def test_pretrained_nodes(self):
"""Test a TestBiFlowNode with two normal pretrained nodes."""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = BiFlowNode(BiFlow([sfa_node, sfa2_node]))
flow = mdp.Flow([flownode])
data_iterables = [[n.random.random((30,10)) for _ in range(6)]]
flow.train(data_iterables)
pretrained_flow = flow[0]._flow
biflownode = BiFlowNode(pretrained_flow)
x = n.random.random([100,10])
biflownode.execute(x)
def test_pretrained_nodes(self):
"""Test a TestBiFlowNode with two normal pretrained nodes."""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = BiFlowNode(BiFlow([sfa_node, sfa2_node]))
flow = mdp.Flow([flownode])
data_iterables = [[n.random.random((30,10)) for _ in range(6)]]
flow.train(data_iterables)
pretrained_flow = flow[0]._flow
biflownode = BiFlowNode(pretrained_flow)
x = n.random.random([100,10])
biflownode.execute(x)
def trace_training(self, path, flow, x, msg=None, stop_msg=None,
trace_name="training", debug=False, **kwargs):
"""Trace a single training phase and the stop_training.
Return a tuple containing a list of the training slide filenames, the
training node ids and the same for stop_training.
path -- Path were the inspection files will be stored.
trace_name -- Name prefix for this inspection (default is training).
**kwargs -- Additional arguments for flow.train can be specified
as keyword arguments.
"""
self._reset()
self._trace_path = path
# train and stop filenames must be different
self._trace_name = trace_name + "_t"
self._flow = flow
self._tracing_decorator.decorate_flow(flow)
biflownode = BiFlowNode(BiFlow(flow.flow))
try:
biflownode.train(x=x, msg=msg, **kwargs)
# reset is important for the following stop_training
biflownode.bi_reset()
# Note: this also catches legacy string exceptions (which are still
# used in numpy, e.g. np.core.multiarray.error)
except:
if debug:
# insert the error slide and encapsulate the exception
traceback.print_exc()
self._write_error_frame()
result = (self._slide_filenames, self._slide_node_ids,
None, None)
raise TraceDebugException(result=result)
else:
raise
train_filenames = self._slide_filenames
train_node_ids = self._slide_node_ids
self._reset()
self._trace_name = trace_name + "_s"
try:
biflownode.stop_training(stop_msg)
except:
if debug:
# insert the error slide and encapsulate the exception
traceback.print_exc()
self._write_error_frame()
result = (train_filenames, train_node_ids,
self._slide_filenames, self._slide_node_ids)
raise TraceDebugException(result=result)
else:
raise
stop_filenames = self._slide_filenames
stop_node_ids = self._slide_node_ids
# restore undecorated flow
self._tracing_decorator.decorate_flow(flow, undecorate_mode=True)
return train_filenames, train_node_ids, stop_filenames, stop_node_ids
def setup_parallel_training(self,
data_iterables,
msg_iterables=None,
stop_messages=None,
train_callable_class=BiFlowTrainCallable):
"""Prepare the flow for handing out tasks to do the training.
After calling setup_parallel_training one has to pick up the
tasks with get_task, run them and finally return the results via
use_results. tasks are available as long as task_available is
True. Training may require multiple phases, which are each closed by
calling use_results.
data_iterables -- A list of iterables, one for each node in the flow.
The iterators returned by the iterables must
return data arrays that are then used for the node training.
See Flow.train for more details.
If a custom train_callable_class is used to preprocess the data
then other data types can be used as well.
msg_iterables - A list of iterables for the messages. Can also be
a single message if data_iterables is a single array.
stop_messages -- Sequence of messages for stop_training.
train_callable_class -- Class used to create training callables for the
scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the train_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
"""
self._bi_reset() # normally not required, just for safety
if self.is_parallel_training:
err = "Parallel training is already underway."
raise ParallelBiFlowException(err)
self._train_callable_class = train_callable_class
data_iterables, msg_iterables = self._sanitize_training_iterables(
data_iterables=data_iterables, msg_iterables=msg_iterables)
self._train_data_iterables = data_iterables
self._train_msg_iterables = msg_iterables
if stop_messages is None:
stop_messages = [None] * len(data_iterables)
self._stop_messages = stop_messages
self._flownode = BiFlowNode(BiFlow(self.flow))
self._i_train_node = 0
self._next_train_phase()
def test_two_nodes1(self):
"""Test a TestBiFlowNode with two normal nodes."""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = BiFlowNode(BiFlow([sfa_node, sfa2_node]))
for _ in range(2):
for _ in range(6):
flownode.train(n.random.random((30,10)))
flownode.stop_training()
x = n.random.random([100,10])
flownode.execute(x)
def test_two_nodes2(self):
"""Test a TestBiFlowNode with two normal nodes using a normal Flow."""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = BiFlowNode(BiFlow([sfa_node, sfa2_node]))
flow = mdp.Flow([flownode])
data_iterables = [[n.random.random((30,10)) for _ in range(6)]]
flow.train(data_iterables)
x = n.random.random([100,10])
flow.execute(x)
def trace_training(self, path, flow, x, msg=None, stop_msg=None,
trace_name="training", debug=False, **kwargs):
"""Trace a single training phase and the stop_training.
Return a tuple containing a list of the training slide filenames, the
training node ids and the same for stop_training.
path -- Path were the inspection files will be stored.
trace_name -- Name prefix for this inspection (default is training).
**kwargs -- Additional arguments for flow.train can be specified
as keyword arguments.
