def __converge(self, tpr_file=None, **kwargs):
for key in ('append_output', ):
if key in kwargs:
raise TypeError(
'Conflicting key word argument. Cannot accept {}.'.format(
key))
self.__prep_input(tpr_file)
md = from_tpr(self._tprs, append_output=False, **kwargs)
self.build_plugins(ConvergencePluginConfig())
for plugin in self.__plugins:
md.add_dependency(plugin)
workdir = os.getcwd()
self._logger.info("=====CONVERGENCE INFO======\n")
self._logger.info(f'Working directory: {workdir}')
context = _context(md,
workdir_list=self.workdirs,
communicator=self._communicator)
with context as session:
session.run()
# Get the absolute time (in ps) at which the convergence run finished.
# This value will be needed if a production run needs to be restarted.
# noinspection PyUnresolvedReferences
self.run_data.set(start_time=context.potentials[0].time)
for name in self.__names:
current_alpha = self.run_data.get('alpha', name=name)
current_target = self.run_data.get('target', name=name)
message = f'Plugin {name}: alpha = {current_alpha}, target = {current_target}'
self._logger.info(message)
return context
def __converge(self, tpr_file=None, **kwargs):
for key in ('append_output', ):
if key in kwargs:
raise TypeError(
'Conflicting key word argument. Cannot accept {}.'.format(
key))
self.__prep_input(tpr_file)
md = from_tpr(self._tprs, append_output=False, **kwargs)
self.build_plugins(ConvergencePluginConfig())
if len(self.__plugins) == 0:
warnings.warn('No BRER restraints are being applied! User error?')
for plugin in self.__plugins:
md.add_dependency(plugin)
workdir = os.getcwd()
self._logger.info("=====CONVERGENCE INFO======\n")
self._logger.info(f'Working directory: {workdir}')
context = _context(md,
workdir_list=self.workdirs,
communicator=self._communicator)
# WARNING: We do not yet handle situations where a rank has no work to do.
# See https://github.com/kassonlab/run_brer/issues/18
# and https://github.com/kassonlab/run_brer/issues/55
with context as session:
session.run()
# Through at least gmxapi 0.4, the *potentials* attribute is created on
# the Context for any Session launched with MD work to perform. An explicit
# error message here should be more helpful than an AttributeError below,
# but we don't really know what went wrong.
# Ref https://github.com/kassonlab/run_brer/issues/55
if not hasattr(context, 'potentials'):
raise RuntimeError(
'Invalid gmxapi Context: missing "potentials" attribute.')
# Get the absolute time (in ps) at which the convergence run finished.
# This value will be needed if a production run needs to be restarted.
# noinspection PyUnresolvedReferences
self.run_data.set(start_time=context.potentials[0].time)
for name in self.__names:
current_alpha = self.run_data.get('alpha', name=name)
current_target = self.run_data.get('target', name=name)
message = f'Plugin {name}: alpha = {current_alpha}, target = {current_target}'
self._logger.info(message)
return context
def test_ensemble_potential_nompi(spc_water_box):
"""Test ensemble potential without an ensemble.
"""
tpr_filename = spc_water_box
print("Testing plugin potential with input file {}".format(
os.path.abspath(tpr_filename)))
assert api_is_at_least(0, 0, 5)
md = from_tpr([tpr_filename], append_output=False)
# Create a WorkElement for the potential
params = {
'sites': [1, 4],
'nbins': 10,
'binWidth': 0.1,
'min_dist': 0.,
'max_dist': 10.,
'experimental': [1.] * 10,
'nsamples': 1,
'sample_period': 0.001,
'nwindows': 4,
'k': 10000.,
'sigma': 1.
