ksuit.utils.training.training_iteration¶

Classes¶

TrainingIteration

Module Contents¶

class ksuit.utils.training.training_iteration.TrainingIteration(epoch=None, update=None, sample=None)¶

Parameters:

epoch (int | None)
update (int | None)
sample (int | None)

epoch = None¶

update = None¶

sample = None¶

copy()¶

Creates a copy of a TrainingIteration object.

Return type:: TrainingIteration

property specified_properties_count: int¶

Counts the number of specified (non-None) properties.

Return type:: int

property is_fully_specified: bool¶

Checks if all properties are specified.

Return type:: bool

property is_minimally_specified: bool¶

Checks if at least one property is specified.

Return type:: bool

get_n_equal_properties(other)¶

Counts the number of equal properties between two TrainingIteration objects.

Parameters:: other (TrainingIteration) – The other TrainingIteration object to compare with.
Return type:: int

to_fully_specified(updates_per_epoch, effective_batch_size)¶

Converts the TrainingIteration object to a fully specified TrainingIteration object. This method calculates the missing properties based on the specified ones.

Parameters:

updates_per_epoch (int) – The number of updates per epoch.
effective_batch_size (int) – The effective batch size used in the training process.

Return type:

TrainingIteration

has_same_specified_properties(other)¶

Checks if the specified properties of two TrainingIteration objects are the same.

Parameters:: other (TrainingIteration) – The other TrainingIteration object to compare with.
Return type:: bool

static from_string(value)¶

Converts a training iteration string into a TrainingIteration object.

The training iteration string should be in the format “E{epoch}_U{update}_S{sample}”. For instance: E5_U12_S123 corresponds to TrainingIteration(epoch=5, update=12, sample=123).

Parameters:: value (str) – The TrainingIteration string to convert.
Return type:: TrainingIteration

static contains_string(source)¶

Checks if the source string contains a training iteration string.

Parameters:: source (str) – The source string to check.
Returns:: True if the source string contains a training iteration string, False otherwise.
Return type:: bool

static find_string(source)¶

Checks if the source string contains a training iteration string and returns it.

Parameters:: source (str) – The source string to check.
Returns:: The found TrainingIteration string.
Return type:: str

static from_filename(fname)¶

Creates a TrainingIteration object from a filename that contains a training iteration string.

Parameters:: fname (str) – The filename to extract the training iteration string from.
Returns:: The created object.
Return type:: TrainingIteration

static to_fully_specified_from_filenames(directory, training_iteration, prefix=None, suffix=None)¶

Converts a minimally specified TrainingIteration to a fully specified one from filenames in a given folder. The first file containing a training iteration string and optionally starting with prefix or ending with suffix is used.

Parameters:

directory (str) – The directory containing training iteration files.
training_iteration (TrainingIteration) – The TrainingIteration object to match against.
prefix (str | None) – Optional prefix to filter files.
suffix (str | None) – Optional suffix to filter files.

Returns:

The created object.

Return type:

TrainingIteration

Raises:

FileNotFoundError – If no matching file is found.

to_target_specification(target)¶

Creates a new object that matches the target specification.

Example: ` self=TrainingIteration(epoch=6, update=12, sample=123)` target=TrainingIteration(epoch=5) print(self.to_target_specification(target)) TrainingIteration(epoch=6) ` :param target: The target TrainingIteration object to match against.

Returns:: The created object.
Return type:: TrainingIteration
Parameters:: target (TrainingIteration)