---
title: Streaming Datasets
description: How to use streaming mode for large-scale datasets and memory-efficient training
order: 10
---

Streaming enables memory-efficient training with large datasets by loading data
incrementally rather than loading the entire dataset into memory at once.

Use streaming when:

- Your dataset is too large to fit in memory (e.g. when you're doing pretraining with massive text corpora)
- You want to start training immediately without preprocessing the entire dataset

Streaming works with both remote and locally stored datasets!

::: {.callout-note}
Streaming currently only supports a single dataset. Multi-dataset support will be added soon.
:::


## Configuration

### Basic Streaming

Enable streaming mode by setting the `streaming` flag:

```yaml
streaming: true
```

### Pretraining with Streaming

For pretraining tasks, streaming is automatically enabled when using `pretraining_dataset`:

```yaml
pretraining_dataset:
  - path: HuggingFaceFW/fineweb-edu
    type: pretrain
    text_column: text
    split: train

# Optionally, enable sample packing
streaming_multipack_buffer_size: 10000
sample_packing: true
```

### SFT with Streaming

For supervised fine-tuning with streaming:

```yaml
streaming: true
datasets:
  - path: tatsu-lab/alpaca
    type: alpaca
    split: train

# Optionally, enable sample packing
streaming_multipack_buffer_size: 10000
sample_packing: true
```

## Configuration Options

### `streaming_multipack_buffer_size`

Controls the buffer size for multipack streaming (default: 10,000). This determines how
many samples are buffered before packing. Larger buffers can improve packing efficiency
but use more memory.

### `shuffle_merged_datasets`

When enabled, shuffles the streaming dataset using the buffer. This requires additional
memory for the shuffle buffer.

## Sample Packing with Streaming

Sample packing is supported for streaming datasets. When enabled, multiple samples are
packed into a single sequence to maximize GPU utilization:

```yaml
sample_packing: true
streaming_multipack_buffer_size: 10000

# For SFT: attention is automatically isolated between packed samples
# For pretraining: control with pretrain_multipack_attn
pretrain_multipack_attn: true  # prevent cross-attention between packed samples
```

For more information, see our [documentation](multipack.qmd) on multipacking.

## Important Considerations

### Memory Usage

While streaming reduces memory usage compared to loading entire datasets, you still need
to consider:

- You can control the memory usage by adjusting `streaming_multipack_buffer_size`
- Sample packing requires buffering multiple samples
- Shuffling requires additional memory for the shuffle buffer

### Performance

- Streaming may have slightly higher latency compared to preprocessed datasets, as samples are processed on-the-fly
- Network speed and disk read speed are important when streaming from remote sources or a local dataset, respectively
- Consider using `axolotl preprocess` for smaller or more frequently used datasets

### Evaluation Datasets

Evaluation datasets are not streamed to ensure consistent evaluation metrics. They're
loaded normally even when training uses streaming.

## Examples

See the `examples/streaming/` directory for complete configuration examples:

- `pretrain.yaml`: Pretraining with streaming dataset
- `sft.yaml`: Supervised fine-tuning with streaming