def __init__(
self, n_neurons, machine_time_step, timescale_factor,
constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0,
duration=None, seed=None):
AbstractPartitionableVertex.__init__(
self, n_atoms=n_neurons, label=label, constraints=constraints,
max_atoms_per_core=self._model_based_max_atoms_per_core)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractSpikeRecordable.__init__(self)
ReceiveBuffersToHostBasicImpl.__init__(self)
AbstractProvidesOutgoingEdgeConstraints.__init__(self)
PopulationSettableChangeRequiresMapping.__init__(self)
# Store the parameters
self._rate = rate
self._start = start
self._duration = duration
self._rng = numpy.random.RandomState(seed)
# Prepare for recording, and to get spikes
self._spike_recorder = SpikeRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
def __init__(self,
n_neurons,
spike_times,
machine_time_step,
spikes_per_second,
ring_buffer_sigma,
timescale_factor,
port=None,
tag=None,
ip_address=None,
board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=None,
space_before_notification=640,
constraints=None,
label="SpikeSourceArray"):
if ip_address is None:
ip_address = config.get("Buffers", "receive_buffer_host")
if port is None:
port = config.getint("Buffers", "receive_buffer_port")
AbstractDataSpecableVertex.__init__(
self,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractPartitionableVertex.__init__(
self,
n_atoms=n_neurons,
label=label,
max_atoms_per_core=self._model_based_max_atoms_per_core,
constraints=constraints)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._spike_times = spike_times
self._max_on_chip_memory_usage_for_spikes = \
max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
self.add_constraint(
TagAllocatorRequireIptagConstraint(ip_address,
port,
strip_sdp=True,
board_address=board_address,
tag_id=tag))
if self._max_on_chip_memory_usage_for_spikes is None:
self._max_on_chip_memory_usage_for_spikes = \
front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP
# check the values do not conflict with chip memory limit
if self._max_on_chip_memory_usage_for_spikes < 0:
raise ConfigurationException(
"The memory usage on chip is either beyond what is supportable"
" on the spinnaker board being supported or you have requested"
" a negative value for a memory usage. Please correct and"
" try again")
# Keep track of any previously generated buffers
self._send_buffers = dict()
def _create_virtual_chip(self, virtual_vertex):
sdram_object = SDRAM()
# creates the two links
spinnaker_link_id = virtual_vertex.get_spinnaker_link_id
spinnaker_link_data = \
self._machine.locate_connected_chips_coords_and_link(
config.getint("Machine", "version"), spinnaker_link_id)
virtual_link_id = (spinnaker_link_data.connected_link + 3) % 6
to_virtual_chip_link = Link(
destination_x=virtual_vertex.virtual_chip_x,
destination_y=virtual_vertex.virtual_chip_y,
source_x=spinnaker_link_data.connected_chip_x,
source_y=spinnaker_link_data.connected_chip_y,
multicast_default_from=virtual_link_id,
multicast_default_to=virtual_link_id,
source_link_id=spinnaker_link_data.connected_link)
from_virtual_chip_link = Link(
destination_x=spinnaker_link_data.connected_chip_x,
destination_y=spinnaker_link_data.connected_chip_y,
source_x=virtual_vertex.virtual_chip_x,
source_y=virtual_vertex.virtual_chip_y,
multicast_default_from=(spinnaker_link_data.connected_link),
multicast_default_to=spinnaker_link_data.connected_link,
source_link_id=virtual_link_id)
# create the router
links = [from_virtual_chip_link]
router_object = MachineRouter(
links=links,
emergency_routing_enabled=False,
clock_speed=MachineRouter.ROUTER_DEFAULT_CLOCK_SPEED,
n_available_multicast_entries=sys.maxint)
# create the processors
processors = list()
for virtual_core_id in range(0, 128):
processors.append(
Processor(virtual_core_id, Processor.CPU_AVAILABLE,
virtual_core_id == 0))
# connect the real chip with the virtual one
connected_chip = self._machine.get_chip_at(
spinnaker_link_data.connected_chip_x,
spinnaker_link_data.connected_chip_y)
connected_chip.router.add_link(to_virtual_chip_link)
# return new v chip
return Chip(processors=processors,
router=router_object,
sdram=sdram_object,
x=virtual_vertex.virtual_chip_x,
y=virtual_vertex.virtual_chip_y,
virtual=True,
nearest_ethernet_x=None,
nearest_ethernet_y=None)
def _create_virtual_chip(self, virtual_vertex):
""" Create a virtual chip as a real chip in the spinnmachine machine\
object
:param virtual_vertex: virutal vertex to convert into a real chip
:return: the real chip
"""
sdram_object = SDRAM()
# creates the two links
spinnaker_link_id = virtual_vertex.get_spinnaker_link_id
spinnaker_link_data = \
self._machine.locate_connected_chips_coords_and_link(
config.getint("Machine", "version"), spinnaker_link_id)
virtual_link_id = (spinnaker_link_data.connected_link + 3) % 6
to_virtual_chip_link = Link(
destination_x=virtual_vertex.virtual_chip_x,
destination_y=virtual_vertex.virtual_chip_y,
source_x=spinnaker_link_data.connected_chip_x,
source_y=spinnaker_link_data.connected_chip_y,
multicast_default_from=virtual_link_id,
multicast_default_to=virtual_link_id,
source_link_id=spinnaker_link_data.connected_link)
from_virtual_chip_link = Link(
destination_x=spinnaker_link_data.connected_chip_x,
destination_y=spinnaker_link_data.connected_chip_y,
source_x=virtual_vertex.virtual_chip_x,
source_y=virtual_vertex.virtual_chip_y,
multicast_default_from=(spinnaker_link_data.connected_link),
multicast_default_to=spinnaker_link_data.connected_link,
source_link_id=virtual_link_id)
# create the router
links = [from_virtual_chip_link]
router_object = MachineRouter(
links=links, emergency_routing_enabled=False,
clock_speed=MachineRouter.ROUTER_DEFAULT_CLOCK_SPEED,
n_available_multicast_entries=sys.maxint)
# create the processors
processors = list()
for virtual_core_id in range(0, 128):
processors.append(Processor(virtual_core_id,
Processor.CPU_AVAILABLE,
virtual_core_id == 0))
# connect the real chip with the virtual one
connected_chip = self._machine.get_chip_at(
spinnaker_link_data.connected_chip_x,
spinnaker_link_data.connected_chip_y)
connected_chip.router.add_link(to_virtual_chip_link)
# return new v chip
return Chip(
processors=processors, router=router_object, sdram=sdram_object,
x=virtual_vertex.virtual_chip_x, y=virtual_vertex.virtual_chip_y,
virtual=True, nearest_ethernet_x=None, nearest_ethernet_y=None)
def __init__(
self, n_neurons, spike_times, machine_time_step, timescale_factor,
port=None, tag=None, ip_address=None, board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=None,
space_before_notification=640,
constraints=None, label="SpikeSourceArray"):
if ip_address is None:
ip_address = config.get("Buffers", "receive_buffer_host")
if port is None:
port = config.getint("Buffers", "receive_buffer_port")
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractPartitionableVertex.__init__(
self, n_atoms=n_neurons, label=label,
max_atoms_per_core=self._model_based_max_atoms_per_core,
constraints=constraints)
AbstractSpikeRecordable.__init__(self)
self._spike_times = spike_times
self._max_on_chip_memory_usage_for_spikes = \
max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
self.add_constraint(TagAllocatorRequireIptagConstraint(
ip_address, port, strip_sdp=True, board_address=board_address,
tag_id=tag))
if self._max_on_chip_memory_usage_for_spikes is None:
self._max_on_chip_memory_usage_for_spikes = \
front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP
# check the values do not conflict with chip memory limit
if self._max_on_chip_memory_usage_for_spikes < 0:
raise ConfigurationException(
"The memory usage on chip is either beyond what is supportable"
" on the spinnaker board being supported or you have requested"
" a negative value for a memory usage. Please correct and"
" try again")
if (self._max_on_chip_memory_usage_for_spikes <
self._space_before_notification):
self._space_before_notification =\
self._max_on_chip_memory_usage_for_spikes
# Keep track of any previously generated buffers
self._send_buffers = dict()
self._spike_recording_region_size = None
# handle recording
self._spike_recorder = EIEIOSpikeRecorder(machine_time_step)
#handle outgoing constraints
