def __init__(self, cpu_limit=None):
"""
Initialize the study.
"""
self.results = None
# Callback method
def callback(experiment_id):
self._callback(experiment_id)
# Create experimenter
self.experimenter = Experimenter(
self.name(),
update_callback=callback,
update_callback_min_pause=self.EXPERIMENTER_CALLBACK_MIN_PAUSE,
cpu_limit=cpu_limit,
results_file=self.name() + '_results.csv',
)
def main(args):
"""
Tries to find the study specified and runs it.
"""
parser = argparse.ArgumentParser(
prog='merge',
description="Merges several result files into a new one.",
)
parser.add_argument("source",
help="files to be merged",
nargs="+",
type=str)
parser.add_argument("dest", help="file to write", nargs=1, type=str)
args = parser.parse_args(args)
try:
Experimenter.merge_result_files(args.source, args.dest[0])
except FileNotFoundError as e:
print(f'Could not find {e.filename.decode()}.')
quit(1)
def __init__(self, cpu_limit=None, gpu_limit=None):
"""
Initialize the study.
"""
self.results = None
self.full_run = True
# Callback method
def callback(experiment_id):
self._callback(experiment_id)
# Create experimenter
self.experimenter = Experimenter(
self.name(),
update_callback=callback,
update_callback_min_pause=self.EXPERIMENTER_CALLBACK_MIN_PAUSE,
cpu_limit=cpu_limit,
gpu_limit=gpu_limit,
results_file=self.name() +
('.csv.gz' if self.COMPRESSION else '.csv'),
)
Helper module to run studies as defined in pypuf.studies
"""
import sys
import argparse
from pypuf.experiments import Experimenter
from pypuf.tools import find_study_class
def main(args):
"""
Tries to find the study specified and runs it.
"""
parser = argparse.ArgumentParser(
prog='study',
description="Runs a pypuf study",
)
parser.add_argument("study", help="name of the study to be run", type=str)
args = parser.parse_args(args)
study_class = find_study_class(args.study)
print('Running {}.{}'.format(study_class.__module__, study_class.__name__))
study = study_class()
study.run()
if __name__ == '__main__':
Experimenter.disable_auto_multiprocessing()
main(sys.argv[1:])
class Study:
"""
Creates a number of experiments, runs them and produces a human-readable result.
"""
EXPERIMENTER_CALLBACK_MIN_PAUSE = 5 * 60
SHUFFLE = False
COMPRESSION = False
STUDY_MODULE_PREFIX = 'pypuf.studies.'
def __init__(self, cpu_limit=None, gpu_limit=None):
"""
Initialize the study.
"""
self.results = None
self.full_run = True
# Callback method
def callback(experiment_id):
self._callback(experiment_id)
# Create experimenter
self.experimenter = Experimenter(
self.name(),
update_callback=callback,
update_callback_min_pause=self.EXPERIMENTER_CALLBACK_MIN_PAUSE,
cpu_limit=cpu_limit,
gpu_limit=gpu_limit,
results_file=self.name() +
('.csv.gz' if self.COMPRESSION else '.csv'),
)
def name(self):
"""
returns the study's name
"""
name = self.__module__.__str__()
if name.startswith(self.STUDY_MODULE_PREFIX):
name = name[len(self.STUDY_MODULE_PREFIX):]
return name
def experiments(self):
"""
returns the study's experiments as a list
"""
return []
def plot(self):
"""
Generates this study's output. (Full or partial) results are available via `self.experimenter.results`.
"""
def run(self, part=0, total=1):
"""
runs the study, that is, create the experiments, run them through an experimenter,
collect the results and plot the output
"""
# Queue experiments
experiments = self.experiments()
assert experiments, 'Study {} did not define any experiments.'.format(
self.name())
partition_size = len(experiments) // total
self.full_run = partition_size == len(experiments)
start = part * partition_size
end = (part + 1) * partition_size
for e in experiments[start:end]:
self.experimenter.queue(e)
# Run experiments
if experiments[start:end]:
self.experimenter.run(shuffle=self.SHUFFLE)
# Plot results
self.plot()
else:
print(
f'Part {part} of {total} parts did not have any experiments, study defines a total of '
f'{len(experiments)} experiments.')
def _callback(self, _experiment_id=None):
"""
will be called by the experimenter after every finished experiment,
but at most every EXPERIMENTER_CALLBACK_MIN_PAUSE seconds.
"""
if not self.experimenter.results.empty and self.full_run:
self.plot()
class Study:
"""
Creates a number of experiments, runs them and produces a human-readable result.
"""
EXPERIMENTER_CALLBACK_MIN_PAUSE = 30
SHUFFLE = False
def __init__(self, cpu_limit=None):
"""
Initialize the study.
"""
self.results = None
# Callback method
def callback(experiment_id):
self._callback(experiment_id)
# Create experimenter
self.experimenter = Experimenter(
self.name(),
update_callback=callback,
update_callback_min_pause=self.EXPERIMENTER_CALLBACK_MIN_PAUSE,
cpu_limit=cpu_limit,
results_file=self.name() + '_results.csv',
)
def name(self):
"""
returns the study's name
"""
def experiments(self):
"""
returns the study's experiments as a list
"""
return []
def plot(self):
"""
Generates this study's output. (Full or partial) results are available via `self.experimenter.results`.
"""
def run(self):
"""
runs the study, that is, create the experiments, run them through an experimenter,
collect the results and plot the output
"""
# Queue experiments
experiments = self.experiments()
assert experiments, 'Study {} did not define any experiments.'.format(self.name())
for e in experiments:
self.experimenter.queue(e)
# Run experiments
self.experimenter.run(shuffle=self.SHUFFLE)
# Plot results
self.plot()
def _callback(self, _experiment_id=None):
"""
will be called by the experimenter after every finished experiment,
but at most every EXPERIMENTER_CALLBACK_MIN_PAUSE seconds.
"""
if not self.experimenter.results.empty:
self.plot()