DistributedSampler(train_dataset, shuffle=True)
时间: 2024-05-27 08:12:00 浏览: 16
The `DistributedSampler` is a PyTorch sampler that is designed to be used in distributed training settings. It is used to split the dataset across multiple processes in a distributed environment, ensuring that each process samples a disjoint subset of the data. This helps to reduce the likelihood of overfitting and to speed up training.
The `shuffle` parameter determines whether or not the data is shuffled before being split across the processes. If `shuffle=True`, the data will be randomly shuffled before being split. If `shuffle=False`, the data will be split into contiguous chunks without shuffling.
In summary, `DistributedSampler(train_dataset, shuffle=True)` creates a sampler that distributes the training dataset across multiple processes and shuffles it before distributing it.
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