"""
self._reset()
self._trace_path = path
# train and stop filenames must be different
self._trace_name = trace_name + "_t"
self._flow = flow
self._tracing_decorator.decorate_flow(flow)
biflownode = BiFlowNode(BiFlow(flow.flow))
try:
biflownode.train(x=x, msg=msg, **kwargs)
# reset is important for the following stop_training
biflownode.bi_reset()
# Note: this also catches legacy string exceptions (which are still
# used in numpy, e.g. np.core.multiarray.error)
except:
if debug:
# insert the error slide and encapsulate the exception
traceback.print_exc()
self._write_error_frame()
result = (self._slide_filenames, self._slide_node_ids,
None, None)
raise TraceDebugException(result=result)
else:
raise
train_filenames = self._slide_filenames
train_node_ids = self._slide_node_ids
self._reset()
self._trace_name = trace_name + "_s"
try:
biflownode.stop_training(stop_msg)
except:
if debug:
# insert the error slide and encapsulate the exception
traceback.print_exc()
self._write_error_frame()
result = (train_filenames, train_node_ids,
self._slide_filenames, self._slide_node_ids)
raise TraceDebugException(result=result)
else:
raise
stop_filenames = self._slide_filenames
stop_node_ids = self._slide_node_ids
# restore undecorated flow
self._tracing_decorator.decorate_flow(flow, undecorate_mode=True)
return train_filenames, train_node_ids, stop_filenames, stop_node_ids
def test_two_nodes1(self):
"""Test a TestBiFlowNode with two normal nodes."""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = BiFlowNode(BiFlow([sfa_node, sfa2_node]))
for _ in range(2):
for _ in range(6):
flownode.train(n.random.random((30,10)))
flownode.stop_training()
x = n.random.random([100,10])
flownode.execute(x)
def test_use_copies_msg_flownode(self):
"""Test the correct reaction to an outgoing use_copies message."""
stop_result = ({"clonelayer" + MSG_ID_SEP + "use_copies": True},
EXIT_TARGET)
stop_sfa_node = SFABiNode(stop_result=stop_result,
input_dim=10, output_dim=3)
biflownode = BiFlowNode(BiFlow([stop_sfa_node]))
clonelayer = CloneBiLayer(node=biflownode,
n_nodes=3,
use_copies=False,
node_id="clonelayer")
biflow = clonelayer + IdentityBiNode()
x = n.random.random((100,30))
biflow.train(x)
assert clonelayer.use_copies is True
def test_use_copies_msg(self):
"""Test the correct reaction to an outgoing use_copies message."""
stop_result = ({
"clonelayer" + MSG_ID_SEP + "use_copies": True
}, EXIT_TARGET)
stop_sfa_node = SFABiNode(stop_result=stop_result,
input_dim=10,
output_dim=3)
biflownode = BiFlowNode(BiFlow([stop_sfa_node]))
clonelayer = ParallelCloneBiLayer(node=biflownode,
n_nodes=3,
use_copies=False,
node_id="clonelayer")
data = [[n.random.random((100, 30)) for _ in range(5)]]
biflow = ParallelBiFlow([clonelayer])
biflow.train(data, scheduler=mdp.parallel.Scheduler())
assert clonelayer.use_copies is True
def setup_parallel_training(self, data_iterables, msg_iterables=None,
stop_messages=None,
train_callable_class=BiFlowTrainCallable):
"""Prepare the flow for handing out tasks to do the training.
After calling setup_parallel_training one has to pick up the
tasks with get_task, run them and finally return the results via
use_results. tasks are available as long as task_available is
True. Training may require multiple phases, which are each closed by
calling use_results.
data_iterables -- A list of iterables, one for each node in the flow.
The iterators returned by the iterables must
return data arrays that are then used for the node training.
See Flow.train for more details.
If a custom train_callable_class is used to preprocess the data
then other data types can be used as well.
msg_iterables - A list of iterables for the messages. Can also be
a single message if data_iterables is a single array.
stop_messages -- Sequence of messages for stop_training.
train_callable_class -- Class used to create training callables for the
scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the train_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
"""
self._bi_reset() # normally not required, just for safety
if self.is_parallel_training:
err = "Parallel training is already underway."
raise ParallelBiFlowException(err)
self._train_callable_class = train_callable_class
data_iterables, msg_iterables = self._sanitize_training_iterables(
data_iterables=data_iterables,
msg_iterables=msg_iterables)
self._train_data_iterables = data_iterables
self._train_msg_iterables = msg_iterables
if stop_messages is None:
stop_messages = [None] * len(data_iterables)
self._stop_messages = stop_messages
self._flownode = BiFlowNode(BiFlow(self.flow))
self._i_train_node = 0
self._next_train_phase()
def use_results(self, results):
"""Use the result from the scheduler.
During parallel training this will start the next training phase.
For parallel execution this will return the result, like a normal
execute would. In addition it will join any forked nodes.
results -- Iterable containing the results, normally the return value
of scheduler.ResultContainer.get_results().
The individual results can be the return values of the tasks.
"""
if self.is_parallel_training:
for result in results:
self._flownode.join(result)
# perform local stop_training with result check
self._stop_training_hook()
result = self._flownode.stop_training(
self._stop_messages[self._i_train_node])
self._post_stop_training_hook()
if (result is not None):
target = result[2]
# values of +1, -1 and EXIT_TARGET are tolerated
if target not in [1, -1, EXIT_TARGET]:
err = ("Target node not found in flow during " +
"stop_training phase, last result: " +
str(result))
raise BiFlowException(err)
self._flownode.bi_reset()
if self.verbose:
print ("finished parallel training phase of node no. " +
"%d in parallel flow" % (self._i_train_node+1))
if not self.flow[self._i_train_node].is_training():
self._i_train_node += 1
self._next_train_phase()
elif self.is_parallel_executing:
self._exec_data_iterator = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
y_results = []
msg_results = MessageResultContainer()
# use internal flownode to join all biflownodes
self._flownode = BiFlowNode(BiFlow(self.flow))
for result_tuple in results:
result, forked_biflownode = result_tuple
# consolidate results
if isinstance(result, tuple) and (len(result) == 2):
y, msg = result
msg_results.add_message(msg)
else:
y = result
if y is not None:
try:
y_results.append(y)
except:
err = "Some but not all y return values were None."
raise BiFlowException(err)
else:
y_results = None
# join biflownode
if forked_biflownode is not None:
self._flownode.join(forked_biflownode)
# return results
if y_results is not None:
y_results = n.concatenate(y_results)
return (y_results, msg_results.get_message())
else:
err = "It seems that there are no results to retrieve."
raise BiFlowException(err)
def execute(self, iterable=None, msg_iterable=None, target_iterable=None,
scheduler=None,
execute_callable_class=None,
overwrite_result_container=True):
"""Execute the flow and return (y, msg).