}
potential = WorkElement(namespace="myplugin",
operation="ensemble_restraint",
params=params)
# Note that we could flexibly capture accessor methods as workflow elements, too. Maybe we can
# hide the extra Python bindings by letting myplugin.HarmonicRestraint automatically convert
# to a WorkElement when add_dependency is called on it.
potential.name = "ensemble_restraint"
md.add_dependency(potential)
context = _context(md)
with context as session:
session.run()
def __production(self, tpr_file=None, **kwargs):
for key in ('append_output', 'end_time'):
if key in kwargs:
raise TypeError(
'Conflicting key word argument. Cannot accept {}.'.format(
key))
tpr_list = list(self._tprs)
tpr_list[self._rank] = self.__prep_input(tpr_file)
if tpr_file is not None:
# If bootstrap TPR is provided, we are not continuing from the
# convergence phase trajectory.
self.run_data.set(start_time=0.0)
# Calculate the time (in ps) at which the trajectory for this BRER iteration should finish.
# This should be: the end time of the convergence run + the amount of time for
# production simulation (specified by the user).
start_time = self.run_data.get('start_time')
target_end_time = self.run_data.get('production_time') + start_time
md = from_tpr(tpr_list,
end_time=target_end_time,
append_output=False,
**kwargs)
self.build_plugins(ProductionPluginConfig())
if len(self.__plugins) == 0:
warnings.warn('No BRER restraints are being applied! User error?')
for plugin in self.__plugins:
md.add_dependency(plugin)
workdir = os.getcwd()
self._logger.info("=====PRODUCTION INFO======\n")
self._logger.info(f'Working directory: {workdir}')
context = _context(md,
workdir_list=self.workdirs,
communicator=self._communicator)
# WARNING: We do not yet handle situations where a rank has no work to do.
# See https://github.com/kassonlab/run_brer/issues/18
# and https://github.com/kassonlab/run_brer/issues/55
with context as session:
session.run()
# Through at least gmxapi 0.4, the *potentials* attribute is created on
# the Context for any Session launched with MD work to perform. An explicit
# error message here should be more helpful than an AttributeError below,
# but we don't really know what went wrong.
# Ref https://github.com/kassonlab/run_brer/issues/55
if not hasattr(context, 'potentials'):
raise RuntimeError(
'Invalid gmxapi Context: missing "potentials" attribute.')
# Get the start and end times for the simulation managed by this Python interpreter.
# Note that these are the times for all potentials in a single simulation
# (which should be the same). We are not gathering values across any potential ensembles.
start_times = [
potential.start_time for potential in context.potentials
if hasattr(potential, 'start_time')
]
if len(start_times) > 0:
session_start_time = start_times[0]
if not all(session_start_time == t for t in start_times):
self._logger.warning(
'Potentials report inconsistent start times: '
', '.join(str(t) for t in start_times))
assert session_start_time >= start_time
else:
# If the plugin attribute is missing, assume that the convergence phase behaved properly.
session_start_time = start_time
end_times = [
potential.time for potential in context.potentials
if hasattr(potential, 'time')
]
if len(end_times) > 0:
session_end_time = end_times[0]
if not all(session_end_time == t for t in end_times):
self._logger.warning(
'Potentials report inconsistent end times: '
', '.join(str(t) for t in end_times))
else:
session_end_time = None
if session_end_time is not None:
self.run_data.set(end_time=session_end_time)
trajectory_time = None
if session_end_time is not None:
trajectory_time = session_end_time - session_start_time
if trajectory_time is not None:
self._logger.info(
f"{trajectory_time} ps production phase trajectory segment.")
for name in self.__names:
current_alpha = self.run_data.get('alpha', name=name)
current_target = self.run_data.get('target', name=name)
self._logger.info("Plugin {}: alpha = {}, target = {}".format(
name, current_alpha, current_target))
return context
def __train(self, tpr_file=None, **kwargs):
for key in ('append_output', ):
if key in kwargs:
raise TypeError(
'Conflicting key word argument. Cannot accept {}.'.format(
key))
# do re-sampling
targets = self.pairs.re_sample()
self._logger.info('New targets: {}'.format(targets))
for name in self.__names:
self.run_data.set(name=name, target=targets[name])
# save the new targets to the BRER checkpoint file.
self.run_data.save_config(fnm=self.state_json)
workdir = self.workdirs[self._rank]
