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

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Can you show me with python programming to scale up generative ai model across multiple GPUs or distributed environments?
Nov 8 in Generative AI by Ashutosh
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1 answer to this question.

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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.

answered Nov 8 by evanjilin

edited Nov 11 by Ashutosh

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