If a scheduler is provided the execution will be done in parallel on
the scheduler.
iterable -- Single array or iterable.
msg_iterable -- Single message or iterable.
target_iterable -- Single target or iterable.
scheduler -- Value can be either None for normal execution (default
value) or a Scheduler instance for parallel execution with the
scheduler.
execute_callable_class -- Class used to create execution callables for
the scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the execute_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
overwrite_result_container -- If set to True (default value) then
the result container in the scheduler will be overwritten with an
instance of OrderedResultContainer, if it is not already an
instance of OrderedResultContainer.
"""
if self.is_parallel_training:
raise ParallelBiFlowException("Parallel training is underway.")
if scheduler is None:
if execute_callable_class is not None:
err = ("A execute_callable_class was specified but no "
"scheduler was given, so the execute_callable_class "
"has no effect.")
raise ParallelBiFlowException(err)
return super(ParallelBiFlow, self).execute(iterable, msg_iterable,
target_iterable)
if execute_callable_class is None:
execute_callable_class = BiFlowExecuteCallable
# check that the scheduler is compatible
if overwrite_result_container:
if not isinstance(scheduler.result_container,
parallel.ExecuteResultContainer):
scheduler.result_container = parallel.ExecuteResultContainer()
# do parallel execution
self._flownode = BiFlowNode(BiFlow(self.flow))
try:
self.setup_parallel_execution(
iterable=iterable,
msg_iterable=msg_iterable,
target_iterable=target_iterable,
execute_callable_class=execute_callable_class)
while self.task_available:
task = self.get_task()
scheduler.add_task(*task)
result = self.use_results(scheduler.get_results())
finally:
# reset remaining iterator references, which cannot be pickled
self._exec_data_iterator = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
return result
class ParallelBiFlow(BiFlow, parallel.ParallelFlow):
"""A parallel provides the tasks for parallel training.
Note that even though a node input x or output y can be None, the data
iterables cannot be None themselves, since they define the iterator length
for the message iterator as well. They can, however, return None for each
iteration step.
"""
def __init__(self, flow, verbose=False, **kwargs):
"""Initialize the internal variables."""
self._train_msg_iterables = None
self._train_msg_iterator = None
self._stop_messages = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
super(ParallelBiFlow, self).__init__(flow, verbose=verbose, **kwargs)
@mdp.with_extension("parallel")
def train(self, data_iterables, msg_iterables=None,
stop_messages=None,
scheduler=None,
train_callable_class=None,
overwrite_result_container=True,
**kwargs):
"""Parallel version of the standard train method.
If a scheduler is provided the training will be done in parallel on the
scheduler.
data_iterables -- A list of iterables, one for each node in the flow.
The iterators returned by the iterables must
return data arrays that are then used for the node training.
See Flow.train for more details.
If a custom train_callable_class is used to preprocess the data
then other data types can be used as well.
msg_iterables - A list of iterables for the messages.
stop_messages -- Sequence of messages for stop_training.
scheduler -- Value can be either None for normal training (default
value) or a Scheduler instance for parallel training with the
scheduler.
If the scheduler value is an iterable or iterator then it is
assumed that it contains a scheduler for each training phase.
After a node has been trained the scheduler is shutdown. Note that
you can e.g. use a generator to create the schedulers just in time.
For nodes which are not trained the scheduler can be None.
train_callable_class -- Class used to create training callables for the
scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the train_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
overwrite_result_container -- If set to True (default value) then
the result container in the scheduler will be overwritten with an
instance of NodeResultContainer, if it is not already an instance
of NodeResultContainer.
"""
if self.is_parallel_training:
raise ParallelBiFlowException("Parallel training is underway.")
if scheduler is None:
if train_callable_class is not None:
err = ("A train_callable_class was specified but no scheduler "
"was given, so the train_callable_class has no effect.")
raise ParallelBiFlowException(err)
super(ParallelBiFlow, self).train(data_iterables, msg_iterables,
stop_messages,
**kwargs)
else:
if train_callable_class is None:
train_callable_class = BiFlowTrainCallable
schedulers = None
# do parallel training
try:
self.setup_parallel_training(
data_iterables=data_iterables,
msg_iterables=msg_iterables,
stop_messages=stop_messages,
train_callable_class=train_callable_class,
**kwargs)
# prepare scheduler
if not isinstance(scheduler, parallel.Scheduler):
# scheduler contains an iterable with the schedulers
# self._i_train_node was set in setup_parallel_training
schedulers = iter(scheduler)
scheduler = next(schedulers)
if self._i_train_node > 0:
# dispose schedulers for pretrained nodes
for _ in range(self._i_train_node):
if scheduler is not None:
scheduler.shutdown()
scheduler = next(schedulers)
elif self._i_train_node is None:
# all nodes are already trained, dispose schedulers
for _ in range(len(self.flow) - 1):
if scheduler is not None:
scheduler.shutdown()
# the last scheduler will be shutdown in finally
scheduler = next(schedulers)
last_trained_node = self._i_train_node
else:
schedulers = None
# check that the scheduler is compatible
if ((scheduler is not None) and
overwrite_result_container and
(not isinstance(scheduler.result_container,
parallel.TrainResultContainer))):
scheduler.result_container = \
parallel.TrainResultContainer()
while self.is_parallel_training:
while self.task_available:
task = self.get_task()
scheduler.add_task(*task)
results = scheduler.get_results()
if results == []:
err = ("Could not get any training tasks or results "
"for the current training phase.")
raise Exception(err)
else:
self.use_results(results)
# check if we have to switch to next scheduler
if ((schedulers is not None) and
(self._i_train_node > last_trained_node)):
# dispose unused schedulers
for _ in range(self._i_train_node - last_trained_node):
if scheduler is not None:
scheduler.shutdown()
scheduler = next(schedulers)
last_trained_node = self._i_train_node
# check that the scheduler is compatible
if ((scheduler is not None) and
overwrite_result_container and
(not isinstance(scheduler.result_container,
parallel.TrainResultContainer))):
scheduler.result_container = \
parallel.TrainResultContainer()
finally:
# reset remaining iterator references, which cannot be pickled
self._train_data_iterator = None
self._train_msg_iterator = None
if (schedulers is not None) and (scheduler is not None):
scheduler.shutdown()
def setup_parallel_training(self, data_iterables, msg_iterables=None,
stop_messages=None,
train_callable_class=BiFlowTrainCallable):
"""Prepare the flow for handing out tasks to do the training.
