TensorDictBase.from_h5#

classmethod TensorDictBase.from_h5(filename, *, mode: str = 'r', auto_batch_size: bool = False, batch_dims: int | None = None, batch_size: Size | None = None, non_tensor_as_stack: bool = False)#

Creates a PersistentTensorDict from a h5 file.

Parameters:

filename (str) – The path to the h5 file.

Keyword Arguments:

mode (str, optional) – how the file is opened: "r" (read only), "r+" (read and write), or another mode of h5py.File (see PersistentTensorDict). Defaults to "r".
auto_batch_size (bool, optional) – If True, the batch size will be computed automatically. Defaults to False.
batch_dims (int, optional) – If auto_batch_size is True, defines how many dimensions the output tensordict should have. Defaults to None (full batch-size at each level).
batch_size (Size, optional) – The batch size of the TensorDict. Defaults to None.
non_tensor_as_stack (bool, optional) – if True, a non-tensor dataset that tensordict did not write, such as a string column with one string per row, reads as a NonTensorStack of its rows when its leading dims equal the batch size. Otherwise it reads as one NonTensorData that holds the whole dataset. Defaults to False.

Returns:

A PersistentTensorDict representation of the input h5 file.

Examples

>>> import os
>>> import tempfile
>>> import torch
>>> from tensordict import TensorDict
>>> data = TensorDict(key1=torch.zeros(3), key2=torch.ones(3), batch_size=[3])
>>> with tempfile.TemporaryDirectory() as tmpdir:
...     path = os.path.join(tmpdir, "file.h5")
...     _ = data.to_h5(path)
...     td = TensorDict.from_h5(path)
...     print(td)
PersistentTensorDict(
    fields={
        key1: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        key2: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

Every tensordict class and every tensorclass has this method. For the other methods, see Methods by task.

TensorDictBase.from_h5#

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