You are training an XGBoost model on a large image dataset with Dask and multiple GPUs, but the training is slower than expected. What is the likely cause of the slow performance?

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Answer

The model architecture is not compatible with GPU acceleration.

Answer

Inefficient data partitioning could lead to an uneven distribution of work across GPUs, causing slowdowns.

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XGBoost does not support multi-GPU training.

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Using too many GPUs can slow down the training process.

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You are training an XGBoost model on a large image dataset with Dask and multipl...

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