After calling setup_parallel_training one has to pick up the
tasks with get_task, run them and finally return the results via
use_results. tasks are available as long as task_available is
True. Training may require multiple phases, which are each closed by
calling use_results.
data_iterables -- A list of iterables, one for each node in the flow.
The iterators returned by the iterables must
return data arrays that are then used for the node training.
See Flow.train for more details.
If a custom train_callable_class is used to preprocess the data
then other data types can be used as well.
msg_iterables - A list of iterables for the messages. Can also be
a single message if data_iterables is a single array.
stop_messages -- Sequence of messages for stop_training.
train_callable_class -- Class used to create training callables for the
scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the train_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
"""
self._bi_reset() # normally not required, just for safety
if self.is_parallel_training:
err = "Parallel training is already underway."
raise ParallelBiFlowException(err)
self._train_callable_class = train_callable_class
data_iterables, msg_iterables = self._sanitize_training_iterables(
data_iterables=data_iterables,
msg_iterables=msg_iterables)
self._train_data_iterables = data_iterables
self._train_msg_iterables = msg_iterables
if stop_messages is None:
stop_messages = [None] * len(data_iterables)
self._stop_messages = stop_messages
self._flownode = BiFlowNode(BiFlow(self.flow))
self._i_train_node = 0
self._next_train_phase()
def _next_train_phase(self):
"""Find the next phase or node for parallel training.
When it is found the corresponding internal variables are set.
Nodes which are not derived from ParallelNode are trained locally.
If a fork() fails due to a TrainingPhaseNotParallelException
in a certain train phase, then the training is done locally as well
(but fork() is tested again for the next phase).
"""
# find next node that can be forked, if required do local training
while self._i_train_node < len(self.flow):
current_node = self.flow[self._i_train_node]
if not current_node.is_training():
self._i_train_node += 1
continue
iterable = self._train_data_iterables[self._i_train_node]
msg_iterable = self._train_msg_iterables[self._i_train_node]
iterable, msg_iterable, _ = self._sanitize_iterables(iterable,
msg_iterable)
try:
self._flownode.fork()
# fork successful, prepare parallel training
if self.verbose:
print ("start parallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node+1))
self._train_data_iterator = iter(iterable)
self._train_msg_iterator = iter(msg_iterable)
first_task = self._create_train_task()
# make sure that iterator is not empty
if first_task is None:
if current_node.get_current_train_phase() == 1:
err_str = ("The training data iteration for node "
"no. %d could not be repeated for the "
"second training phase, you probably "
"provided an iterator instead of an "
"iterable." % (self._i_train_node+1))
raise mdp.FlowException(err_str)
else:
err_str = ("The training data iterator for node "
"no. %d is empty." % (self._i_train_node+1))
raise mdp.FlowException(err_str)
task_data_chunk = first_task[0]
if task_data_chunk is None:
err = "Training data iterator is empty."
raise ParallelBiFlowException(err)
# Only first task contains the new callable (enable caching).
# A fork is not required here, since the callable is always
# forked in the scheduler.
self._next_task = (task_data_chunk,
self._train_callable_class(self._flownode,
purge_nodes=True))
break
except parallel.NotForkableParallelException as exception:
if self.verbose:
print ("could not fork node no. %d: %s" %
(self._i_train_node + 1, str(exception)))
print ("start nonparallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node+1))
self._local_train_phase(iterable, msg_iterable)
if self.verbose:
print ("finished nonparallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node+1))
if not self.flow[self._i_train_node].is_training():
self._i_train_node += 1
else:
# training is finished
self._i_train_node = None
def _local_train_phase(self, iterable, msg_iterable):
"""Perform a single training phase locally.
The internal _train_callable_class is used for the training.
"""
task_callable = self._train_callable_class(self._flownode,
purge_nodes=False)
i_task = 0
for (x, msg) in zip(iterable, msg_iterable):
i_task += 1
# Note: if x contains additional args assume that the
# callable can handle this
task_callable((x, msg))
if self.verbose:
print (" finished nonparallel task no. %d" % i_task)
# perform stop_training with result check
self._stop_training_hook()
result = self._flownode.stop_training(
self._stop_messages[self._i_train_node])
self._post_stop_training_hook()
if (result is not None) and (not isinstance(result, dict)):
if (isinstance(result, tuple) and
(result[2] in [1, -1, EXIT_TARGET])):
pass
else:
err = ("Target node not found in flow during " +
"stop_training phase, last result: " +
str(result))
raise BiFlowException(err)
self._bi_reset()
def _create_train_task(self):
"""Create and return a single training task without callable.
Returns None if data iterator end is reached.
Raises NoTaskException if any other problem arises.
"""
try:
x = next(self._train_data_iterator)
msg = next(self._train_msg_iterator)
return ((x, msg), None)
except StopIteration:
return None
@mdp.with_extension("parallel") # needed for fork in local scheduler
def execute(self, iterable=None, msg_iterable=None, target_iterable=None,
scheduler=None,
execute_callable_class=None,
overwrite_result_container=True):
"""Execute the flow and return (y, msg).
If a scheduler is provided the execution will be done in parallel on
the scheduler.
iterable -- Single array or iterable.
msg_iterable -- Single message or iterable.
target_iterable -- Single target or iterable.
scheduler -- Value can be either None for normal execution (default
value) or a Scheduler instance for parallel execution with the
scheduler.
execute_callable_class -- Class used to create execution callables for
the scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the execute_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
overwrite_result_container -- If set to True (default value) then
the result container in the scheduler will be overwritten with an
instance of OrderedResultContainer, if it is not already an
instance of OrderedResultContainer.