self._outgoing_edge_key_restrictor = \
OutgoingEdgeSameContiguousKeysRestrictor()
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0,
duration=None, seed=None):
AbstractPartitionableVertex.__init__(
self, n_neurons, label, self._model_based_max_atoms_per_core,
constraints)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
PopulationSettableChangeRequiresMapping.__init__(self)
# Store the parameters
self._rate = utility_calls.convert_param_to_numpy(rate, n_neurons)
self._start = utility_calls.convert_param_to_numpy(start, n_neurons)
self._duration = utility_calls.convert_param_to_numpy(
duration, n_neurons)
self._rng = numpy.random.RandomState(seed)
# Prepare for recording, and to get spikes
self._spike_recorder = MultiSpikeRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
self._receive_buffer_host = config.get(
"Buffers", "receive_buffer_host")
self._receive_buffer_port = config.getint(
"Buffers", "receive_buffer_port")
self._minimum_buffer_sdram = config.getint(
"Buffers", "minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0,
duration=None, seed=None):
AbstractPartitionableVertex.__init__(
self, n_neurons, label, self._model_based_max_atoms_per_core,
constraints)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
PopulationSettableChangeRequiresMapping.__init__(self)
# Store the parameters
self._rate = rate
self._start = start
self._duration = duration
self._rng = numpy.random.RandomState(seed)
# Prepare for recording, and to get spikes
self._spike_recorder = SpikeRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
self._receive_buffer_host = config.get(
"Buffers", "receive_buffer_host")
self._receive_buffer_port = config.getint(
"Buffers", "receive_buffer_port")
self._minimum_buffer_sdram = config.getint(
"Buffers", "minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
def __init__(
self, n_neurons, spike_times, machine_time_step, spikes_per_second,
ring_buffer_sigma, timescale_factor, port=None, tag=None,
ip_address=None, board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=None,
constraints=None, label="SpikeSourceArray"):
if ip_address is None:
ip_address = config.get("Buffers", "receive_buffer_host")
if port is None:
port = config.getint("Buffers", "receive_buffer_port")
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractPartitionableVertex.__init__(
self, n_atoms=n_neurons, label=label,
max_atoms_per_core=self._model_based_max_atoms_per_core,
constraints=constraints)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._spike_times = spike_times
self._max_on_chip_memory_usage_for_spikes = \
max_on_chip_memory_usage_for_spikes_in_bytes
self._threshold_for_reporting_bytes_written = 0
self.add_constraint(TagAllocatorRequireIptagConstraint(
ip_address, port, strip_sdp=True, board_address=board_address,
tag_id=tag))
if self._max_on_chip_memory_usage_for_spikes is None:
self._max_on_chip_memory_usage_for_spikes = 8 * 1024 * 1024
# check the values do not conflict with chip memory limit
if self._max_on_chip_memory_usage_for_spikes < 0:
raise ConfigurationException(
"The memory usage on chip is either beyond what is supportable"
" on the spinnaker board being supported or you have requested"
" a negative value for a memory usage. Please correct and"
" try again")
# Keep track of any previously generated buffers
self._send_buffers = dict()
def __init__(
self, n_neurons, spike_times, machine_time_step, timescale_factor,
port=None, tag=None, ip_address=None, board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=(
constants.SPIKE_BUFFER_SIZE_BUFFERING_IN),
space_before_notification=640,
constraints=None, label="SpikeSourceArray",
spike_recorder_buffer_size=(
constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT),
buffer_size_before_receive=(
constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE)):
self._ip_address = ip_address
if ip_address is None:
self._ip_address = config.get("Buffers", "receive_buffer_host")
self._port = port
if port is None:
self._port = config.getint("Buffers", "receive_buffer_port")
ReverseIpTagMultiCastSource.__init__(
self, n_keys=n_neurons, machine_time_step=machine_time_step,
timescale_factor=timescale_factor, label=label,
constraints=constraints,
max_atoms_per_core=(SpikeSourceArray.
_model_based_max_atoms_per_core),
board_address=board_address,
receive_port=None, receive_sdp_port=None, receive_tag=None,
virtual_key=None, prefix=None, prefix_type=None, check_keys=False,
send_buffer_times=spike_times,
send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes,
send_buffer_space_before_notify=space_before_notification,
send_buffer_notification_ip_address=self._ip_address,
send_buffer_notification_port=self._port,
send_buffer_notification_tag=tag)
AbstractSpikeRecordable.__init__(self)
# handle recording
self._spike_recorder = EIEIOSpikeRecorder(machine_time_step)
self._spike_recorder_buffer_size = spike_recorder_buffer_size
self._buffer_size_before_receive = buffer_size_before_receive
def __init__(self, host_name=None, timestep=None, min_delay=None,
max_delay=None, graph_label=None,
database_socket_addresses=None):
FrontEndCommonConfigurationFunctions.__init__(self, host_name,
graph_label)
SpynnakerConfigurationFunctions.__init__(self)
FrontEndCommonProvenanceFunctions.__init__(self)
self._database_socket_addresses = set()
self._database_interface = None
self._create_database = None
self._populations = list()
if self._app_id is None:
self._set_up_main_objects(
app_id=config.getint("Machine", "appID"),
execute_data_spec_report=config.getboolean(
"Reports", "writeTextSpecs"),
execute_partitioner_report=config.getboolean(
"Reports", "writePartitionerReports"),
execute_placer_report=config.getboolean(
"Reports", "writePlacerReports"),
execute_router_dat_based_report=config.getboolean(
"Reports", "writeRouterDatReport"),
reports_are_enabled=config.getboolean(
"Reports", "reportsEnabled"),
generate_performance_measurements=config.getboolean(
"Reports", "outputTimesForSections"),
execute_router_report=config.getboolean(
"Reports", "writeRouterReports"),
execute_write_reload_steps=config.getboolean(
"Reports", "writeReloadSteps"),
generate_transciever_report=config.getboolean(
"Reports", "writeTransceiverReport"),
execute_routing_info_report=config.getboolean(
"Reports", "writeRouterInfoReport"),
in_debug_mode=config.get("Mode", "mode") == "Debug",
generate_tag_report=config.getboolean(
"Reports", "writeTagAllocationReports"))
self._set_up_pacman_algorthms_listings(
partitioner_algorithm=config.get("Partitioner", "algorithm"),
placer_algorithm=config.get("Placer", "algorithm"),
key_allocator_algorithm=config.get(
"KeyAllocator", "algorithm"),
routing_algorithm=config.get("Routing", "algorithm"))
# set up exeuctable specifics
self._set_up_executable_specifics()
self._set_up_report_specifics(
default_report_file_path=config.get(
"Reports", "defaultReportFilePath"),
max_reports_kept=config.getint("Reports", "max_reports_kept"),
reports_are_enabled=config.getboolean(
"Reports", "reportsEnabled"),
write_provance_data=config.getboolean(
"Reports", "writeProvanceData"),
write_text_specs=config.getboolean(
"Reports", "writeTextSpecs"))
self._set_up_output_application_data_specifics(
max_application_binaries_kept=config.getint(
"Reports", "max_application_binaries_kept"),
where_to_write_application_data_files=config.get(
"Reports", "defaultApplicationDataFilePath"))
# set up spynnaker specifics, such as setting the machineName from conf
self._set_up_machine_specifics(
timestep, min_delay, max_delay, host_name)
self._spikes_per_second = float(config.getfloat(
"Simulation", "spikes_per_second"))
self._ring_buffer_sigma = float(config.getfloat(
"Simulation", "ring_buffer_sigma"))
# Determine default executable folder location
# and add this default to end of list of search paths
executable_finder.add_path(os.path.dirname(model_binaries.__file__))
FrontEndCommonInterfaceFunctions.__init__(
self, self._reports_states, self._report_default_directory,
self._app_data_runtime_folder)
logger.info("Setting time scale factor to {}."
.format(self._time_scale_factor))
logger.info("Setting appID to %d." % self._app_id)
# get the machine time step
logger.info("Setting machine time step to {} micro-seconds."
