# ValueError: batch_size must be None for IterableDataset, but got 32

- **ID:** `huggingface/dataset-iterable-batch-size`
- **Domain:** huggingface
- **Category:** type_error
- **Verification:** ai_generated
- **Fix Rate:** 90%

## Root Cause

When using an IterableDataset with the Trainer or DataLoader, a fixed batch_size is provided, but IterableDataset requires dynamic batching via `batch_size=None`.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| datasets>=2.10.0 | active | — | — |
| transformers>=4.28.0 | active | — | — |
| torch>=2.0.0 | active | — | — |

## Workarounds

1. **Set `batch_size=None` in the DataLoader or Trainer: `from transformers import Trainer; trainer = Trainer(model=model, args=training_args, train_dataset=iterable_dataset, data_collator=collator, batch_size=None)`** (95% success)
   ```
   Set `batch_size=None` in the DataLoader or Trainer: `from transformers import Trainer; trainer = Trainer(model=model, args=training_args, train_dataset=iterable_dataset, data_collator=collator, batch_size=None)`
   ```
2. **Use `DataLoader` with `batch_size=None` and `batch_sampler` if needed: `from torch.utils.data import DataLoader; dl = DataLoader(iterable_dataset, batch_size=None, collate_fn=collator)`** (90% success)
   ```
   Use `DataLoader` with `batch_size=None` and `batch_sampler` if needed: `from torch.utils.data import DataLoader; dl = DataLoader(iterable_dataset, batch_size=None, collate_fn=collator)`
   ```
3. **If using Trainer, override `get_train_dataloader` to handle batching: `class CustomTrainer(Trainer): def get_train_dataloader(self): return DataLoader(self.train_dataset, batch_size=None, collate_fn=self.data_collator)`** (85% success)
   ```
   If using Trainer, override `get_train_dataloader` to handle batching: `class CustomTrainer(Trainer): def get_train_dataloader(self): return DataLoader(self.train_dataset, batch_size=None, collate_fn=self.data_collator)`
   ```

## Dead Ends

- **Setting batch_size=1 for IterableDataset** — IterableDataset requires batch_size=None; any integer value raises the same error. (90% fail)
- **Converting IterableDataset to a regular Dataset with `.to_iterable_dataset()`** — This creates another IterableDataset; the correct fix is to use `with_format('torch')` and handle batching manually. (80% fail)
- **Downgrading datasets to version 2.8.0** — Older versions had the same restriction; the error is by design. (70% fail)