"""
if self.is_parallel_training:
raise ParallelBiFlowException("Parallel training is underway.")
if scheduler is None:
if execute_callable_class is not None:
err = ("A execute_callable_class was specified but no "
"scheduler was given, so the execute_callable_class "
"has no effect.")
raise ParallelBiFlowException(err)
return super(ParallelBiFlow, self).execute(iterable, msg_iterable,
target_iterable)
if execute_callable_class is None:
execute_callable_class = BiFlowExecuteCallable
# check that the scheduler is compatible
if overwrite_result_container:
if not isinstance(scheduler.result_container,
parallel.ExecuteResultContainer):
scheduler.result_container = parallel.ExecuteResultContainer()
# do parallel execution
self._flownode = BiFlowNode(BiFlow(self.flow))
try:
self.setup_parallel_execution(
iterable=iterable,
msg_iterable=msg_iterable,
target_iterable=target_iterable,
execute_callable_class=execute_callable_class)
while self.task_available:
task = self.get_task()
scheduler.add_task(*task)
result = self.use_results(scheduler.get_results())
finally:
# reset remaining iterator references, which cannot be pickled
self._exec_data_iterator = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
return result
def setup_parallel_execution(self, iterable, msg_iterable=None,
target_iterable=None,
execute_callable_class=BiFlowExecuteCallable):
"""Prepare the flow for handing out tasks to do the execution.
Instead of automatically executing the _flow with the iterable, it only
prepares the tasks for the scheduler.
iterable -- Single array or iterable.
msg_iterable -- Single message or iterable.
target_iterable -- Single target or iterable.
execute_callable_class -- Class used to create execution callables for
the scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the execute_callable_class is only used if a scheduler
was provided. If a scheduler is provided the default class used is
NodeResultContainer.
"""
self._bi_reset() # normally not required, just for safety
if self.is_parallel_training:
raise ParallelBiFlowException("Parallel training is underway.")
self._execute_callable_class = execute_callable_class
iterable, msg_iterable, target_iterable = self._sanitize_iterables(
iterable,
msg_iterable,
target_iterable)
self._exec_data_iterator = iter(iterable)
self._exec_msg_iterator = iter(msg_iterable)
self._exec_target_iterator = iter(target_iterable)
first_task = self._create_execute_task()
if first_task is None:
err = ("The execute data iterable is empty.")
raise mdp.FlowException(err)
task_data_chunk = first_task[0]
if task_data_chunk is None:
err = "Execution data iterable is empty."
raise ParallelBiFlowException(err)
# Only first task contains the new callable (enable caching).
# A fork is not required here, since the callable is always
# forked in the scheduler.
self._next_task = (task_data_chunk,
self._execute_callable_class(self._flownode,
purge_nodes=True))
def _create_execute_task(self):
"""Create and return a single execution task.
Returns None if data iterator end is reached.
Raises NoTaskException if no task is available.
"""
try:
x = next(self._exec_data_iterator)
msg = next(self._exec_msg_iterator)
target = next(self._exec_target_iterator)
return ((x, msg, target), None)
except StopIteration:
return None
def use_results(self, results):
"""Use the result from the scheduler.
During parallel training this will start the next training phase.
For parallel execution this will return the result, like a normal
execute would. In addition it will join any forked nodes.
results -- Iterable containing the results, normally the return value
of scheduler.ResultContainer.get_results().
The individual results can be the return values of the tasks.
"""
if self.is_parallel_training:
for result in results:
self._flownode.join(result)
# perform local stop_training with result check
self._stop_training_hook()
result = self._flownode.stop_training(
self._stop_messages[self._i_train_node])
self._post_stop_training_hook()
if (result is not None):
target = result[2]
# values of +1, -1 and EXIT_TARGET are tolerated
if target not in [1, -1, EXIT_TARGET]:
err = ("Target node not found in flow during " +
"stop_training phase, last result: " +
str(result))
raise BiFlowException(err)
self._flownode.bi_reset()
if self.verbose:
print ("finished parallel training phase of node no. " +
"%d in parallel flow" % (self._i_train_node+1))
if not self.flow[self._i_train_node].is_training():
self._i_train_node += 1
self._next_train_phase()
elif self.is_parallel_executing:
self._exec_data_iterator = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
y_results = []
msg_results = MessageResultContainer()
# use internal flownode to join all biflownodes
self._flownode = BiFlowNode(BiFlow(self.flow))
for result_tuple in results:
result, forked_biflownode = result_tuple
# consolidate results
if isinstance(result, tuple) and (len(result) == 2):
y, msg = result
msg_results.add_message(msg)
else:
y = result
if y is not None:
try:
y_results.append(y)
except:
err = "Some but not all y return values were None."
raise BiFlowException(err)
else:
y_results = None
# join biflownode
if forked_biflownode is not None:
self._flownode.join(forked_biflownode)
# return results
if y_results is not None:
y_results = n.concatenate(y_results)
return (y_results, msg_results.get_message())
else:
err = "It seems that there are no results to retrieve."
raise BiFlowException(err)
def execute(self,
iterable=None,
msg_iterable=None,
target_iterable=None,
scheduler=None,
execute_callable_class=None,
overwrite_result_container=True):
"""Execute the flow and return (y, msg).
If a scheduler is provided the execution will be done in parallel on
the scheduler.
iterable -- Single array or iterable.
msg_iterable -- Single message or iterable.
target_iterable -- Single target or iterable.
scheduler -- Value can be either None for normal execution (default
value) or a Scheduler instance for parallel execution with the
scheduler.
execute_callable_class -- Class used to create execution callables for
the scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the execute_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
overwrite_result_container -- If set to True (default value) then
the result container in the scheduler will be overwritten with an
instance of OrderedResultContainer, if it is not already an
instance of OrderedResultContainer.
