Progress

class olympus.observers.progress.ElapsedRealTime(frequency_new_epoch: int = 0, frequency_new_batch: int = 0, frequency_new_trial: int = 0, priority: int = 0, start_time: datetime.datetime = <factory>, end_time: datetime.datetime = <factory>, frequency_end_batch: int = 1, frequency_end_train: int = 1)

Bases: olympus.observers.observer.Observer

Attributes:

elapsed_time

Methods

every(self, \*args[, epoch, batch])	Define how often this metric should be called
load_state_dict(self, state_dict)	Load a state dictionary to resume a previous training
on_end_train(self, task[, step])	Called at the end of training after the last epoch
on_new_batch(self, task, step[, input, context])	Called after a batch has been processed
on_new_epoch(self, task, epoch, context)	Called at the end of an epoch, before a new epoch starts
on_new_trial(self, task, step, parameters, uid)	Called after a trial has been processed
on_start_train(self, task[, step])	Called on ce the training starts
state_dict(self)	Return a state dictionary used to checkpointing and resuming
value(self)	Return the key values that metrics computes

on_end_batch

elapsed_time

frequency_end_batch = 1

frequency_end_train = 1

load_state_dict(self, state_dict)

Load a state dictionary to resume a previous training

on_end_batch(self, step, task, input=None, context=None)

on_end_train(self, task, step=None)

Called at the end of training after the last epoch

state_dict(self)

Return a state dictionary used to checkpointing and resuming

value(self)

Return the key values that metrics computes

class olympus.observers.progress.ProgressView(frequency_new_epoch: int = 0, frequency_new_batch: int = 0, frequency_new_trial: int = 0, priority: int = 0, speed_observer: Union[olympus.observers.progress.Speed, NoneType] = None, max_epoch: int = 0, max_step: int = 0, step_length: int = 0, epoch: int = 0, step: int = 0, multiplier: int = 0, frequency_end_epoch: int = <factory>, frequency_end_batch: int = <factory>, show_metrics: str = <factory>, frequency_trial: int = 0, worker_id: int = -1)

Bases: olympus.observers.observer.Observer

Attributes:

orion_handle

speed_observer

Methods

estimate_time_trial_finish(self, obs, epoch)	Estimate when a trial will finish
every(self, \*args[, epoch, batch])	Define how often this metric should be called
load_state_dict(self, state_dict)	Load a state dictionary to resume a previous training
on_end_train(self, task[, step])	Called at the end of training after the last epoch
on_new_batch(self, task, step[, input, context])	Called after a batch has been processed
on_new_epoch(self, task, epoch, context)	Called at the end of an epoch, before a new epoch starts
on_new_trial(self, task, step, parameters, uid)	Called after a trial has been processed
on_start_train(self, task[, step])	Called on ce the training starts
overall_progress(self)	Return the overall HPO progress in % completion
print_fun(value, …[, sep, end, file, flush])	Prints the values to a stream, or to sys.stdout by default.
state_dict(self)	Return a state dictionary used to checkpointing and resuming
value(self)	Return the key values that metrics computes

eta
init_speed_observer
number_of_trials
on_end_batch
on_end_epoch
show_progress

epoch = 0

estimate_time_trial_finish(self, obs, epoch)

Estimate when a trial will finish

eta(self, obs, epoch)

frequency_trial = 0

init_speed_observer(self, task)

load_state_dict(self, state_dict)

Load a state dictionary to resume a previous training

max_epoch = 0

max_step = 0

multiplier = 0

number_of_trials(self)

on_end_batch(self, task, step, input=None, context=None)

on_end_epoch(self, task, epoch, context)

orion_handle = None

overall_progress(self)

Return the overall HPO progress in % completion

print_fun(value, ..., sep=' ', end='n', file=sys.stdout, flush=False)

Prints the values to a stream, or to sys.stdout by default. Optional keyword arguments: file: a file-like object (stream); defaults to the current sys.stdout. sep: string inserted between values, default a space. end: string appended after the last value, default a newline. flush: whether to forcibly flush the stream.

show_progress(self, epoch, step=None)

speed_observer = None

state_dict(self)

Return a state dictionary used to checkpointing and resuming

step = 0

step_length = 0

value(self)

Return the key values that metrics computes

worker_id = -1

class olympus.observers.progress.SampleCount(frequency_new_epoch: int = 0, frequency_new_batch: int = 0, frequency_new_trial: int = 0, priority: int = 0, sample_count: int = 0, epoch: int = 0, frequency_end_batch: int = 1, frequency_end_epoch: int = 1)

Bases: olympus.observers.observer.Observer

Methods

every(self, \*args[, epoch, batch])	Define how often this metric should be called
load_state_dict(self, state_dict)	Load a state dictionary to resume a previous training
on_end_train(self, task[, step])	Called at the end of training after the last epoch
on_new_batch(self, task, step[, input, context])	Called after a batch has been processed
on_new_epoch(self, task, epoch, context)	Called at the end of an epoch, before a new epoch starts
on_new_trial(self, task, step, parameters, uid)	Called after a trial has been processed
on_start_train(self, task[, step])	Called on ce the training starts
state_dict(self)	Return a state dictionary used to checkpointing and resuming
value(self)	Return the key values that metrics computes

on_end_batch
on_end_epoch

epoch = 0

frequency_end_batch = 1

frequency_end_epoch = 1

load_state_dict(self, state_dict)

Load a state dictionary to resume a previous training

on_end_batch(self, task, step, input=None, context=None)

on_end_epoch(self, task, epoch, context)

sample_count = 0

state_dict(self)

Return a state dictionary used to checkpointing and resuming

value(self)

Return the key values that metrics computes

class olympus.observers.progress.Speed(frequency_new_epoch: int = 1, frequency_new_batch: int = 1, frequency_new_trial: int = 0, priority: int = 0, batch_size: int = 0, frequency_end_epoch: int = 1, frequency_end_batch: int = 1, step_time: olympus.utils.stat.StatStream = <factory>, epoch_time: olympus.utils.stat.StatStream = <factory>, step_start: datetime.datetime = <factory>, epoch_start: datetime.datetime = <factory>)

Bases: olympus.observers.observer.Observer

Methods

every(self, \*args[, epoch, batch])	Define how often this metric should be called
load_state_dict(self, state_dict)	Load a state dictionary to resume a previous training
on_end_train(self, task[, step])	Called at the end of training after the last epoch
on_new_batch(self, task, step[, input, context])	Called after a batch has been processed
on_new_epoch(self, epoch, task, context)	Called at the end of an epoch, before a new epoch starts
on_new_trial(self, task, step, parameters, uid)	Called after a trial has been processed
on_start_train(self, task[, step])	Called on ce the training starts
state_dict(self)	Return a state dictionary used to checkpointing and resuming
value(self)	Return the key values that metrics computes

guess_batch_size
on_end_batch
on_end_epoch

batch_size = 0

frequency_end_batch = 1

frequency_end_epoch = 1

frequency_new_batch = 1

frequency_new_epoch = 1

guess_batch_size(self, input)

load_state_dict(self, state_dict)

Load a state dictionary to resume a previous training

on_end_batch(self, task, step, input=None, context=None)

on_end_epoch(self, task, epoch, context=None)

on_new_batch(self, task, step, input=None, context=None)

Called after a batch has been processed

on_new_epoch(self, epoch, task, context)

Called at the end of an epoch, before a new epoch starts

state_dict(self)

Return a state dictionary used to checkpointing and resuming

value(self)

Return the key values that metrics computes

olympus.observers.progress.get_time_delta(start)