H5StorageCheckpointer

class torchrl.data.replay_buffers.H5StorageCheckpointer(*, checkpoint_file: str = 'checkpoint.h5', done_keys=None, reward_keys=None, h5_kwargs=None, **kwargs)

Saves the storage in a compact form, saving space on the TED format and using H5 format to save the data.

This class explicitly assumes and does NOT check that:

• done states (including terminated and truncated) at the root are always False;

• observations in the “next” tensordict are shifted by one step in the future (this is not the case when a multi-step transform is used for instance).

Keyword Arguments:

• checkpoint_file – the filename where to save the checkpointed data. This will be ignored iff the path passed to dumps / loads ends with the .h5 suffix. Defaults to "checkpoint.h5".

• h5_kwargs (Dict[str, Any] or Tuple[Tuple[str, Any], ...]) – kwargs to be passed to h5py.File.create_dataset().

Note

To prevent out-of-memory issues, the data of the H5 file will be temporarily written on memory-mapped tensors stored in shared file system. The physical memory usage may increase during loading as a consequence.