.format(self._machine_time_step))
self._edge_count = 0
# Manager of buffered sending
self._send_buffer_manager = None
def _set_up_timings(self, timestep, min_delay, max_delay):
self._machine_time_step = config.getint("Machine", "machineTimeStep")
# deal with params allowed via the setup options
if timestep is not None:
# convert into milliseconds from microseconds
timestep *= 1000
self._machine_time_step = timestep
if min_delay is not None and float(min_delay * 1000) < 1.0 * timestep:
raise common_exceptions.ConfigurationException(
"Pacman does not support min delays below {} ms with the "
"current machine time step"
.format(constants.MIN_SUPPORTED_DELAY * timestep))
natively_supported_delay_for_models = \
constants.MAX_SUPPORTED_DELAY_TICS
delay_extension_max_supported_delay = \
constants.MAX_DELAY_BLOCKS \
* constants.MAX_TIMER_TICS_SUPPORTED_PER_BLOCK
max_delay_tics_supported = \
natively_supported_delay_for_models + \
delay_extension_max_supported_delay
if max_delay is not None\
and float(max_delay * 1000) > max_delay_tics_supported * timestep:
raise common_exceptions.ConfigurationException(
"Pacman does not support max delays above {} ms with the "
"current machine time step".format(0.144 * timestep))
if min_delay is not None:
self._min_supported_delay = min_delay
else:
self._min_supported_delay = timestep / 1000.0
if max_delay is not None:
self._max_supported_delay = max_delay
else:
self._max_supported_delay = (max_delay_tics_supported *
(timestep / 1000.0))
if (config.has_option("Machine", "timeScaleFactor") and
config.get("Machine", "timeScaleFactor") != "None"):
self._time_scale_factor = \
config.getint("Machine", "timeScaleFactor")
if timestep * self._time_scale_factor < 1000:
if config.getboolean(
"Mode", "violate_1ms_wall_clock_restriction"):
logger.warn(
"****************************************************")
logger.warn(
"*** The combination of simulation time step and ***")
logger.warn(
"*** the machine time scale factor results in a ***")
logger.warn(
"*** wall clock timer tick that is currently not ***")
logger.warn(
"*** reliably supported by the spinnaker machine. ***")
logger.warn(
"****************************************************")
else:
raise common_exceptions.ConfigurationException(
"The combination of simulation time step and the"
" machine time scale factor results in a wall clock "
"timer tick that is currently not reliably supported "
"by the spinnaker machine. If you would like to "
"override this behaviour (at your own risk), please "
"add violate_1ms_wall_clock_restriction = True to the "
"[Mode] section of your .spynnaker.cfg file")
else:
self._time_scale_factor = max(1,
math.ceil(1000.0 / float(timestep)))
if self._time_scale_factor > 1:
logger.warn("A timestep was entered that has forced sPyNNaker "
"to automatically slow the simulation down from "
"real time by a factor of {}. To remove this "
"automatic behaviour, please enter a "
"timescaleFactor value in your .spynnaker.cfg"
.format(self._time_scale_factor))
def _create_pacman_executor_inputs(
self, this_run_time, is_resetting=False):
application_graph_changed = \
self._detect_if_graph_has_changed(not is_resetting)
inputs = list()
# file path to store any provenance data to
provenance_file_path = \
os.path.join(self._report_default_directory, "provance_data")
if not os.path.exists(provenance_file_path):
os.mkdir(provenance_file_path)
# all modes need the NoSyncChanges
if application_graph_changed:
self._no_sync_changes = 0
inputs.append(
{'type': "NoSyncChanges", 'value': self._no_sync_changes})
# support resetting the machine during start up
if (config.getboolean("Machine", "reset_machine_on_startup") and
not self._has_ran and not is_resetting):
inputs.append(
{"type": "ResetMachineOnStartupFlag", 'value': True})
else:
inputs.append(
{"type": "ResetMachineOnStartupFlag", 'value': False})
# support runtime updater
if self._has_ran and not is_resetting:
no_machine_time_steps =\
int((this_run_time * 1000.0) /
self._machine_time_step)
inputs.append({'type': "RunTimeMachineTimeSteps",
'value': no_machine_time_steps})
# FrontEndCommonPartitionableGraphApplicationDataLoader after a
# reset and no changes
if not self._has_ran and not application_graph_changed:
inputs.append(({
'type': "ProcessorToAppDataBaseAddress",
"value": self._processor_to_app_data_base_address_mapper}))
inputs.append({"type": "PlacementToAppDataFilePaths",
'value': self._placement_to_app_data_file_paths})
inputs.append({'type': "WriteCheckerFlag",
'value': config.getboolean(
"Mode", "verify_writes")})
# support resetting when there's changes in the application graph
# (only need to exit)
if application_graph_changed and is_resetting:
inputs.append({"type": "MemoryTransciever", 'value': self._txrx})
inputs.append({'type': "ExecutableTargets",
'value': self._executable_targets})
inputs.append({'type': "MemoryPlacements",
'value': self._placements})
inputs.append({'type': "MemoryGraphMapper",
'value': self._graph_mapper})
inputs.append({'type': "APPID", 'value': self._app_id})
inputs.append({'type': "RanToken", 'value': self._has_ran})
elif application_graph_changed and not is_resetting:
# make a folder for the json files to be stored in
json_folder = os.path.join(
self._report_default_directory, "json_files")
if not os.path.exists(json_folder):
os.mkdir(json_folder)
# translate config "None" to None
width = config.get("Machine", "width")
height = config.get("Machine", "height")
if width == "None":
width = None
else:
width = int(width)
if height == "None":
height = None
else:
height = int(height)
number_of_boards = config.get("Machine", "number_of_boards")
if number_of_boards == "None":
number_of_boards = None
scamp_socket_addresses = config.get("Machine",
"scamp_connections_data")
if scamp_socket_addresses == "None":
scamp_socket_addresses = None
boot_port_num = config.get("Machine", "boot_connection_port_num")
if boot_port_num == "None":
boot_port_num = None
else:
boot_port_num = int(boot_port_num)
inputs.append({'type': "MemoryPartitionableGraph",
'value': self._partitionable_graph})
inputs.append({'type': 'ReportFolder',
'value': self._report_default_directory})
inputs.append({'type': "ApplicationDataFolder",
'value': self._app_data_runtime_folder})
inputs.append({'type': 'IPAddress', 'value': self._hostname})
# basic input stuff
inputs.append({'type': "BMPDetails",
'value': config.get("Machine", "bmp_names")})
inputs.append({'type': "DownedChipsDetails",
'value': config.get("Machine", "down_chips")})
inputs.append({'type': "DownedCoresDetails",
'value': config.get("Machine", "down_cores")})
inputs.append({'type': "BoardVersion",
'value': config.getint("Machine", "version")})
inputs.append({'type': "NumberOfBoards",
'value': number_of_boards})
inputs.append({'type': "MachineWidth", 'value': width})
inputs.append({'type': "MachineHeight", 'value': height})
inputs.append({'type': "AutoDetectBMPFlag",
'value': config.getboolean("Machine",
"auto_detect_bmp")})
inputs.append({'type': "EnableReinjectionFlag",
'value': config.getboolean("Machine",
"enable_reinjection")})
inputs.append({'type': "ScampConnectionData",
'value': scamp_socket_addresses})
inputs.append({'type': "BootPortNum", 'value': boot_port_num})
inputs.append({'type': "APPID", 'value': self._app_id})
inputs.append({'type': "RunTime", 'value': this_run_time})
inputs.append({'type': "TimeScaleFactor",
'value': self._time_scale_factor})
inputs.append({'type': "MachineTimeStep",
'value': self._machine_time_step})
inputs.append({'type': "DatabaseSocketAddresses",
'value': self._database_socket_addresses})
inputs.append({'type': "DatabaseWaitOnConfirmationFlag",
'value': config.getboolean(
"Database", "wait_on_confirmation")})
inputs.append({'type': "WriteCheckerFlag",
'value': config.getboolean(
"Mode", "verify_writes")})
inputs.append({'type': "WriteTextSpecsFlag",
'value': config.getboolean(
"Reports", "writeTextSpecs")})
inputs.append({'type': "ExecutableFinder",
'value': executable_finder})
inputs.append({'type': "MachineHasWrapAroundsFlag",
'value': config.getboolean(
"Machine", "requires_wrap_arounds")})
inputs.append({'type': "ReportStates",
'value': self._reports_states})
inputs.append({'type': "UserCreateDatabaseFlag",
'value': config.get("Database", "create_database")})
inputs.append({'type': "ExecuteMapping",
'value': config.getboolean(
"Database",
"create_routing_info_to_neuron_id_mapping")})
inputs.append({'type': "DatabaseSocketAddresses",
'value': self._database_socket_addresses})
inputs.append({'type': "SendStartNotifications",
'value': config.getboolean(
"Database", "send_start_notification")})
inputs.append({'type': "ProvenanceFilePath",
'value': provenance_file_path})
# add paths for each file based version
inputs.append({'type': "FileCoreAllocationsFilePath",
'value': os.path.join(
json_folder, "core_allocations.json")})
inputs.append({'type': "FileSDRAMAllocationsFilePath",
'value': os.path.join(
json_folder, "sdram_allocations.json")})
inputs.append({'type': "FileMachineFilePath",
'value': os.path.join(
json_folder, "machine.json")})
inputs.append({'type': "FilePartitionedGraphFilePath",
'value': os.path.join(
json_folder, "partitioned_graph.json")})
inputs.append({'type': "FilePlacementFilePath",
'value': os.path.join(
json_folder, "placements.json")})
inputs.append({'type': "FileRouingPathsFilePath",
'value': os.path.join(
json_folder, "routing_paths.json")})
inputs.append({'type': "FileConstraintsFilePath",
'value': os.path.join(
json_folder, "constraints.json")})
if self._has_ran:
logger.warn(
"The network has changed, and therefore mapping will be"
" done again. Any recorded data will be erased.")
