How to Train Really Large Models on Many GPUs?
The article discusses techniques for training large neural networks using multiple GPUs. It provides an overview of the challenges involved in scaling up model size and data parallelism, including communication overhead and synchronization issues. The author outlines various strategies to mitigate these problems, such as expert choice routing, gradient accumulation, and model parallelism. These methods can help reduce the time it takes to train large models on multiple GPUs.
The article discusses techniques for training large neural networks using multiple GPUs. It provides an overview of the challenges involved in scaling up model size and data parallelism, including communication overhead and synchronization issues. The author outlines various strategies to mitigate these problems, such as expert choice routing, gradient accumulation, and model parallelism. These methods can help reduce the time it takes to train large models on multiple GPUs.
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Why it matters: This matters because training large neural networks is a crucial step in developing advanced AI applications, but it's also a significant bottleneck due to computational requirements. Engineers working with large models need to understand these techniques to optimize their training processes and make progress in areas like natural language processing and computer vision.
Source: https://lilianweng.github.io/posts/2021-09-25-train-large/
This article was originally published at: https://lilianweng.github.io/posts/2021-09-25-train-large/