What is the difference between data and model parallelism?

Can you explain the difference between data parallelism and model parallelism?

Sure! Data parallelism involves splitting the dataset across multiple processors, while model parallelism splits the model itself.

How does data parallelism improve training speed?

Data parallelism allows multiple processors to work on different batches of data simultaneously, speeding up the training process.

When should I use model parallelism instead?

Model parallelism is useful when the model is too large to fit into the memory of a single processor, allowing for distributed computation.

Can you give an example of a scenario for each type?

Certainly! For data parallelism, think of training a neural network on a large dataset across multiple GPUs. For model parallelism, consider a deep learning model with many layers that needs to be split across different devices.