DataHooks — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)

class lightning.pytorch.core.hooks.DataHooks[source]¶

Bases: object

Hooks to be used for data related stuff.

prepare_data_per_node¶

If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.

allow_zero_length_dataloader_with_multiple_devices¶

If True, dataloader with zero length within local rank is allowed. Default value is False.

on_after_batch_transfer(batch, dataloader_idx)[source]¶

Override to alter or apply batch augmentations to your batch after it is transferred to the device.

Note

To check the current state of execution of this hook you can useself.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

batch¶ (Any) – A batch of data that needs to be altered or augmented.
dataloader_idx¶ (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A batch of data

Example:

def on_after_batch_transfer(self, batch, dataloader_idx): batch['x'] = gpu_transforms(batch['x']) return batch

on_before_batch_transfer(batch, dataloader_idx)[source]¶

Override to alter or apply batch augmentations to your batch before it is transferred to the device.

Note

To check the current state of execution of this hook you can useself.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

batch¶ (Any) – A batch of data that needs to be altered or augmented.
dataloader_idx¶ (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A batch of data

Example:

def on_before_batch_transfer(self, batch, dataloader_idx): batch['x'] = transforms(batch['x']) return batch

predict_dataloader()[source]¶

An iterable or collection of iterables specifying prediction samples.

For more information about multiple dataloaders, see this section.

It’s recommended that all data downloads and preparation happen in prepare_data().

predict()
prepare_data()
setup()

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.

Return type:

Any

Returns:

A torch.utils.data.DataLoader or a sequence of them specifying prediction samples.

prepare_data()[source]¶

Use this to download and prepare data. Downloading and saving data with multiple processes (distributed settings) will result in corrupted data. Lightning ensures this method is called only within a single process, so you can safely add your downloading logic within. :rtype: None

Warning

DO NOT set state to the model (use setup instead) since this is NOT called on every device

Example:

def prepare_data(self): # good download_data() tokenize() etc()

# bad
self.split = data_split
self.some_state = some_other_state()

In a distributed environment, prepare_data can be called in two ways (using prepare_data_per_node)

Once per node. This is the default and is only called on LOCAL_RANK=0.
Once in total. Only called on GLOBAL_RANK=0.

Example:

DEFAULT

called once per node on LOCAL_RANK=0 of that node

class LitDataModule(LightningDataModule): def init(self): super().init() self.prepare_data_per_node = True

call on GLOBAL_RANK=0 (great for shared file systems)

class LitDataModule(LightningDataModule): def init(self): super().init() self.prepare_data_per_node = False

This is called before requesting the dataloaders:

model.prepare_data() initialize_distributed() model.setup(stage) model.train_dataloader() model.val_dataloader() model.test_dataloader() model.predict_dataloader()

setup(stage)[source]¶

Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when you need to build models dynamically or adjust something about them. This hook is called on every process when using DDP.

Parameters:

stage¶ (str) – either 'fit', 'validate', 'test', or 'predict'

Return type:

None

Example:

class LitModel(...): def init(self): self.l1 = None

def prepare_data(self):
    download_data()
    tokenize()

    # don't do this
    self.something = else

def setup(self, stage):
    data = load_data(...)
    self.l1 = nn.Linear(28, data.num_classes)

teardown(stage)[source]¶

Called at the end of fit (train + validate), validate, test, or predict.

Parameters:

stage¶ (str) – either 'fit', 'validate', 'test', or 'predict'

Return type:

None

test_dataloader()[source]¶

An iterable or collection of iterables specifying test samples.

For more information about multiple dataloaders, see this section.

For data processing use the following pattern: :rtype: Any

download in prepare_data()

process and split in setup()

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

test()
prepare_data()
setup()

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Note

If you don’t need a test dataset and a test_step(), you don’t need to implement this method.

train_dataloader()[source]¶

An iterable or collection of iterables specifying training samples.

For more information about multiple dataloaders, see this section.

The dataloader you return will not be reloaded unless you setreload_dataloaders_every_n_epochs to a positive integer.

For data processing use the following pattern: :rtype: Any

download in prepare_data()

process and split in setup()

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

fit()
prepare_data()
setup()

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

transfer_batch_to_device(batch, device, dataloader_idx)[source]¶

Override this hook if your DataLoader returns tensors wrapped in a custom data structure.

The data types listed below (and any arbitrary nesting of them) are supported out of the box:

torch.Tensor or anything that implements .to(…)
list
dict
tuple

For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, …).

Note

This hook should only transfer the data and not modify it, nor should it move the data to any other device than the one passed in as argument (unless you know what you are doing). To check the current state of execution of this hook you can useself.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

batch¶ (Any) – A batch of data that needs to be transferred to a new device.
device¶ (device) – The target device as defined in PyTorch.
dataloader_idx¶ (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A reference to the data on the new device.

Example:

def transfer_batch_to_device(self, batch, device, dataloader_idx): if isinstance(batch, CustomBatch): # move all tensors in your custom data structure to the device batch.samples = batch.samples.to(device) batch.targets = batch.targets.to(device) elif dataloader_idx == 0: # skip device transfer for the first dataloader or anything you wish pass else: batch = super().transfer_batch_to_device(batch, device, dataloader_idx) return batch