else:
# mapping does not need to be executed, therefore add
# the data elements needed for the application runner and
# runtime re-setter
inputs.append({"type": "BufferManager",
"value": self._buffer_manager})
inputs.append({'type': "DatabaseWaitOnConfirmationFlag",
'value': config.getboolean(
"Database", "wait_on_confirmation")})
inputs.append({'type': "SendStartNotifications",
'value': config.getboolean(
"Database", "send_start_notification")})
inputs.append({'type': "DatabaseInterface",
'value': self._database_interface})
inputs.append({"type": "DatabaseSocketAddresses",
'value': self._database_socket_addresses})
inputs.append({'type': "DatabaseFilePath",
'value': self._database_file_path})
inputs.append({'type': "ExecutableTargets",
'value': self._executable_targets})
inputs.append({'type': "APPID", 'value': self._app_id})
inputs.append({"type": "MemoryTransciever", 'value': self._txrx})
inputs.append({"type": "RunTime",
'value': this_run_time})
inputs.append({'type': "TimeScaleFactor",
'value': self._time_scale_factor})
inputs.append({'type': "LoadedReverseIPTagsToken",
'value': True})
inputs.append({'type': "LoadedIPTagsToken", 'value': True})
inputs.append({'type': "LoadedRoutingTablesToken",
'value': True})
inputs.append({'type': "LoadBinariesToken", 'value': True})
inputs.append({'type': "LoadedApplicationDataToken",
'value': True})
inputs.append({'type': "MemoryPlacements",
'value': self._placements})
inputs.append({'type': "MemoryGraphMapper",
'value': self._graph_mapper})
inputs.append({'type': "MemoryPartitionableGraph",
'value': self._partitionable_graph})
inputs.append({'type': "MemoryExtendedMachine",
'value': self._machine})
inputs.append({'type': "MemoryRoutingTables",
'value': self._router_tables})
inputs.append({'type': "ProvenanceFilePath",
'value': provenance_file_path})
inputs.append({'type': "RanToken", 'value': self._has_ran})
return inputs, application_graph_changed
def __init__(self, host_name=None, timestep=None, min_delay=None,
max_delay=None, graph_label=None,
database_socket_addresses=None):
self._hostname = host_name
# update graph label if needed
if graph_label is None:
graph_label = "Application_graph"
# delays parameters
self._min_supported_delay = None
self._max_supported_delay = None
# pacman objects
self._partitionable_graph = PartitionableGraph(label=graph_label)
self._partitioned_graph = None
self._graph_mapper = None
self._placements = None
self._router_tables = None
self._routing_infos = None
self._tags = None
self._machine = None
self._txrx = None
self._reports_states = None
self._app_id = None
self._buffer_manager = None
# database objects
self._database_socket_addresses = set()
if database_socket_addresses is not None:
self._database_socket_addresses.union(database_socket_addresses)
self._database_interface = None
self._create_database = None
self._database_file_path = None
# Determine default executable folder location
# and add this default to end of list of search paths
executable_finder.add_path(os.path.dirname(model_binaries.__file__))
# population holders
self._populations = list()
self._projections = list()
self._multi_cast_vertex = None
self._edge_count = 0
self._live_spike_recorder = dict()
# holder for the executable targets (which we will need for reset and
# pause and resume functionality
self._executable_targets = None
# holders for data needed for reset when nothing changes in the
# application graph
self._processor_to_app_data_base_address_mapper = None
self._placement_to_app_data_file_paths = None
# holder for timing related values
self._has_ran = False
self._has_reset_last = False
self._current_run_ms = 0
self._no_machine_time_steps = None
self._machine_time_step = None
self._no_sync_changes = 0
# state thats needed the first time around
if self._app_id is None:
self._app_id = config.getint("Machine", "appID")
if config.getboolean("Reports", "reportsEnabled"):
self._reports_states = ReportState(
config.getboolean("Reports", "writePartitionerReports"),
config.getboolean("Reports",
"writePlacerReportWithPartitionable"),
config.getboolean("Reports",
"writePlacerReportWithoutPartitionable"),
config.getboolean("Reports", "writeRouterReports"),
config.getboolean("Reports", "writeRouterInfoReport"),
config.getboolean("Reports", "writeTextSpecs"),
config.getboolean("Reports", "writeReloadSteps"),
config.getboolean("Reports", "writeTransceiverReport"),
config.getboolean("Reports", "outputTimesForSections"),
config.getboolean("Reports", "writeTagAllocationReports"))
# set up reports default folder
self._report_default_directory, this_run_time_string = \
helpful_functions.set_up_report_specifics(
default_report_file_path=config.get(
"Reports", "defaultReportFilePath"),
max_reports_kept=config.getint(
"Reports", "max_reports_kept"),
app_id=self._app_id)
# set up application report folder
self._app_data_runtime_folder = \
helpful_functions.set_up_output_application_data_specifics(
max_application_binaries_kept=config.getint(
"Reports", "max_application_binaries_kept"),
where_to_write_application_data_files=config.get(
"Reports", "defaultApplicationDataFilePath"),
app_id=self._app_id,
this_run_time_string=this_run_time_string)
self._spikes_per_second = float(config.getfloat(
"Simulation", "spikes_per_second"))
self._ring_buffer_sigma = float(config.getfloat(
"Simulation", "ring_buffer_sigma"))
# set up machine targeted data
self._set_up_machine_specifics(timestep, min_delay, max_delay,
host_name)
logger.info("Setting time scale factor to {}."
.format(self._time_scale_factor))
logger.info("Setting appID to %d." % self._app_id)
# get the machine time step
logger.info("Setting machine time step to {} micro-seconds."
