Breaking the Myth: Heterogeneous Hardware in Distributed Training
For a long time, it was believed that distributed training required homogeneous hardware for optimal performance. However, this notion has been debunked by recent advancements in distributed training techniques. In reality, heterogeneous hardware can be beneficial, especially when using methods like model distillation.
The Benefits of Heterogeneous Hardware
Using a mix of hardware types can lead to improved efficiency and reduced costs. For example, a combination of fast GPUs for compute-intensive tasks and lower-cost TPUs or FPGAs for less demanding operations can be an ideal setup. Additionally, leveraging the strengths of specialized hardware like TPUs for neural network inference can further accelerate the training process.
Model Distillation: A Key Enabler
Model distillation is a technique that allows for efficient knowledge transfer between different models or hardware configurations. By distill...
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