"""
if self.is_parallel_training:
raise ParallelBiFlowException("Parallel training is underway.")
if scheduler is None:
if execute_callable_class is not None:
err = ("A execute_callable_class was specified but no "
"scheduler was given, so the execute_callable_class "
"has no effect.")
raise ParallelBiFlowException(err)
return super(ParallelBiFlow, self).execute(iterable, msg_iterable,
target_iterable)
if execute_callable_class is None:
execute_callable_class = BiFlowExecuteCallable
# check that the scheduler is compatible
if overwrite_result_container:
if not isinstance(scheduler.result_container,
parallel.ExecuteResultContainer):
scheduler.result_container = parallel.ExecuteResultContainer()
# do parallel execution
self._flownode = BiFlowNode(BiFlow(self.flow))
try:
self.setup_parallel_execution(
iterable=iterable,
msg_iterable=msg_iterable,
target_iterable=target_iterable,
execute_callable_class=execute_callable_class)
while self.task_available:
task = self.get_task()
scheduler.add_task(*task)
result = self.use_results(scheduler.get_results())
finally:
# reset remaining iterator references, which cannot be pickled
self._exec_data_iterator = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
return result
def use_results(self, results):
"""Use the result from the scheduler.
During parallel training this will start the next training phase.
For parallel execution this will return the result, like a normal
execute would. In addition it will join any forked nodes.
results -- Iterable containing the results, normally the return value
of scheduler.ResultContainer.get_results().
The individual results can be the return values of the tasks.
"""
if self.is_parallel_training:
for result in results:
self._flownode.join(result)
# perform local stop_training with result check
self._stop_training_hook()
result = self._flownode.stop_training(
self._stop_messages[self._i_train_node])
self._post_stop_training_hook()
if (result is not None):
target = result[2]
# values of +1, -1 and EXIT_TARGET are tolerated
if target not in [1, -1, EXIT_TARGET]:
err = ("Target node not found in flow during " +
"stop_training phase, last result: " + str(result))
raise BiFlowException(err)
self._flownode.bi_reset()
if self.verbose:
print("finished parallel training phase of node no. " +
"%d in parallel flow" % (self._i_train_node + 1))
if not self.flow[self._i_train_node].is_training():
self._i_train_node += 1
self._next_train_phase()
elif self.is_parallel_executing:
self._exec_data_iterator = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
y_results = []
msg_results = MessageResultContainer()
# use internal flownode to join all biflownodes
self._flownode = BiFlowNode(BiFlow(self.flow))
for result_tuple in results:
result, forked_biflownode = result_tuple
# consolidate results
if isinstance(result, tuple) and (len(result) == 2):
y, msg = result
msg_results.add_message(msg)
else:
y = result
if y is not None:
try:
y_results.append(y)
except:
err = "Some but not all y return values were None."
raise BiFlowException(err)
else:
y_results = None
# join biflownode
if forked_biflownode is not None:
self._flownode.join(forked_biflownode)
# return results
if y_results is not None:
y_results = n.concatenate(y_results)
return (y_results, msg_results.get_message())
else:
err = "It seems that there are no results to retrieve."
raise BiFlowException(err)
class ParallelBiFlow(BiFlow, parallel.ParallelFlow):
"""A parallel provides the tasks for parallel training.
Note that even though a node input x or output y can be None, the data
iterables cannot be None themselves, since they define the iterator length
for the message iterator as well. They can, however, return None for each
iteration step.
"""
def __init__(self, flow, verbose=False, **kwargs):
"""Initialize the internal variables."""
self._train_msg_iterables = None
self._train_msg_iterator = None
self._stop_messages = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
super(ParallelBiFlow, self).__init__(flow, verbose=verbose, **kwargs)
@mdp.with_extension("parallel")
def train(self,
data_iterables,
msg_iterables=None,
stop_messages=None,
scheduler=None,
train_callable_class=None,
overwrite_result_container=True,
**kwargs):
"""Parallel version of the standard train method.
If a scheduler is provided the training will be done in parallel on the
scheduler.
data_iterables -- A list of iterables, one for each node in the flow.
The iterators returned by the iterables must
return data arrays that are then used for the node training.
See Flow.train for more details.
If a custom train_callable_class is used to preprocess the data
then other data types can be used as well.
msg_iterables - A list of iterables for the messages.
stop_messages -- Sequence of messages for stop_training.
scheduler -- Value can be either None for normal training (default
value) or a Scheduler instance for parallel training with the
scheduler.
If the scheduler value is an iterable or iterator then it is
assumed that it contains a scheduler for each training phase.
After a node has been trained the scheduler is shutdown. Note that
you can e.g. use a generator to create the schedulers just in time.
For nodes which are not trained the scheduler can be None.
train_callable_class -- Class used to create training callables for the
scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the train_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
overwrite_result_container -- If set to True (default value) then
the result container in the scheduler will be overwritten with an
instance of NodeResultContainer, if it is not already an instance
of NodeResultContainer.
"""
if self.is_parallel_training:
raise ParallelBiFlowException("Parallel training is underway.")
if scheduler is None:
if train_callable_class is not None:
err = ("A train_callable_class was specified but no scheduler "
"was given, so the train_callable_class has no effect.")
raise ParallelBiFlowException(err)
super(ParallelBiFlow, self).train(data_iterables, msg_iterables,
stop_messages, **kwargs)
else:
if train_callable_class is None:
train_callable_class = BiFlowTrainCallable
schedulers = None
# do parallel training
try:
self.setup_parallel_training(
data_iterables=data_iterables,
msg_iterables=msg_iterables,
stop_messages=stop_messages,
train_callable_class=train_callable_class,
**kwargs)
# prepare scheduler
if not isinstance(scheduler, parallel.Scheduler):
# scheduler contains an iterable with the schedulers
# self._i_train_node was set in setup_parallel_training
schedulers = iter(scheduler)
scheduler = next(schedulers)
if self._i_train_node > 0:
# dispose schedulers for pretrained nodes
for _ in range(self._i_train_node):
if scheduler is not None:
scheduler.shutdown()
scheduler = next(schedulers)
elif self._i_train_node is None:
# all nodes are already trained, dispose schedulers
for _ in range(len(self.flow) - 1):
if scheduler is not None:
scheduler.shutdown()
# the last scheduler will be shutdown in finally
scheduler = next(schedulers)
last_trained_node = self._i_train_node
else:
schedulers = None
# check that the scheduler is compatible
if ((scheduler is not None) and overwrite_result_container
and (not isinstance(scheduler.result_container,
parallel.TrainResultContainer))):
scheduler.result_container = \
parallel.TrainResultContainer()
while self.is_parallel_training:
while self.task_available:
task = self.get_task()
scheduler.add_task(*task)
results = scheduler.get_results()
if results == []:
err = ("Could not get any training tasks or results "
"for the current training phase.")