.format(self._machine_time_step))
def _set_up_machine_specifics(self, timestep, min_delay, max_delay,
hostname):
self._machine_time_step = config.getint("Machine", "machineTimeStep")
# deal with params allowed via the setup options
if timestep is not None:
# convert into milliseconds from microseconds
timestep *= 1000
self._machine_time_step = timestep
if min_delay is not None and float(min_delay * 1000) < 1.0 * timestep:
raise common_exceptions.ConfigurationException(
"Pacman does not support min delays below {} ms with the "
"current machine time step"
.format(constants.MIN_SUPPORTED_DELAY * timestep))
natively_supported_delay_for_models = \
constants.MAX_SUPPORTED_DELAY_TICS
delay_extention_max_supported_delay = \
constants.MAX_DELAY_BLOCKS \
* constants.MAX_TIMER_TICS_SUPPORTED_PER_BLOCK
max_delay_tics_supported = \
natively_supported_delay_for_models + \
delay_extention_max_supported_delay
if max_delay is not None\
and float(max_delay * 1000) > max_delay_tics_supported * timestep:
raise common_exceptions.ConfigurationException(
"Pacman does not support max delays above {} ms with the "
"current machine time step".format(0.144 * timestep))
if min_delay is not None:
self._min_supported_delay = min_delay
else:
self._min_supported_delay = timestep / 1000.0
if max_delay is not None:
self._max_supported_delay = max_delay
else:
self._max_supported_delay = (max_delay_tics_supported *
(timestep / 1000.0))
if (config.has_option("Machine", "timeScaleFactor") and
config.get("Machine", "timeScaleFactor") != "None"):
self._time_scale_factor = \
config.getint("Machine", "timeScaleFactor")
if timestep * self._time_scale_factor < 1000:
logger.warn("the combination of machine time step and the "
"machine time scale factor results in a real "
"timer tick that is currently not reliably "
"supported by the spinnaker machine.")
else:
self._time_scale_factor = max(1,
math.ceil(1000.0 / float(timestep)))
if self._time_scale_factor > 1:
logger.warn("A timestep was entered that has forced pacman103 "
"to automatically slow the simulation down from "
"real time by a factor of {}. To remove this "
"automatic behaviour, please enter a "
"timescaleFactor value in your .pacman.cfg"
.format(self._time_scale_factor))
if hostname is not None:
self._hostname = hostname
logger.warn("The machine name from PYNN setup is overriding the "
"machine name defined in the spynnaker.cfg file")
elif config.has_option("Machine", "machineName"):
self._hostname = config.get("Machine", "machineName")
else:
raise Exception("A SpiNNaker machine must be specified in "
"spynnaker.cfg.")
use_virtual_board = config.getboolean("Machine", "virtual_board")
if self._hostname == 'None' and not use_virtual_board:
raise Exception("A SpiNNaker machine must be specified in "
"spynnaker.cfg.")
def __init__(self,
n_neurons,
binary,
label,
max_atoms_per_core,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
incoming_spike_buffer_size,
model_name,
neuron_model,
input_type,
synapse_type,
threshold_type,
additional_input=None,
constraints=None):
AbstractPartitionableVertex.__init__(self, n_neurons, label,
max_atoms_per_core, constraints)
AbstractDataSpecableVertex.__init__(self, machine_time_step,
timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractVRecordable.__init__(self)
AbstractGSynRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
AbstractProvidesIncomingPartitionConstraints.__init__(self)
AbstractPopulationInitializable.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
self._binary = binary
self._label = label
self._machine_time_step = machine_time_step
self._timescale_factor = timescale_factor
self._incoming_spike_buffer_size = incoming_spike_buffer_size
if incoming_spike_buffer_size is None:
self._incoming_spike_buffer_size = config.getint(
"Simulation", "incoming_spike_buffer_size")
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._spike_recorder = SpikeRecorder(machine_time_step)
self._v_recorder = VRecorder(machine_time_step)
self._gsyn_recorder = GsynRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint("Buffers",
"spike_buffer_size")
self._v_buffer_max_size = config.getint("Buffers", "v_buffer_size")
self._gsyn_buffer_max_size = config.getint("Buffers",
"gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint("Buffers",
"time_between_requests")
self._minimum_buffer_sdram = config.getint("Buffers",
"minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
self._receive_buffer_host = config.get("Buffers",
"receive_buffer_host")
self._receive_buffer_port = config.getint("Buffers",
"receive_buffer_port")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
# Set up synapse handling
self._synapse_manager = SynapticManager(synapse_type,
machine_time_step,
ring_buffer_sigma,
spikes_per_second)
# bool for if state has changed.
self._change_requires_mapping = True
def set_recording_spikes(self):
ip_address = config.get("Buffers", "receive_buffer_host")
port = config.getint("Buffers", "receive_buffer_port")
self.set_buffering_output(ip_address, port)
self._spike_recorder.record = True
def __init__(
self,
n_neurons,
machine_time_step,
timescale_factor,
spike_times=None,
port=None,
tag=None,
ip_address=None,
board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=(constants.SPIKE_BUFFER_SIZE_BUFFERING_IN),
space_before_notification=640,
constraints=None,
label="SpikeSourceArray",
spike_recorder_buffer_size=(constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT),
buffer_size_before_receive=(constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE),
):
self._ip_address = ip_address
if ip_address is None:
self._ip_address = config.get("Buffers", "receive_buffer_host")
self._port = port
if port is None:
self._port = config.getint("Buffers", "receive_buffer_port")
if spike_times is None:
spike_times = []
ReverseIpTagMultiCastSource.__init__(
self,
n_keys=n_neurons,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
label=label,
constraints=constraints,
max_atoms_per_core=(SpikeSourceArray._model_based_max_atoms_per_core),
board_address=board_address,
receive_port=None,
receive_sdp_port=None,
receive_tag=None,
virtual_key=None,
prefix=None,
prefix_type=None,
check_keys=False,
send_buffer_times=spike_times,
send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes,
send_buffer_space_before_notify=space_before_notification,
send_buffer_notification_ip_address=self._ip_address,
send_buffer_notification_port=self._port,
send_buffer_notification_tag=tag,
)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingEdgeConstraints.__init__(self)
SimplePopulationSettable.__init__(self)
AbstractMappable.__init__(self)
AbstractHasFirstMachineTimeStep.__init__(self)
# handle recording
self._spike_recorder = EIEIOSpikeRecorder(machine_time_step)
self._spike_recorder_buffer_size = spike_recorder_buffer_size
self._buffer_size_before_receive = buffer_size_before_receive
# Keep track of any previously generated buffers
self._send_buffers = dict()
self._spike_recording_region_size = None
self._partitioned_vertices = list()
self._partitioned_vertices_current_max_buffer_size = dict()
# used for reset and rerun
self._requires_mapping = True
self._last_runtime_position = 0
self._max_on_chip_memory_usage_for_spikes = max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
if self._max_on_chip_memory_usage_for_spikes is None:
self._max_on_chip_memory_usage_for_spikes = front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP
# check the values do not conflict with chip memory limit
if self._max_on_chip_memory_usage_for_spikes < 0:
raise exceptions.ConfigurationException(
"The memory usage on chip is either beyond what is supportable"
" on the spinnaker board being supported or you have requested"
" a negative value for a memory usage. Please correct and"
" try again"
)
if self._max_on_chip_memory_usage_for_spikes < self._space_before_notification:
self._space_before_notification = self._max_on_chip_memory_usage_for_spikes
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
spike_times=None, port=None, tag=None, ip_address=None,
board_address=None, max_on_chip_memory_usage_for_spikes_in_bytes=(
constants.SPIKE_BUFFER_SIZE_BUFFERING_IN),
space_before_notification=640,
constraints=None, label="SpikeSourceArray",
spike_recorder_buffer_size=(
constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT),
buffer_size_before_receive=(
constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE)):
self._ip_address = ip_address
if ip_address is None:
self._ip_address = config.get("Buffers", "receive_buffer_host")
self._port = port
if port is None:
self._port = config.getint("Buffers", "receive_buffer_port")
if spike_times is None:
spike_times = []
self._minimum_sdram_for_buffering = config.getint(
"Buffers", "minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
ReverseIpTagMultiCastSource.__init__(
self, n_keys=n_neurons, machine_time_step=machine_time_step,
timescale_factor=timescale_factor, label=label,
constraints=constraints,
max_atoms_per_core=(SpikeSourceArray.
