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Answer: Training a single model on several data subsets at the same time
Data parallelism is a strategy in distributed machine learning where a single model is trained across multiple processors or machines by splitting the training dataset into several subsets. Each processor or machine processes its subset simultaneously, calculating gradients or updates for the model parameters based on its data. These updates are then combined (usually averaged) to update the model globally. This approach speeds up model training by utilizing multiple processors or machines to manage larger datasets more effectively than a single processor or machine could. Options A and C refer to scenarios more related to model parallelism or ensemble methods, involving multiple models, which is different from data parallelism's focus on a single model. Option D outlines a sequential training process on a single machine, which is contrary to the parallel processing nature of data parallelism. Therefore, option B is the correct answer as it precisely describes the core concept of data parallelism in distributed machine learning.
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In the realm of distributed machine learning, what does data parallelism entail? Select the single best answer.
A
Training multiple models one after another on different data subsets
B
Training a single model on several data subsets at the same time
C
Training multiple models at once on different data subsets
D
Training a single model on the whole dataset one step at a time
E
None of the above
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