raise Exception(err)
else:
self.use_results(results)
# check if we have to switch to next scheduler
if ((schedulers is not None)
and (self._i_train_node > last_trained_node)):
# dispose unused schedulers
for _ in range(self._i_train_node - last_trained_node):
if scheduler is not None:
scheduler.shutdown()
scheduler = next(schedulers)
last_trained_node = self._i_train_node
# check that the scheduler is compatible
if ((scheduler is not None)
and overwrite_result_container and
(not isinstance(scheduler.result_container,
parallel.TrainResultContainer))):
scheduler.result_container = \
parallel.TrainResultContainer()
finally:
# reset remaining iterator references, which cannot be pickled
self._train_data_iterator = None
self._train_msg_iterator = None
if (schedulers is not None) and (scheduler is not None):
scheduler.shutdown()
def setup_parallel_training(self,
data_iterables,
msg_iterables=None,
stop_messages=None,
train_callable_class=BiFlowTrainCallable):
"""Prepare the flow for handing out tasks to do the training.
After calling setup_parallel_training one has to pick up the
tasks with get_task, run them and finally return the results via
use_results. tasks are available as long as task_available is
True. Training may require multiple phases, which are each closed by
calling use_results.
data_iterables -- A list of iterables, one for each node in the flow.
The iterators returned by the iterables must
return data arrays that are then used for the node training.
See Flow.train for more details.
If a custom train_callable_class is used to preprocess the data
then other data types can be used as well.
msg_iterables - A list of iterables for the messages. Can also be
a single message if data_iterables is a single array.
stop_messages -- Sequence of messages for stop_training.
train_callable_class -- Class used to create training callables for the
scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the train_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
"""
self._bi_reset() # normally not required, just for safety
if self.is_parallel_training:
err = "Parallel training is already underway."
raise ParallelBiFlowException(err)
self._train_callable_class = train_callable_class
data_iterables, msg_iterables = self._sanitize_training_iterables(
data_iterables=data_iterables, msg_iterables=msg_iterables)
self._train_data_iterables = data_iterables
self._train_msg_iterables = msg_iterables
if stop_messages is None:
stop_messages = [None] * len(data_iterables)
self._stop_messages = stop_messages
self._flownode = BiFlowNode(BiFlow(self.flow))
self._i_train_node = 0
self._next_train_phase()
def _next_train_phase(self):
"""Find the next phase or node for parallel training.
When it is found the corresponding internal variables are set.
Nodes which are not derived from ParallelNode are trained locally.
If a fork() fails due to a TrainingPhaseNotParallelException
in a certain train phase, then the training is done locally as well
(but fork() is tested again for the next phase).
"""
# find next node that can be forked, if required do local training
while self._i_train_node < len(self.flow):
current_node = self.flow[self._i_train_node]
if not current_node.is_training():
self._i_train_node += 1
continue
iterable = self._train_data_iterables[self._i_train_node]
msg_iterable = self._train_msg_iterables[self._i_train_node]
iterable, msg_iterable, _ = self._sanitize_iterables(
iterable, msg_iterable)
try:
self._flownode.fork()
# fork successful, prepare parallel training
if self.verbose:
print("start parallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node + 1))
self._train_data_iterator = iter(iterable)
self._train_msg_iterator = iter(msg_iterable)
first_task = self._create_train_task()
# make sure that iterator is not empty
if first_task is None:
if current_node.get_current_train_phase() == 1:
err_str = ("The training data iteration for node "
"no. %d could not be repeated for the "
"second training phase, you probably "
"provided an iterator instead of an "
"iterable." % (self._i_train_node + 1))
raise mdp.FlowException(err_str)
else:
err_str = ("The training data iterator for node "
"no. %d is empty." %
(self._i_train_node + 1))
raise mdp.FlowException(err_str)
task_data_chunk = first_task[0]
if task_data_chunk is None:
err = "Training data iterator is empty."
raise ParallelBiFlowException(err)
# Only first task contains the new callable (enable caching).
# A fork is not required here, since the callable is always
# forked in the scheduler.
self._next_task = (task_data_chunk,
self._train_callable_class(
self._flownode, purge_nodes=True))
break
except parallel.NotForkableParallelException as exception:
if self.verbose:
print("could not fork node no. %d: %s" %
(self._i_train_node + 1, str(exception)))
print("start nonparallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node + 1))
self._local_train_phase(iterable, msg_iterable)
if self.verbose:
print("finished nonparallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node + 1))
if not self.flow[self._i_train_node].is_training():
self._i_train_node += 1
else:
# training is finished
self._i_train_node = None
def _local_train_phase(self, iterable, msg_iterable):
"""Perform a single training phase locally.
The internal _train_callable_class is used for the training.
"""
task_callable = self._train_callable_class(self._flownode,
purge_nodes=False)
i_task = 0
for (x, msg) in zip(iterable, msg_iterable):
i_task += 1
# Note: if x contains additional args assume that the
# callable can handle this
task_callable((x, msg))
if self.verbose:
print(" finished nonparallel task no. %d" % i_task)
# perform stop_training with result check
self._stop_training_hook()
result = self._flownode.stop_training(
self._stop_messages[self._i_train_node])
self._post_stop_training_hook()
if (result is not None) and (not isinstance(result, dict)):
if (isinstance(result, tuple)
and (result[2] in [1, -1, EXIT_TARGET])):
pass
else:
err = ("Target node not found in flow during " +
"stop_training phase, last result: " + str(result))
raise BiFlowException(err)
self._bi_reset()
def _create_train_task(self):
"""Create and return a single training task without callable.
Returns None if data iterator end is reached.
