In recent years, distributed training has evolved from a mere optimization strategy to a data-driven approach that adapts to available infrastructure, revolutionizing how artificial intelligence and machine learning (AI/ML) models are trained. This shift is driven by the increasing need to process vast amounts of data while minimizing latency in real-world applications.
Data Locality and Infrastructure Adaptation
The data-driven approach to distributed training involves adapting the model architecture to the available infrastructure, ensuring data locality and minimizing latency. This means that the model is designed to run efficiently on a variety of hardware configurations, from single machines to large-scale clusters. By doing so, the model can take advantage of the available computing resources, reducing the time and cost associated with training.
Real-World Applications
This adaptive approach to distributed training has far-reaching implications for various industri...
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