# backup existing checkpoint.
# TODO: Don't backup the cpt, actually use it!!
cpt = '{}/state.cpt'.format(workdir)
if os.path.exists(cpt):
self._logger.warning(
'There is a checkpoint file in your current working directory, but you '
'are '
'training. The cpt will be backed up and the run will start over with '
'new targets')
shutil.move(cpt, '{}.bak'.format(cpt))
# If this is not the first BRER iteration, grab the checkpoint from the production
# phase of the last round
self.__prep_input(tpr_file)
# Set up a dictionary to go from plugin name -> restraint name
sites_to_name = {}
# Build the gmxapi session.
tpr_list: Sequence[str] = self._tprs
md = from_tpr(tpr_list, append_output=False, **kwargs)
self.build_plugins(TrainingPluginConfig())
if len(self.__plugins) == 0:
warnings.warn('No BRER restraints are being applied! User error?')
for plugin in self.__plugins:
plugin_name = plugin.name
for name in self.__names:
run_data_sites = "{}".format(
self.run_data.get('sites', name=name))
if run_data_sites == plugin_name:
sites_to_name[plugin_name] = name
md.add_dependency(plugin)
context = _context(md,
workdir_list=self.workdirs,
communicator=self._communicator)
self._logger.info("=====TRAINING INFO======\n")
self._logger.info(f'Working directory: {workdir}')
# Run it.
# WARNING: We do not yet handle situations where a rank has no work to do.
# See https://github.com/kassonlab/run_brer/issues/18
# and https://github.com/kassonlab/run_brer/issues/55
with context as session:
session.run()
# Through at least gmxapi 0.4, the *potentials* attribute is created on
# the Context for any Session launched with MD work to perform. An explicit
# error message here should be more helpful than an AttributeError below,
# but we don't really know what went wrong.
# Ref https://github.com/kassonlab/run_brer/issues/55
if not hasattr(context, 'potentials'):
raise RuntimeError(
'Invalid gmxapi Context: missing "potentials" attribute.')
for i in range(len(self.__names)):
# TODO: ParallelArrayContext.potentials needs to be declared to avoid IDE
# warnings.
# noinspection PyUnresolvedReferences
current_name = sites_to_name[context.potentials[i].name]
# In the future runs (convergence, production) we need the ABSOLUTE VALUE
# of alpha.
# noinspection PyUnresolvedReferences
current_alpha = context.potentials[i].alpha
if current_alpha == 0.0:
raise RuntimeError(
'Alpha value was constrained to 0.0, which indicates something went wrong'
)
# noinspection PyUnresolvedReferences
current_target = context.potentials[i].target
self.run_data.set(name=current_name, alpha=current_alpha)
self.run_data.set(name=current_name, target=current_target)
self._logger.info("Plugin {}: alpha = {}, target = {}".format(
current_name, current_alpha, current_target))
return context
def __production(self, tpr_file=None, **kwargs):
for key in ('append_output', 'end_time'):
if key in kwargs:
raise TypeError(
'Conflicting key word argument. Cannot accept {}.'.format(
key))
tpr_list = list(self._tprs)
tpr_list[self._rank] = self.__prep_input(tpr_file)
if tpr_file is not None:
# If bootstrap TPR is provided, we are not continuing from the
# convergence phase trajectory.
self.run_data.set(start_time=0.0)
# Calculate the time (in ps) at which the trajectory for this BRER iteration should finish.
# This should be: the end time of the convergence run + the amount of time for
# production simulation (specified by the user).
start_time = self.run_data.get('start_time')
target_end_time = self.run_data.get('production_time') + start_time
md = from_tpr(tpr_list,
end_time=target_end_time,
append_output=False,
**kwargs)
self.build_plugins(ProductionPluginConfig())
for plugin in self.__plugins:
md.add_dependency(plugin)
workdir = os.getcwd()
self._logger.info("=====PRODUCTION INFO======\n")
self._logger.info(f'Working directory: {workdir}')
context = _context(md,
workdir_list=self.workdirs,
communicator=self._communicator)
with context as session:
session.run()
