gradients to update the model.

A common implementation is data parallelism, where each device holds a copy of the entire model. During training, each device processes a different subset of the data, computes gradients, and shares them with other devices to update the model parameters collectively. Frameworks like PyTorch’s DistributedDataParallel or TensorFlow’s tf.distribute.MirroredStrategy automate this process. Model parallelism, on the other hand, is used when the model is too large to fit on one device. For instance, large language models like GPT-3 split layers across multiple GPUs, with each device computing a portion of the forward and backward passes. This requires careful coordination to manage communication between devices.

While distributed training speeds up training, it introduces challenges. Synchronizing gradients or parameters across devices adds communication overhead, which can become a bottleneck if not optimized. Techniques like gradient averaging (for data parallelism) or pipeline parallelism (for model parallelism) help mitigate this. Developers must also handle hardware setup, such as configuring high-speed interconnects like NVLink for GPUs. Tools like Horovod or cloud-based solutions (e.g., AWS SageMaker) simplify deployment. However, debugging distributed systems can be complex due to race conditions or inconsistent device states. Balancing speed gains with these trade-offs is key to effective implementation.