def compute_estimate_async():
"""Submit calculations to a running server instance"""
from openforcefield.typing.engines import smirnoff
setup_timestamp_logging()
# Load in the data set of interest.
data_set = ThermoMLDataSet.from_file(get_data_filename('properties/single_dielectric.xml'))
# Load in the force field to use.
force_field = smirnoff.ForceField(get_data_filename('forcefield/smirnoff99Frosst.offxml'))
# new_property_0 = copy.deepcopy(data_set.properties['COCCO{1.0}'][0])
# new_property_0.thermodynamic_state.temperature -= 2.0 * unit.kelvin
# new_property_0.id = str(uuid4())
#
# data_set.properties['COCCO{1.0}'].append(new_property_0)
#
# new_property_1 = copy.deepcopy(data_set.properties['COCCO{1.0}'][0])
# new_property_1.thermodynamic_state.temperature += 2.0 * unit.kelvin
# new_property_1.id = str(uuid4())
#
# data_set.properties['COCCO{1.0}'].append(new_property_1)
# Modify the submission options
submission_options = PropertyEstimatorOptions()
workflow_options = PropertyWorkflowOptions(PropertyWorkflowOptions.ConvergenceMode.RelativeUncertainty,
relative_uncertainty_fraction=100000)
submission_options.workflow_options = {
'Density': workflow_options,
'Dielectric': workflow_options,
'EnthalpyOfMixing': workflow_options
}
# submission_options.allowed_calculation_layers = ['SimulationLayer']
submission_options.allowed_calculation_layers = ['ReweightingLayer']
# Create the client object.
property_estimator = client.PropertyEstimatorClient()
# Submit the request to a running server.
request = property_estimator.request_estimate(data_set, force_field, submission_options)
logging.info('Request info: {}'.format(str(request)))
# Wait for the results.
result = request.results(synchronous=True)
logging.info('The server has returned a response: {}'.format(result.json()))
def compute_estimate_sync():
"""Submit calculations to a running server instance"""
from openforcefield.typing.engines import smirnoff
setup_timestamp_logging()
# Load in the data set of interest.
data_set = ThermoMLDataSet.from_file(get_data_filename('properties/single_density.xml'))
# Load in the force field to use.
force_field = smirnoff.ForceField(get_data_filename('forcefield/smirnoff99Frosst.offxml'))
# Create the client object.
property_estimator = client.PropertyEstimatorClient()
# Submit the request to a running server, and wait for the results.
result = property_estimator.request_estimate(data_set, force_field)
logging.info('The server has returned a response: {}'.format(result))
def main():
setup_timestamp_logging()
# Load in the force field
force_field_path = 'smirnoff99Frosst-1.1.0.offxml'
force_field_source = SmirnoffForceFieldSource.from_path(force_field_path)
# Load in the data set containing a single dielectric
# property.
with open('pure_data_set.json') as file:
data_set = PhysicalPropertyDataSet.parse_json(file.read())
data_set.filter_by_property_types('DielectricConstant')
# Set up the server object which run the calculations.
setup_server(backend_type=BackendType.LocalGPU,
max_number_of_workers=1,
port=8001)
# Request the estimates.
property_estimator = client.PropertyEstimatorClient(
client.ConnectionOptions(server_port=8001))
options = PropertyEstimatorOptions()
options.allowed_calculation_layers = ['SimulationLayer']
options.workflow_options = {
'DielectricConstant': {
'SimulationLayer':
WorkflowOptions(WorkflowOptions.ConvergenceMode.NoChecks),
'ReweightingLayer':
WorkflowOptions(WorkflowOptions.ConvergenceMode.NoChecks)
}
}
parameter_gradient_keys = [
ParameterGradientKey(tag='vdW', smirks='[#6X4:1]',
attribute='epsilon'),
ParameterGradientKey(tag='vdW',
smirks='[#6X4:1]',
attribute='rmin_half')
]
request = property_estimator.request_estimate(
property_set=data_set,
force_field_source=force_field_source,
options=options,
parameter_gradient_keys=parameter_gradient_keys)
# Wait for the results.
results = request.results(True, 5)
# Save the result to file.
with open('dielectric_simulation.json', 'wb') as file:
json_results = json.dumps(results,
sort_keys=True,
indent=2,
separators=(',', ': '),
cls=TypedJSONEncoder)
file.write(json_results.encode('utf-8'))
# Attempt to reweight the cached data.
options.allowed_calculation_layers = ['ReweightingLayer']
request = property_estimator.request_estimate(
property_set=data_set,
force_field_source=force_field_source,
options=options,
parameter_gradient_keys=parameter_gradient_keys)
# Wait for the results.
results = request.results(True, 5)
# Save the result to file.
with open('dielectric_reweight.json', 'wb') as file:
json_results = json.dumps(results,
sort_keys=True,
indent=2,
separators=(',', ': '),
cls=TypedJSONEncoder)
file.write(json_results.encode('utf-8'))
def main():
setup_timestamp_logging()
# Load in the force field
force_field_path = 'smirnoff99Frosst-1.1.0.offxml'
force_field_source = SmirnoffForceFieldSource.from_path(force_field_path)
# Create a data set containing three solvation free energies.
data_set = _create_data_set()
# Set up the compute backend which will run the calculations.
working_directory = 'working_directory'
storage_directory = 'storage_directory'
queue_resources = QueueWorkerResources(
number_of_threads=1,
number_of_gpus=1,
preferred_gpu_toolkit=QueueWorkerResources.GPUToolkit.CUDA,
per_thread_memory_limit=5 * unit.gigabyte,
wallclock_time_limit="05:59")
worker_script_commands = [
'conda activate propertyestimator', 'module load cuda/10.1'
]
calculation_backend = DaskLSFBackend(
minimum_number_of_workers=1,
maximum_number_of_workers=3,
resources_per_worker=queue_resources,
queue_name='gpuqueue',
setup_script_commands=worker_script_commands,
adaptive_interval='1000ms',
adaptive_class=CustomAdaptive)
# Set up a backend to cache simulation data in.
storage_backend = LocalFileStorage(storage_directory)
# Spin up the server object.
PropertyEstimatorServer(calculation_backend=calculation_backend,
storage_backend=storage_backend,
port=8005,
working_directory=working_directory)
# Request the estimates.
property_estimator = client.PropertyEstimatorClient(
client.ConnectionOptions(server_port=8005))
options = PropertyEstimatorOptions()
options.allowed_calculation_layers = ['SimulationLayer']
workflow_options = WorkflowOptions(
WorkflowOptions.ConvergenceMode.NoChecks)
options.workflow_options = {
'SolvationFreeEnergy': {
'SimulationLayer': workflow_options
}
}
options.workflow_schemas = {
'SolvationFreeEnergy': {
'SimulationLayer': _get_fixed_lambda_schema(workflow_options)
}
}
request = property_estimator.request_estimate(
property_set=data_set,
force_field_source=force_field_source,
options=options)
# Wait for the results.
results = request.results(True, 5)
# Save the result to file.
with open('solvation_free_energy_simulation.json', 'wb') as file:
json_results = json.dumps(results,
sort_keys=True,
indent=2,
separators=(',', ': '),
cls=TypedJSONEncoder)
file.write(json_results.encode('utf-8'))