# Get the start and end times for the simulation managed by this Python interpreter.
# Note that these are the times for all potentials in a single simulation
# (which should be the same). We are not gathering values across any potential ensembles.
start_times = [
potential.start_time for potential in context.potentials
if hasattr(potential, 'start_time')
]
if len(start_times) > 0:
session_start_time = start_times[0]
if not all(session_start_time == t for t in start_times):
self._logger.warning(
'Potentials report inconsistent start times: '
', '.join(str(t) for t in start_times))
assert session_start_time >= start_time
else:
# If the plugin attribute is missing, assume that the convergence phase behaved properly.
session_start_time = start_time
end_times = [
potential.time for potential in context.potentials
if hasattr(potential, 'time')
]
if len(end_times) > 0:
session_end_time = end_times[0]
if not all(session_end_time == t for t in end_times):
self._logger.warning(
'Potentials report inconsistent end times: '
', '.join(str(t) for t in end_times))
else:
session_end_time = None
if session_end_time is not None:
self.run_data.set(end_time=session_end_time)
trajectory_time = None
if session_end_time is not None:
trajectory_time = session_end_time - session_start_time
if trajectory_time is not None:
self._logger.info(
f"{trajectory_time} ps production phase trajectory segment.")
for name in self.__names:
current_alpha = self.run_data.get('alpha', name=name)
current_target = self.run_data.get('target', name=name)
self._logger.info("Plugin {}: alpha = {}, target = {}".format(
name, current_alpha, current_target))
return context
def __train(self, tpr_file=None, **kwargs):
for key in ('append_output', ):
if key in kwargs:
raise TypeError(
'Conflicting key word argument. Cannot accept {}.'.format(
key))
# do re-sampling
targets = self.pairs.re_sample()
self._logger.info('New targets: {}'.format(targets))
for name in self.__names:
self.run_data.set(name=name, target=targets[name])
# save the new targets to the BRER checkpoint file.
self.run_data.save_config(fnm=self.state_json)
workdir = self.workdirs[self._rank]
# backup existing checkpoint.
# TODO: Don't backup the cpt, actually use it!!
cpt = '{}/state.cpt'.format(workdir)
if os.path.exists(cpt):
self._logger.warning(
'There is a checkpoint file in your current working directory, but you '
'are '
'training. The cpt will be backed up and the run will start over with '
'new targets')
shutil.move(cpt, '{}.bak'.format(cpt))
# If this is not the first BRER iteration, grab the checkpoint from the production
# phase of the last round
self.__prep_input(tpr_file)
# Set up a dictionary to go from plugin name -> restraint name
sites_to_name = {}
# Build the gmxapi session.
tpr_list: Sequence[str] = self._tprs
md = from_tpr(tpr_list, append_output=False, **kwargs)
self.build_plugins(TrainingPluginConfig())
for plugin in self.__plugins:
plugin_name = plugin.name
for name in self.__names:
run_data_sites = "{}".format(
self.run_data.get('sites', name=name))
if run_data_sites == plugin_name:
sites_to_name[plugin_name] = name
md.add_dependency(plugin)
context = _context(md,
workdir_list=self.workdirs,
communicator=self._communicator)
self._logger.info("=====TRAINING INFO======\n")
self._logger.info(f'Working directory: {workdir}')
# Run it.
with context as session:
session.run()
for i in range(len(self.__names)):
# TODO: ParallelArrayContext.potentials needs to be declared to avoid IDE
# warnings.
# noinspection PyUnresolvedReferences
current_name = sites_to_name[context.potentials[i].name]
# In the future runs (convergence, production) we need the ABSOLUTE VALUE
# of alpha.
# noinspection PyUnresolvedReferences
current_alpha = context.potentials[i].alpha
# noinspection PyUnresolvedReferences
current_target = context.potentials[i].target
self.run_data.set(name=current_name, alpha=current_alpha)
self.run_data.set(name=current_name, target=current_target)
self._logger.info("Plugin {}: alpha = {}, target = {}".format(
current_name, current_alpha, current_target))
return context