def distribute_job(dict_input: dict, split_function: Callable) -> dict:
"""
Using the jobs and parameters defined in `dict_input` create
a set of independent jobs.
"""
dict_jobs = split_function(lift(dict_input))
return run_parallel_jobs(dict_jobs, lift(dict_input))
def test_storable():
a = A()
b = A()
a.x = 1
b.x = f(3, 4)
c = g(a, lift(b))
b.x = c
d = h(lift(b), a)
result = run_process(d, n_processes=1, registry=serial.base)
assert result.x == 7
def test_storable():
a = A()
b = A()
a.x = 1
b.x = f(3, 4)
c = g(a, lift(b))
b.x = c
d = h(lift(b), a)
result = run_process(d, n_processes=1, registry=serial.base)
assert result.x == 7
def test_lift_01():
a = A()
a.x = add(1, 2)
a.y = sub(9, 11)
b = f(lift(a))
result = run_single(b)
assert result == 1
def _parameter_sweep(self, parameter_space, kernel_options, device_options, tuning_options):
"""Build a Noodles workflow by sweeping the parameter space"""
results = []
#randomize parameter space to do pseudo load balancing
parameter_space = list(parameter_space)
random.shuffle(parameter_space)
#split parameter space into chunks
work_per_thread = int(numpy.ceil(len(parameter_space) / float(self.max_threads)))
chunks = _chunk_list(parameter_space, work_per_thread)
for chunk in chunks:
chunked_result = self._run_chunk(chunk, kernel_options, device_options, tuning_options)
results.append(lift(chunked_result))
return gather(*results)
def run_partialcharges(results: Results,
options: Options,
promise: PromisedObject = None) -> dict:
"""
Compute partial charges
"""
opts = edit_calculator_options(options,
['esp2multipole', 'partialcharges'])
if promise is not None:
path_partialcharges = options.path_optionfiles / "partialcharges.xml"
sections_to_edit = {"partialcharges": {"input": promise}}
opts['partialcharges'] = schedule(edit_xml_file)(
path_partialcharges, 'options', lift(sections_to_edit))
logger.info("Running CHELPG fit")
args = create_promise_command("xtp_tools -e partialcharges -o {}",
opts['partialcharges'])
return call_xtp_cmd(args,
options.scratch_dir / 'partialcharges',
expected_output={})
def _parameter_sweep(self, parameter_space, kernel_options, device_options,
tuning_options):
"""Build a Noodles workflow by sweeping the parameter space"""
results = []
#randomize parameter space to do pseudo load balancing
parameter_space = list(parameter_space)
random.shuffle(parameter_space)
#split parameter space into chunks
work_per_thread = int(
numpy.ceil(len(parameter_space) / float(self.max_threads)))
chunks = _chunk_list(parameter_space, work_per_thread)
for chunk in chunks:
chunked_result = self._run_chunk(chunk, kernel_options,
device_options, tuning_options)
results.append(lift(chunked_result))
return gather(*results)
def lift_object():
a = A()
a.x = add(1, 2)
a.y = sub(9, 11)
return f(noodles.lift(a))
def lift_ordered_dict():
x = OrderedDict()
x['a'] = 1
x['b'] = add(1, 2)
return g(noodles.lift(x))
