What s the code to use distributed training with Horovod for scalable image generation on large datasets

Question

Give me the code to use distributed training with Horovod for scalable image generation on large datasets?

Answer 1

To use distributed training with Horovod for scalable image generation on large datasets, you need to integrate Horovod with your PyTorch model. Horovod enables data parallelism across multiple GPUs/machines, improving scalability and training speed.

Here are the steps you can follow:

• Install Horovod
• Distributed Training Code

Here is the code you can refer to:

In the code, we are using the following:

• Horovod Initialization:

  • hvd.init() initializes the Horovod environment.
  • Set the device for each rank using torch.cuda.set_device(hvd.local_rank()).

• Distributed Optimizer:

  • hvd.DistributedOptimizer is used to ensure synchronization of gradients across multiple workers.

• Broadcasting Model:

  • hvd.broadcast_parameters ensures that the model parameters are synchronized across all workers.

• Gradient Averaging:

  • hvd.allreduce averages gradients across all workers to synchronize updates.

• Scalability:

  • Horovod enables you to scale the training to multiple GPUs across different nodes for large datasets and high-performance training.

Hence referring to the above will help you in using distributed training with Horovod for scalable image generation on large datasets