Distributed training with Optimum for Intel Gaudi

As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude.

All the PyTorch examples and the GaudiTrainer script work out of the box with distributed training. There are two ways of launching them:

  1. Using the gaudi_spawn.py script:

  2. Using the DistributedRunner directly in code:

    from optimum.habana.distributed import DistributedRunner
    from optimum.utils import logging
    
    world_size=8 # Number of HPUs to use (1 or 8)
    
    # define distributed runner
    distributed_runner = DistributedRunner(
        command_list=["scripts/train.py --args1 --args2 ... --argsN"],
        world_size=world_size,
        use_mpi=True,
    )
    
    # start job
    ret_code = distributed_runner.run()

You can set the training argument --distribution_strategy fast_ddp for simpler and usually faster distributed training management. More information here.

To go further, we invite you to read our guides about:

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