What s your approach to scaling up model training across multiple GPUs or distributed environments

Question

Can you show me with python programming to scale up generative ai model across multiple GPUs or distributed environments?

Answer 1

You can scale up the generative AI model across multiple GPUs or distributed environments by referring to the code snippet below:

In this code, model training is scaled across multiple GPUs using Distributed Data parallel in PyTorch by:

Setup Function: Initialize a distributed process group and assign each GPU by rank to process for parallel training.

Train Function: 

• Calls are set up to configure the process group and set the GPU device.
• Wraps the model with DDP, which synchronizes gradient across processes during backpropagation.
• Runs a training loop where each process computes gradients and updates the model in sync with others.

In the main block, we retrieve the number of GPUs available(Word_size). We use torch.multiprocessing.spawn will launch multiple processes, each assigned to a GPU, to execute the train function.