_model_based_max_atoms_per_core),
board_address=board_address,
receive_port=None, receive_sdp_port=None, receive_tag=None,
virtual_key=None, prefix=None, prefix_type=None, check_keys=False,
send_buffer_times=spike_times,
send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes,
send_buffer_space_before_notify=space_before_notification,
send_buffer_notification_ip_address=self._ip_address,
send_buffer_notification_port=self._port,
send_buffer_notification_tag=tag)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
SimplePopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
AbstractHasFirstMachineTimeStep.__init__(self)
# handle recording
self._spike_recorder = EIEIOSpikeRecorder(machine_time_step)
self._spike_recorder_buffer_size = spike_recorder_buffer_size
self._buffer_size_before_receive = buffer_size_before_receive
# Keep track of any previously generated buffers
self._send_buffers = dict()
self._spike_recording_region_size = None
self._partitioned_vertices = list()
self._partitioned_vertices_current_max_buffer_size = dict()
# used for reset and rerun
self._requires_mapping = True
self._last_runtime_position = 0
self._max_on_chip_memory_usage_for_spikes = \
max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
if self._max_on_chip_memory_usage_for_spikes is None:
self._max_on_chip_memory_usage_for_spikes = \
front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP
# check the values do not conflict with chip memory limit
if self._max_on_chip_memory_usage_for_spikes < 0:
raise exceptions.ConfigurationException(
"The memory usage on chip is either beyond what is supportable"
" on the spinnaker board being supported or you have requested"
" a negative value for a memory usage. Please correct and"
" try again")
if (self._max_on_chip_memory_usage_for_spikes <
self._space_before_notification):
self._space_before_notification =\
self._max_on_chip_memory_usage_for_spikes
def __init__(self,
host_name=None,
timestep=None,
min_delay=None,
max_delay=None,
graph_label=None,
database_socket_addresses=None):
FrontEndCommonConfigurationFunctions.__init__(self, host_name,
graph_label)
SpynnakerConfigurationFunctions.__init__(self)
FrontEndCommonProvenanceFunctions.__init__(self)
self._database_socket_addresses = set()
self._database_interface = None
self._create_database = None
self._populations = list()
if self._app_id is None:
self._set_up_main_objects(
app_id=config.getint("Machine", "appID"),
execute_data_spec_report=config.getboolean(
"Reports", "writeTextSpecs"),
execute_partitioner_report=config.getboolean(
"Reports", "writePartitionerReports"),
execute_placer_report=config.getboolean(
"Reports", "writePlacerReports"),
execute_router_dat_based_report=config.getboolean(
"Reports", "writeRouterDatReport"),
reports_are_enabled=config.getboolean("Reports",
"reportsEnabled"),
generate_performance_measurements=config.getboolean(
"Reports", "outputTimesForSections"),
execute_router_report=config.getboolean(
"Reports", "writeRouterReports"),
execute_write_reload_steps=config.getboolean(
"Reports", "writeReloadSteps"),
generate_transciever_report=config.getboolean(
"Reports", "writeTransceiverReport"),
execute_routing_info_report=config.getboolean(
"Reports", "writeRouterInfoReport"),
in_debug_mode=config.get("Mode", "mode") == "Debug",
generate_tag_report=config.getboolean(
"Reports", "writeTagAllocationReports"))
self._set_up_pacman_algorthms_listings(
partitioner_algorithm=config.get("Partitioner", "algorithm"),
placer_algorithm=config.get("Placer", "algorithm"),
key_allocator_algorithm=config.get("KeyAllocator",
"algorithm"),
routing_algorithm=config.get("Routing", "algorithm"))
# set up exeuctable specifics
self._set_up_executable_specifics()
self._set_up_report_specifics(
default_report_file_path=config.get("Reports",
"defaultReportFilePath"),
max_reports_kept=config.getint("Reports", "max_reports_kept"),
reports_are_enabled=config.getboolean("Reports",
"reportsEnabled"),
write_provance_data=config.getboolean("Reports",
"writeProvanceData"),
write_text_specs=config.getboolean("Reports",
"writeTextSpecs"))
self._set_up_output_application_data_specifics(
max_application_binaries_kept=config.getint(
"Reports", "max_application_binaries_kept"),
where_to_write_application_data_files=config.get(
"Reports", "defaultApplicationDataFilePath"))
# set up spynnaker specifics, such as setting the machineName from conf
self._set_up_machine_specifics(timestep, min_delay, max_delay,
host_name)
self._spikes_per_second = float(
config.getfloat("Simulation", "spikes_per_second"))
self._ring_buffer_sigma = float(
config.getfloat("Simulation", "ring_buffer_sigma"))
# Determine default executable folder location
# and add this default to end of list of search paths
executable_finder.add_path(os.path.dirname(model_binaries.__file__))
FrontEndCommonInterfaceFunctions.__init__(
self, self._reports_states, self._report_default_directory,
self._app_data_runtime_folder)
logger.info("Setting time scale factor to {}.".format(
self._time_scale_factor))
logger.info("Setting appID to %d." % self._app_id)
# get the machine time step
logger.info("Setting machine time step to {} micro-seconds.".format(
self._machine_time_step))
self._edge_count = 0
# Manager of buffered sending
self._send_buffer_manager = None
height = config.get("Machine", "height")
if width == "None":
width = None
else:
width = int(width)
if height == "None":
height = None
else:
height = int(height)
number_of_boards = config.get("Machine", "number_of_boards")
if number_of_boards == "None":
number_of_boards = None
hostname = config.get("Machine", "machineName")
board_version = config.getint("Machine", "version")
interfacer = FrontEndCommonMachineInterfacer()
interfacer_results = interfacer(
hostname=hostname,
bmp_details=config.get("Machine", "bmp_names"),
downed_chips=config.get("Machine", "down_chips"),
downed_cores=config.get("Machine", "down_cores"),
board_version=board_version,
number_of_boards=number_of_boards, width=width, height=height,
auto_detect_bmp=config.getboolean("Machine", "auto_detect_bmp"),
enable_reinjection=config.getboolean("Machine", "enable_reinjection"),
scamp_connection_data=None, boot_port_num=None,
reset_machine_on_start_up=config.get(
"Machine", "reset_machine_on_startup"))
def __init__(
self, n_neurons, binary, label, max_atoms_per_core,
machine_time_step, timescale_factor, spikes_per_second,
ring_buffer_sigma, model_name, neuron_model, input_type,
synapse_type, threshold_type, additional_input=None,
constraints=None):
ReceiveBuffersToHostBasicImpl.__init__(self)
AbstractPartitionableVertex.__init__(
self, n_neurons, label, max_atoms_per_core, constraints)
AbstractDataSpecableVertex.__init__(
self, machine_time_step, timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractVRecordable.__init__(self)
AbstractGSynRecordable.__init__(self)
AbstractProvidesOutgoingEdgeConstraints.__init__(self)
AbstractProvidesIncomingEdgeConstraints.__init__(self)
AbstractPopulationInitializable.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractMappable.__init__(self)
self._binary = binary
self._label = label
self._machine_time_step = machine_time_step
self._timescale_factor = timescale_factor
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._spike_recorder = SpikeRecorder(machine_time_step)
self._v_recorder = VRecorder(machine_time_step)
self._gsyn_recorder = GsynRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._v_buffer_max_size = config.getint(
"Buffers", "v_buffer_size")
self._gsyn_buffer_max_size = config.getint(
"Buffers", "gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
# Set up synapse handling
self._synapse_manager = SynapticManager(
synapse_type, machine_time_step, ring_buffer_sigma,
spikes_per_second)
# Get buffering information for later use
self._receive_buffer_host = config.get(
"Buffers", "receive_buffer_host")
self._receive_buffer_port = config.getint(
"Buffers", "receive_buffer_port")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
