Advantages of using PyTorch’s built-in DataParallel for multi-GPU training:
1. Easy to use: PyTorch’s DataParallel module seamlessly replicates the model across multiple GPUs without requiring explicit communication between them. This makes it relatively easy to use and deploy on a multi-GPU system.
2. Automatic load-balancing: The DataParallel module evenly distributes the mini-batches of data across the available GPUs. It also ensures that each GPU receives an equal number of iterations during training, which helps to avoid imbalances in the training process.
3. Improved model performance: Training a deep learning model on multiple GPUs can greatly reduce the training time. The reasoning behind this is that each GPU can process a large amount of data simultaneously, making it easier for the model to converge to an optimal solution.
Disadvantages of using PyTorch’s built-in DataParallel for multi-GPU training:
1. Memory limitations: One major problem of DataParallel is that it replicates the model on each device, which requires a large amount of memory. This means that the number of GPUs that can be used in DataParallel is limited by the amount of memory available on the machine.
2. Communication overhead: Although DataParallel handles communication between replicas seamlessly, there can still be a bit of communication overhead between or across GPUs. This can affect training speed, especially if the model is computationally heavy.
3. Limited flexibility: DataParallel is highly optimized, but it is not very flexible when it comes to defining advanced training loops that require more complex control. This can be limiting in some cases, especially if you are working on complex models that require fine-grained control over the training process.
Note: These advantages and disadvantages can change based on various factors like size of the model, complexity of parallelization, type of data etc.