A data science team is developing a complex fraud detection model using a large-scale Long Short-Term Memory (LSTM) neural network. The project involves training the model on terabytes of sequential transaction data. A key business requirement is the ability to rapidly iterate and experiment with different model architectures using various popular deep learning frameworks. Which of the following infrastructure choices is the MOST appropriate for efficiently training this type of model?
A cluster of servers equipped with Tensor Processing Units (TPUs).
A single server with a high number of CPU cores and a large amount of RAM.
A distributed computing cluster running Apache Spark on CPU-only nodes.
A cluster of servers equipped with high-end Graphics Processing Units (GPUs).
The correct answer is a cluster of servers equipped with high-end Graphics Processing Units (GPUs). Training deep learning models, such as LSTMs, involves a massive number of parallel computations like matrix multiplications. GPUs are specifically designed for such parallel processing, containing thousands of cores that can handle these operations simultaneously, which drastically reduces training time compared to CPUs. While Tensor Processing Units (TPUs) are also designed for neural network training, they are ASICs most heavily optimized for Google's TensorFlow framework. The requirement to use 'various popular deep learning frameworks' makes GPUs a more flexible and broadly compatible choice. A high-CPU server would be inefficient as CPUs are designed for sequential, not parallel, tasks and would be a significant bottleneck. A standard Apache Spark cluster is ideal for large-scale data preprocessing (ETL) but not for the computationally intensive training of the neural network model itself, which is the primary bottleneck addressed by specialized hardware like GPUs.
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Why are GPUs preferred for training deep learning models like LSTMs?
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