# bool for if state has changed.
self._change_requires_mapping = True
def _create_pacman_executor_inputs(self):
# make a folder for the json files to be stored in
json_folder = os.path.join(
self._report_default_directory, "json_files")
if not os.path.exists(json_folder):
os.mkdir(json_folder)
# file path to store any provenance data to
provenance_file_path = os.path.join(self._report_default_directory,
"provance_data")
if not os.path.exists(provenance_file_path):
os.mkdir(provenance_file_path)
# translate config "None" to None
width = config.get("Machine", "width")
height = config.get("Machine", "height")
if width == "None":
width = None
else:
width = int(width)
if height == "None":
height = None
else:
height = int(height)
number_of_boards = config.get("Machine", "number_of_boards")
if number_of_boards == "None":
number_of_boards = None
scamp_socket_addresses = config.get(
"Machine", "scamp_connections_data")
if scamp_socket_addresses == "None":
scamp_socket_addresses = None
boot_port_num = config.get("Machine", "boot_connection_port_num")
if boot_port_num == "None":
boot_port_num = None
else:
boot_port_num = int(boot_port_num)
inputs = list()
inputs.append({'type': "MemoryPartitionableGraph",
'value': self._partitionable_graph})
inputs.append({'type': 'ReportFolder',
'value': self._report_default_directory})
inputs.append({'type': "ApplicationDataFolder",
'value': self._app_data_runtime_folder})
inputs.append({'type': 'IPAddress', 'value': self._hostname})
# basic input stuff
inputs.append({'type': "BMPDetails",
'value': config.get("Machine", "bmp_names")})
inputs.append({'type': "DownedChipsDetails",
'value': config.get("Machine", "down_chips")})
inputs.append({'type': "DownedCoresDetails",
'value': config.get("Machine", "down_cores")})
inputs.append({'type': "BoardVersion",
'value': config.getint("Machine", "version")})
inputs.append({'type': "NumberOfBoards", 'value': number_of_boards})
inputs.append({'type': "MachineWidth", 'value': width})
inputs.append({'type': "MachineHeight", 'value': height})
inputs.append({'type': "AutoDetectBMPFlag",
'value': config.getboolean("Machine",
"auto_detect_bmp")})
inputs.append({'type': "EnableReinjectionFlag",
'value': config.getboolean("Machine",
"enable_reinjection")})
inputs.append({'type': "ScampConnectionData",
'value': scamp_socket_addresses})
inputs.append({'type': "BootPortNum", 'value': boot_port_num})
inputs.append({'type': "APPID", 'value': self._app_id})
inputs.append({'type': "RunTime", 'value': self._runtime})
inputs.append({'type': "TimeScaleFactor",
'value': self._time_scale_factor})
inputs.append({'type': "MachineTimeStep",
'value': self._machine_time_step})
inputs.append({'type': "DatabaseSocketAddresses",
'value': self._database_socket_addresses})
inputs.append({'type': "DatabaseWaitOnConfirmationFlag",
'value': config.getboolean("Database",
"wait_on_confirmation")})
inputs.append({'type': "WriteCheckerFlag",
'value': config.getboolean("Mode", "verify_writes")})
inputs.append({'type': "WriteTextSpecsFlag",
'value': config.getboolean("Reports",
"writeTextSpecs")})
inputs.append({'type': "ExecutableFinder", 'value': executable_finder})
inputs.append({'type': "MachineHasWrapAroundsFlag",
'value': config.getboolean("Machine",
"requires_wrap_arounds")})
inputs.append({'type': "ReportStates", 'value': self._reports_states})
inputs.append({'type': "UserCreateDatabaseFlag",
'value': config.get("Database", "create_database")})
inputs.append({'type': "ExecuteMapping",
'value': config.getboolean(
"Database",
"create_routing_info_to_neuron_id_mapping")})
inputs.append({'type': "DatabaseSocketAddresses",
'value': self._database_socket_addresses})
inputs.append({'type': "SendStartNotifications",
'value': config.getboolean("Database",
"send_start_notification")})
inputs.append({'type': "ProvenanceFilePath",
'value': provenance_file_path})
# add paths for each file based version
inputs.append({'type': "FileCoreAllocationsFilePath",
'value': os.path.join(
json_folder, "core_allocations.json")})
inputs.append({'type': "FileSDRAMAllocationsFilePath",
'value': os.path.join(
json_folder, "sdram_allocations.json")})
inputs.append({'type': "FileMachineFilePath",
'value': os.path.join(
json_folder, "machine.json")})
inputs.append({'type': "FilePartitionedGraphFilePath",
'value': os.path.join(
json_folder, "partitioned_graph.json")})
inputs.append({'type': "FilePlacementFilePath",
'value': os.path.join(
json_folder, "placements.json")})
inputs.append({'type': "FileRouingPathsFilePath",
'value': os.path.join(
json_folder, "routing_paths.json")})
inputs.append({'type': "FileConstraintsFilePath",
'value': os.path.join(
json_folder, "constraints.json")})
return inputs
def run(self, run_time):
"""
:param run_time:
:return:
"""
# sort out config param to be valid types
width = config.get("Machine", "width")
height = config.get("Machine", "height")
if width == "None":
width = None
else:
width = int(width)
if height == "None":
height = None
else:
height = int(height)
number_of_boards = config.get("Machine", "number_of_boards")
if number_of_boards == "None":
number_of_boards = None
self.setup_interfaces(
hostname=self._hostname,
bmp_details=config.get("Machine", "bmp_names"),
downed_chips=config.get("Machine", "down_chips"),
downed_cores=config.get("Machine", "down_cores"),
board_version=config.getint("Machine", "version"),
number_of_boards=number_of_boards, width=width, height=height,
is_virtual=config.getboolean("Machine", "virtual_board"),
virtual_has_wrap_arounds=config.getboolean(
"Machine", "requires_wrap_arounds"),
auto_detect_bmp=config.getboolean("Machine", "auto_detect_bmp"))
# adds extra stuff needed by the reload script which cannot be given
# directly.
if self._reports_states.transciever_report:
self._reload_script.runtime = run_time
self._reload_script.time_scale_factor = self._time_scale_factor
# create network report if needed
if self._reports_states is not None:
reports.network_specification_partitionable_report(
self._report_default_directory, self._partitionable_graph,
self._hostname)
# calculate number of machine time steps
if run_time is not None:
self._no_machine_time_steps =\
int((run_time * 1000.0) / self._machine_time_step)
ceiled_machine_time_steps = \
math.ceil((run_time * 1000.0) / self._machine_time_step)
if self._no_machine_time_steps != ceiled_machine_time_steps:
raise common_exceptions.ConfigurationException(
"The runtime and machine time step combination result in "
"a factional number of machine runable time steps and "
"therefore spinnaker cannot determine how many to run for")
for vertex in self._partitionable_graph.vertices:
if isinstance(vertex, AbstractDataSpecableVertex):
vertex.set_no_machine_time_steps(
self._no_machine_time_steps)
else:
self._no_machine_time_steps = None
logger.warn("You have set a runtime that will never end, this may"
"cause the neural models to fail to partition "
"correctly")
for vertex in self._partitionable_graph.vertices:
if (isinstance(vertex, AbstractPopulationRecordableVertex) and
vertex.record):
raise common_exceptions.ConfigurationException(
"recording a population when set to infinite runtime "
"is not currently supportable in this tool chain."
