Bringing PyTorch Monarch to AMD GPUs: Single-Controller Distributed Training on ROCm – PyTorch
- Featured projects Training state-of-the-art large language models (LLMs) with billions of parameters requires distributed training across hundreds or thousands of GPUs.
- At this scale, hardware failures are not exceptional events—they are expected.
- A single GPU memory error, network partition, or node crash can bring down an entire training run that has been progressing for days or weeks.
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- Featured projects Training state-of-the-art large language models (LLMs) with billions of parameters requires distributed training across hundreds or thousands of GPUs.
- At this scale, hardware failures are not exceptional events—they are expected.
- A single GPU memory error, network partition, or node crash can bring down an entire training run that has been progressing for days or weeks.
Sources: Pytorch