# Fast Resumption

Being resistant to timeouts, hardware failures, or other errors is crucial to efficient distributed training. While other datasets require iterating through previously seen samples before resuming, Streaming allows for **immediate and deterministic resumption** in the middle of an epoch by being stateful.

## Saving and loading state

To get fast, deterministic mid-epoch resumption, **make sure to use the {class}`streaming.StreamingDataLoader` object**. StreamingDataLoader works in conjunction with StreamingDataset to save and load dataset state. It works exactly like a normal PyTorch DataLoader.

When checkpointing, simply call the `state_dict` method of `StreamingDataLoader` and save it along with your checkpoint. Then, when resuming, call `load_state_dict` with the saved state, and you'll be running in no time. Here's an example:
<!--pytest.mark.skip-->
```python
from streaming import StreamingDataset
from streaming import StreamingDataLoader

dataset = StreamingDataset(local='/tmp/cache', remote='s3://remote/dataset', batch_size=1)
dataloader = StreamingDataLoader(dataset, batch_size=1)

# Here, we assume each sample in our dataset has fields 'x' and 'y'.
# We save the dataloader state after 4 batches, and stop after 6 batches.
state_dict = None
for i, batch in enumerate(dataloader):
    print(i, batch['x'], batch['y'])
    if i == 4:
        state_dict = dataloader.state_dict()
    if i == 6:
        break
```

Now, we've completed 4 batches and seen 6, when training has "stopped". This is akin to a training job failing some time after a checkpointing interval. Now, we resume from where we left off:
<!--pytest.mark.skip-->
```python
# Create a new dataset
dataset_2 = StreamingDataset(local='cache', remote='path', batch_size=1)
dataloader_2 = StreamingDataLoader(dataset_2, batch_size=1)
# Load in the state dict that was previously saved
dataloader_2.load_state_dict(state_dict)

# Iterate over the dataset, which will start from batch 5 now.
for i, batch in enumerate(dataloader_2):
    print(i, batch['x'], batch['y'])
```

## Resumption with Composer

When training with [Composer](https://docs.mosaicml.com/projects/composer/en/stable/), our open-soure deep learning training library built on top of PyTorch, fast resumption is handled automatically. Composer and Streaming work seamlessly together to provide efficient, scalable neural network training.