Raises NoTaskException if any other problem arises.
"""
try:
x = next(self._train_data_iterator)
msg = next(self._train_msg_iterator)
return ((x, msg), None)
except StopIteration:
return None
@mdp.with_extension("parallel") # needed for fork in local scheduler
def execute(self,
iterable=None,
msg_iterable=None,
target_iterable=None,
scheduler=None,
execute_callable_class=None,
overwrite_result_container=True):
"""Execute the flow and return (y, msg).
If a scheduler is provided the execution will be done in parallel on
the scheduler.
iterable -- Single array or iterable.
msg_iterable -- Single message or iterable.
target_iterable -- Single target or iterable.
scheduler -- Value can be either None for normal execution (default
value) or a Scheduler instance for parallel execution with the
scheduler.
execute_callable_class -- Class used to create execution callables for
the scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the execute_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
overwrite_result_container -- If set to True (default value) then
the result container in the scheduler will be overwritten with an
instance of OrderedResultContainer, if it is not already an
instance of OrderedResultContainer.
"""
if self.is_parallel_training:
raise ParallelBiFlowException("Parallel training is underway.")
if scheduler is None:
if execute_callable_class is not None:
err = ("A execute_callable_class was specified but no "
"scheduler was given, so the execute_callable_class "
"has no effect.")
raise ParallelBiFlowException(err)
return super(ParallelBiFlow, self).execute(iterable, msg_iterable,
target_iterable)
if execute_callable_class is None:
execute_callable_class = BiFlowExecuteCallable
# check that the scheduler is compatible
if overwrite_result_container:
if not isinstance(scheduler.result_container,
parallel.ExecuteResultContainer):
scheduler.result_container = parallel.ExecuteResultContainer()
# do parallel execution
self._flownode = BiFlowNode(BiFlow(self.flow))
try:
self.setup_parallel_execution(
iterable=iterable,
msg_iterable=msg_iterable,
target_iterable=target_iterable,
execute_callable_class=execute_callable_class)
while self.task_available:
task = self.get_task()
scheduler.add_task(*task)
result = self.use_results(scheduler.get_results())
finally:
# reset remaining iterator references, which cannot be pickled
self._exec_data_iterator = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
return result
def setup_parallel_execution(self,
iterable,
msg_iterable=None,
target_iterable=None,
execute_callable_class=BiFlowExecuteCallable):
"""Prepare the flow for handing out tasks to do the execution.
Instead of automatically executing the _flow with the iterable, it only
prepares the tasks for the scheduler.
iterable -- Single array or iterable.
msg_iterable -- Single message or iterable.
target_iterable -- Single target or iterable.
execute_callable_class -- Class used to create execution callables for
the scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the execute_callable_class is only used if a scheduler
was provided. If a scheduler is provided the default class used is
NodeResultContainer.
"""
self._bi_reset() # normally not required, just for safety
if self.is_parallel_training:
raise ParallelBiFlowException("Parallel training is underway.")
self._execute_callable_class = execute_callable_class
iterable, msg_iterable, target_iterable = self._sanitize_iterables(
iterable, msg_iterable, target_iterable)
self._exec_data_iterator = iter(iterable)
self._exec_msg_iterator = iter(msg_iterable)
self._exec_target_iterator = iter(target_iterable)
first_task = self._create_execute_task()
if first_task is None:
err = ("The execute data iterable is empty.")
raise mdp.FlowException(err)
task_data_chunk = first_task[0]
if task_data_chunk is None:
err = "Execution data iterable is empty."
raise ParallelBiFlowException(err)
# Only first task contains the new callable (enable caching).
# A fork is not required here, since the callable is always
# forked in the scheduler.
self._next_task = (task_data_chunk,
self._execute_callable_class(self._flownode,
purge_nodes=True))
def _create_execute_task(self):
"""Create and return a single execution task.
Returns None if data iterator end is reached.
Raises NoTaskException if no task is available.
"""
try:
x = next(self._exec_data_iterator)
msg = next(self._exec_msg_iterator)
target = next(self._exec_target_iterator)
return ((x, msg, target), None)
except StopIteration:
return None
def use_results(self, results):
"""Use the result from the scheduler.
During parallel training this will start the next training phase.
For parallel execution this will return the result, like a normal
execute would. In addition it will join any forked nodes.
results -- Iterable containing the results, normally the return value
of scheduler.ResultContainer.get_results().
The individual results can be the return values of the tasks.
"""
if self.is_parallel_training:
for result in results:
self._flownode.join(result)
# perform local stop_training with result check
self._stop_training_hook()
result = self._flownode.stop_training(
self._stop_messages[self._i_train_node])
self._post_stop_training_hook()
if (result is not None):
target = result[2]
# values of +1, -1 and EXIT_TARGET are tolerated
if target not in [1, -1, EXIT_TARGET]:
err = ("Target node not found in flow during " +
"stop_training phase, last result: " + str(result))
raise BiFlowException(err)
self._flownode.bi_reset()
if self.verbose:
print("finished parallel training phase of node no. " +
"%d in parallel flow" % (self._i_train_node + 1))
if not self.flow[self._i_train_node].is_training():
self._i_train_node += 1
self._next_train_phase()
elif self.is_parallel_executing:
self._exec_data_iterator = None
self._exec_msg_iterator = None
self._exec_target_iterator = None
y_results = []
msg_results = MessageResultContainer()
# use internal flownode to join all biflownodes
self._flownode = BiFlowNode(BiFlow(self.flow))
for result_tuple in results:
result, forked_biflownode = result_tuple
# consolidate results
if isinstance(result, tuple) and (len(result) == 2):
y, msg = result
msg_results.add_message(msg)
else:
y = result
if y is not None:
try:
y_results.append(y)
except:
err = "Some but not all y return values were None."
raise BiFlowException(err)
else:
y_results = None
# join biflownode
if forked_biflownode is not None:
self._flownode.join(forked_biflownode)
# return results
if y_results is not None:
y_results = n.concatenate(y_results)
return (y_results, msg_results.get_message())
else:
err = "It seems that there are no results to retrieve."
raise BiFlowException(err)
