def _run_interface(self, runtime):
# Get workflow logger for runtime profile error reporting
from nipype import logging
logger = logging.getLogger('workflow')
# Create function handle
function_handle = create_function_from_source(self.inputs.function_str,
self.imports)
# Wrapper for running function handle in multiprocessing.Process
# Can catch exceptions and report output via multiprocessing.Queue
def _function_handle_wrapper(queue, **kwargs):
try:
out = function_handle(**kwargs)
queue.put(out)
except Exception as exc:
queue.put(exc)
# Get function args
args = {}
for name in self._input_names:
value = getattr(self.inputs, name)
if isdefined(value):
args[name] = value
# Profile resources if set
if runtime_profile:
from nipype.interfaces.base import get_max_resources_used
import multiprocessing
# Init communication queue and proc objs
queue = multiprocessing.Queue()
proc = multiprocessing.Process(target=_function_handle_wrapper,
args=(queue,), kwargs=args)
# Init memory and threads before profiling
mem_mb = 0
num_threads = 0
# Start process and profile while it's alive
proc.start()
while proc.is_alive():
mem_mb, num_threads = \
get_max_resources_used(proc.pid, mem_mb, num_threads,
pyfunc=True)
# Get result from process queue
out = queue.get()
# If it is an exception, raise it
if isinstance(out, Exception):
raise out
# Function ran successfully, populate runtime stats
setattr(runtime, 'runtime_memory_gb', mem_mb/1024.0)
setattr(runtime, 'runtime_threads', num_threads)
else:
out = function_handle(**args)
if len(self._output_names) == 1:
self._out[self._output_names[0]] = out
else:
if isinstance(out, tuple) and (len(out) != len(self._output_names)):
raise RuntimeError('Mismatch in number of expected outputs')
else:
for idx, name in enumerate(self._output_names):
self._out[name] = out[idx]
return runtime
def _run_interface(self, runtime):
# Get workflow logger for runtime profile error reporting
from nipype import logging
logger = logging.getLogger('workflow')
# Create function handle
function_handle = create_function_from_source(self.inputs.function_str,
self.imports)
# Wrapper for running function handle in multiprocessing.Process
# Can catch exceptions and report output via multiprocessing.Queue
def _function_handle_wrapper(queue, **kwargs):
try:
out = function_handle(**kwargs)
queue.put(out)
except Exception as exc:
queue.put(exc)
# Get function args
args = {}
for name in self._input_names:
value = getattr(self.inputs, name)
if isdefined(value):
args[name] = value
# Profile resources if set
if runtime_profile:
from nipype.interfaces.base import get_max_resources_used
import multiprocessing
# Init communication queue and proc objs
queue = multiprocessing.Queue()
proc = multiprocessing.Process(target=_function_handle_wrapper,
args=(queue, ),
kwargs=args)
# Init memory and threads before profiling
mem_mb = 0
num_threads = 0
# Start process and profile while it's alive
proc.start()
while proc.is_alive():
mem_mb, num_threads = \
get_max_resources_used(proc.pid, mem_mb, num_threads,
pyfunc=True)
# Get result from process queue
out = queue.get()
# If it is an exception, raise it
if isinstance(out, Exception):
raise out
# Function ran successfully, populate runtime stats
setattr(runtime, 'runtime_memory_gb', mem_mb / 1024.0)
setattr(runtime, 'runtime_threads', num_threads)
else:
out = function_handle(**args)
if len(self._output_names) == 1:
self._out[self._output_names[0]] = out
else:
if isinstance(out,
tuple) and (len(out) != len(self._output_names)):
raise RuntimeError('Mismatch in number of expected outputs')
else:
for idx, name in enumerate(self._output_names):
self._out[name] = out[idx]
return runtime