qimpy.io.Checkpoint

class Checkpoint(filename, *, writable=False, rotate=True)

Bases: File

Helper for checkpoint load/save from HDF5 files.

Parameters:

filename (str)
writable (bool)
rotate (bool)

__init__(filename, *, writable=False, rotate=True)

Open a HDF5 checkpoint file filename for read or write based on writable.

In write mode, if rotate (the default), the file is first written to filename + ‘.part’ and then rotated into filename upon closing (with a previously existing filename moved into filename + ‘.bak’). This prevents corruption of the checkpoint if the job is terminated due to time limit while the checkpoint is being written.

Parameters:

filename (str)
writable (bool)
rotate (bool)

Return type:

None

Methods

`__init__`	Open a HDF5 checkpoint file filename for read or write based on writable.
`build_virtual_dataset`	Assemble a virtual dataset in this group.
`clear`	D.clear() -> None.
`close`	Close the file, and perform rotations if applicable.
`copy`	Copy an object or group.
`create_dataset`	Create a new HDF5 dataset
`create_dataset_complex`	Create a dataset at path suitable for a complex array of size shape.
`create_dataset_like`	Create a dataset similar to other.
`create_dataset_real`	Create a dataset at path suitable for a real array of size shape.
`create_group`	Create and return a new subgroup.
`create_virtual_dataset`	Create a new virtual dataset in this group.
`flush`	Tell the HDF5 library to flush its buffers.
`get`	Retrieve an item or other information.
`in_memory`	Create an HDF5 file in memory, without an underlying file
`items`	Get a view object on member items
`keys`	Get a view object on member names
`move`	Move a link to a new location in the file.
`pop`	D.pop(k[,d]) -> v, remove specified key and return the corresponding value.
`popitem`	D.popitem() -> (k, v), remove and return some (key, value) pair as a 2-tuple; but raise KeyError if D is empty.
`read_slice`	Read a slice of data from data set dset in file, starting at offset and of length size in each dimension.
`read_slice_complex`	Same as `read_slice()`, but for complex data.
`require_dataset`	Open a dataset, creating it if it doesn't exist.
`require_group`	Return a group, creating it if it doesn't exist.
`setdefault`	D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D
`update`	D.update([E, ]**F) -> None.
`values`	Get a view object on member objects
`visit`	Recursively visit all names in this group and subgroups.
`visit_links`	Recursively visit all names in this group and subgroups.
`visititems`	Recursively visit names and objects in this group.
`visititems_links`	Recursively visit links in this group.
`write_slice`	Write a slice of data to dataset dset at offset offset from data (taking care of transfer to CPU if needed).
`write_slice_complex`	Same as `write_slice()`, but for complex data.

Attributes

`attrs`	Attributes attached to this object
`driver`	Low-level HDF5 file driver used to open file
`file`	Return a File instance associated with this object
`filename`	File name on disk
`id`	Low-level identifier appropriate for this object
`libver`	low, high)
`meta_block_size`	Meta block size (in bytes)
`mode`	Python mode used to open file
`name`	Return the full name of this object.
`parent`	Return the parent group of this object.
`ref`	An (opaque) HDF5 reference to this object
`regionref`	Create a region reference (Datasets only).
`swmr_mode`	Controls single-writer multiple-reader mode
`userblock_size`	User block size (in bytes)
`writable`	Whether file has been opened for writing
`filename_move`	If non-empty, move to this filename upon closing

close(): Close the file, and perform rotations if applicable.

create_dataset_complex(path, shape, dtype=torch.complex128)

Create a dataset at path suitable for a complex array of size shape. This creates a real array with a final dimension of length 2. This format is used by write_slice_complex() and read_slice_complex().

Parameters:

path (str)
shape (tuple[int, ...])
dtype (dtype)

Return type:

Any

create_dataset_real(path, shape, dtype=torch.float64)

Create a dataset at path suitable for a real array of size shape. Additionally, dtype is translated from torch to numpy for convenience.

Parameters:

path (str)
shape (tuple[int, ...])
dtype (dtype)

Return type:

Any

read_slice(dset, offset, size)

Read a slice of data from data set dset in file, starting at offset and of length size in each dimension. Returns data on CPU or GPU as specified by qimpy.rc.device.

Parameters:

dset (Any)
offset (tuple[int, ...])
size (tuple[int, ...])

Return type:

Tensor

read_slice_complex(dset, offset, size)

Same as read_slice(), but for complex data. Converts data from real storage as created by create_dataset_complex() to a complex tensor on output.

Parameters:

dset (Any)
offset (tuple[int, ...])
size (tuple[int, ...])

Return type:

Tensor

write_slice(dset, offset, data)

Write a slice of data to dataset dset at offset offset from data (taking care of transfer to CPU if needed). Note that all of data is written, so pass in the slice to be written from current process. This may be called from any subset of MPI processes independently, as no metadata modification such as dataset creation is done here.

Parameters:

dset (Any)
offset (tuple[int, ...])
data (Tensor)

Return type:

None

write_slice_complex(dset, offset, data)

Same as write_slice(), but for complex data. Converts data to real storage compatible with create_dataset_complex()

Parameters:

dset (Any)
offset (tuple[int, ...])
data (Tensor)

Return type:

None

filename_move: str: If non-empty, move to this filename upon closing

writable: bool: Whether file has been opened for writing