huggingface data_error ai_generated true

RuntimeError: Dataset shuffling requires a deterministic seed for iterable datasets, but seed is None

ID: huggingface/dataset-shuffling-iterator-break

Also available as: JSON · Markdown · 中文
88%Fix Rate
82%Confidence
1Evidence
2023-11-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
datasets>=2.10.0 active — — —
torch>=1.13.0 active — — —

Root Cause

IterableDataset does not support random shuffling without a fixed seed; the dataset iterator cannot be deterministically replayed for shuffling.

generic

中文

IterableDataset 不支持在没有固定种子的情况下随机洗牌；数据集迭代器无法确定性重放以进行洗牌。

Official Documentation

https://huggingface.co/docs/datasets/v2.10.0/en/stream#shuffling

Workarounds

  1. 95% success Specify a seed when shuffling: dataset = dataset.shuffle(seed=42, buffer_size=1000). This ensures deterministic shuffle order for the streaming dataset. 
    Specify a seed when shuffling: dataset = dataset.shuffle(seed=42, buffer_size=1000). This ensures deterministic shuffle order for the streaming dataset.
  2. 80% success Disable shuffling for IterableDataset and shuffle externally: train_loader = DataLoader(dataset, shuffle=False); then manually shuffle indices before each epoch if using MapDataset. 
    Disable shuffling for IterableDataset and shuffle externally: train_loader = DataLoader(dataset, shuffle=False); then manually shuffle indices before each epoch if using MapDataset.

中文步骤

  1. 在洗牌时指定种子：dataset = dataset.shuffle(seed=42, buffer_size=1000)。这可确保流式数据集的确定性洗牌顺序。
  2. 禁用 IterableDataset 的洗牌并外部洗牌：train_loader = DataLoader(dataset, shuffle=False)；然后如果使用 MapDataset，在每个 epoch 前手动洗牌索引。

Dead Ends

Common approaches that don't work:

  1. Set `shuffle=True` on the DataLoader without fixing the seed 80% fail

    The DataLoader's shuffle is incompatible with IterableDataset; it raises an error or silently fails to shuffle.

  2. Convert the IterableDataset to a MapDataset by calling `.to_iterable_dataset()` 90% fail

    This method does not exist; conversion requires loading the entire dataset into memory, which defeats the purpose of streaming.

  3. Use `dataset.shuffle(buffer_size=1000)` without a seed 100% fail

    The shuffle method on IterableDataset requires a seed parameter; omitting it raises the same error.