"watch this space")
do_timing = config.getboolean("Reports", "outputTimesForSections")
if do_timing:
timer = Timer()
else:
timer = None
self.set_runtime(run_time)
logger.info("*** Running Mapper *** ")
if do_timing:
timer.start_timing()
self.map_model()
if do_timing:
timer.take_sample()
# add database generation if requested
needs_database = self._auto_detect_database(self._partitioned_graph)
user_create_database = config.get("Database", "create_database")
if ((user_create_database == "None" and needs_database) or
user_create_database == "True"):
wait_on_confirmation = config.getboolean(
"Database", "wait_on_confirmation")
self._database_interface = SpynnakerDataBaseInterface(
self._app_data_runtime_folder, wait_on_confirmation,
self._database_socket_addresses)
self._database_interface.add_system_params(
self._time_scale_factor, self._machine_time_step,
self._runtime)
self._database_interface.add_machine_objects(self._machine)
self._database_interface.add_partitionable_vertices(
self._partitionable_graph)
self._database_interface.add_partitioned_vertices(
self._partitioned_graph, self._graph_mapper,
self._partitionable_graph)
self._database_interface.add_placements(self._placements,
self._partitioned_graph)
self._database_interface.add_routing_infos(
self._routing_infos, self._partitioned_graph)
self._database_interface.add_routing_tables(self._router_tables)
self._database_interface.add_tags(self._partitioned_graph,
self._tags)
execute_mapping = config.getboolean(
"Database", "create_routing_info_to_neuron_id_mapping")
if execute_mapping:
self._database_interface.create_neuron_to_key_mapping(
graph_mapper=self._graph_mapper,
partitionable_graph=self._partitionable_graph,
partitioned_graph=self._partitioned_graph,
routing_infos=self._routing_infos)
# if using a reload script, add if that needs to wait for
# confirmation
if self._reports_states.transciever_report:
self._reload_script.wait_on_confirmation = wait_on_confirmation
for socket_address in self._database_socket_addresses:
self._reload_script.add_socket_address(socket_address)
self._database_interface.send_read_notification()
# execute data spec generation
if do_timing:
timer.start_timing()
logger.info("*** Generating Output *** ")
logger.debug("")
executable_targets = self.generate_data_specifications()
if do_timing:
timer.take_sample()
# execute data spec execution
if do_timing:
timer.start_timing()
processor_to_app_data_base_address = \
self.execute_data_specification_execution(
config.getboolean("SpecExecution", "specExecOnHost"),
self._hostname, self._placements, self._graph_mapper,
write_text_specs=config.getboolean(
"Reports", "writeTextSpecs"),
runtime_application_data_folder=self._app_data_runtime_folder,
machine=self._machine)
if self._reports_states is not None:
reports.write_memory_map_report(self._report_default_directory,
processor_to_app_data_base_address)
if do_timing:
timer.take_sample()
if (not isinstance(self._machine, VirtualMachine) and
config.getboolean("Execute", "run_simulation")):
if do_timing:
timer.start_timing()
logger.info("*** Loading tags ***")
self.load_tags(self._tags)
if self._do_load is True:
logger.info("*** Loading data ***")
self._load_application_data(
self._placements, self._graph_mapper,
processor_to_app_data_base_address, self._hostname,
app_data_folder=self._app_data_runtime_folder,
verify=config.getboolean("Mode", "verify_writes"))
self.load_routing_tables(self._router_tables, self._app_id)
logger.info("*** Loading executables ***")
self.load_executable_images(executable_targets, self._app_id)
logger.info("*** Loading buffers ***")
self.set_up_send_buffering(self._partitioned_graph,
self._placements, self._tags)
# end of entire loading setup
if do_timing:
timer.take_sample()
if self._do_run is True:
logger.info("*** Running simulation... *** ")
if do_timing:
timer.start_timing()
# every thing is in sync0. load the initial buffers
self._send_buffer_manager.load_initial_buffers()
if do_timing:
timer.take_sample()
wait_on_confirmation = config.getboolean(
"Database", "wait_on_confirmation")
send_start_notification = config.getboolean(
"Database", "send_start_notification")
self.wait_for_cores_to_be_ready(executable_targets,
self._app_id)
# wait till external app is ready for us to start if required
if (self._database_interface is not None and
wait_on_confirmation):
self._database_interface.wait_for_confirmation()
self.start_all_cores(executable_targets, self._app_id)
if (self._database_interface is not None and
send_start_notification):
self._database_interface.send_start_notification()
if self._runtime is None:
logger.info("Application is set to run forever - exiting")
else:
self.wait_for_execution_to_complete(
executable_targets, self._app_id, self._runtime,
self._time_scale_factor)
self._has_ran = True
if self._retrieve_provance_data:
progress = ProgressBar(self._placements.n_placements + 1,
"getting provenance data")
# retrieve provence data from central
file_path = os.path.join(self._report_default_directory,
"provance_data")
# check the directory doesnt already exist
if not os.path.exists(file_path):
os.mkdir(file_path)
# write provanence data
self.write_provenance_data_in_xml(file_path, self._txrx)
progress.update()
# retrieve provenance data from any cores that provide data
for placement in self._placements.placements:
if isinstance(placement.subvertex,
AbstractProvidesProvenanceData):
core_file_path = os.path.join(
file_path,
"Provanence_data_for_{}_{}_{}_{}.xml".format(
placement.subvertex.label,
placement.x, placement.y, placement.p))
placement.subvertex.write_provenance_data_in_xml(
core_file_path, self.transceiver, placement)
progress.update()
progress.end()
elif isinstance(self._machine, VirtualMachine):
logger.info(
"*** Using a Virtual Machine so no simulation will occur")
else:
logger.info("*** No simulation requested: Stopping. ***")
def run(self, run_time):
"""
:param run_time:
:return:
"""
self._setup_interfaces(
hostname=self._hostname,
virtual_x_dimension=config.getint("Machine",
"virtual_board_x_dimension"),
virtual_y_dimension=config.getint("Machine",
"virtual_board_y_dimension"),
downed_chips=config.get("Machine", "down_chips"),
downed_cores=config.get("Machine", "down_cores"),
requires_virtual_board=config.getboolean("Machine",
"virtual_board"),
requires_wrap_around=config.getboolean("Machine",
"requires_wrap_arounds"),
machine_version=config.getint("Machine", "version"))
# add database generation if requested
if self._create_database:
wait_on_confirmation = \
config.getboolean("Database", "wait_on_confirmation")
self._database_interface = DataBaseInterface(
self._app_data_runtime_folder, wait_on_confirmation,
self._database_socket_addresses)
# create network report if needed
if self._reports_states is not None:
reports.network_specification_partitionable_report(
self._report_default_directory, self._partitionable_graph,
self._hostname)
# calculate number of machine time steps
if run_time is not None:
self._no_machine_time_steps =\
int((run_time * 1000.0) / self._machine_time_step)
ceiled_machine_time_steps = \
math.ceil((run_time * 1000.0) / self._machine_time_step)
if self._no_machine_time_steps != ceiled_machine_time_steps:
raise common_exceptions.ConfigurationException(
"The runtime and machine time step combination result in "
"a factional number of machine runable time steps and "
"therefore spinnaker cannot determine how many to run for")
for vertex in self._partitionable_graph.vertices:
if isinstance(vertex, AbstractDataSpecableVertex):
vertex.set_no_machine_time_steps(
self._no_machine_time_steps)
else:
self._no_machine_time_steps = None
logger.warn("You have set a runtime that will never end, this may"
"cause the neural models to fail to partition "
"correctly")
for vertex in self._partitionable_graph.vertices:
if vertex.is_set_to_record_spikes():
raise common_exceptions.ConfigurationException(
"recording a population when set to infinite runtime "
"is not currently supportable in this tool chain."
"watch this space")
do_timing = config.getboolean("Reports", "outputTimesForSections")
if do_timing:
timer = Timer()
else:
timer = None
self.set_runtime(run_time)
logger.info("*** Running Mapper *** ")
if do_timing:
timer.start_timing()
self.map_model()
if do_timing:
timer.take_sample()
# load database if needed
if self._create_database:
self._database_interface.add_system_params(
self._time_scale_factor, self._machine_time_step,
self._runtime)
self._database_interface.add_machine_objects(self._machine)
self._database_interface.add_partitionable_vertices(
self._partitionable_graph)
self._database_interface.add_partitioned_vertices(
self._partitioned_graph, self._graph_mapper,
self._partitionable_graph)
self._database_interface.add_placements(self._placements,
self._partitioned_graph)
self._database_interface.add_routing_infos(
self._routing_infos, self._partitioned_graph)
self._database_interface.add_routing_tables(self._router_tables)
self._database_interface.add_tags(self._partitioned_graph,
self._tags)
execute_mapping = config.getboolean(
"Database", "create_routing_info_to_neuron_id_mapping")
if execute_mapping:
self._database_interface.create_neuron_to_key_mapping(
graph_mapper=self._graph_mapper,
partitionable_graph=self._partitionable_graph,
partitioned_graph=self._partitioned_graph,
routing_infos=self._routing_infos)
self._database_interface.send_read_notification()
# execute data spec generation
if do_timing:
timer.start_timing()
logger.info("*** Generating Output *** ")
logger.debug("")
executable_targets = self.generate_data_specifications()
if do_timing:
timer.take_sample()
# execute data spec execution
if do_timing:
timer.start_timing()
processor_to_app_data_base_address = \
self.execute_data_specification_execution(
config.getboolean("SpecExecution", "specExecOnHost"),
self._hostname, self._placements, self._graph_mapper,
write_text_specs=config.getboolean(
"Reports", "writeTextSpecs"),
runtime_application_data_folder=self._app_data_runtime_folder)
if self._reports_states is not None:
reports.write_memory_map_report(self._report_default_directory,
processor_to_app_data_base_address)
if do_timing:
timer.take_sample()
if (not isinstance(self._machine, VirtualMachine) and
config.getboolean("Execute", "run_simulation")):
if do_timing:
timer.start_timing()
logger.info("*** Loading tags ***")
self._load_tags(self._tags)
if self._do_load is True:
logger.info("*** Loading data ***")
self._load_application_data(
self._placements, self._router_tables, self._graph_mapper,
processor_to_app_data_base_address, self._hostname,
self._app_id,
machine_version=config.getint("Machine", "version"),
app_data_folder=self._app_data_runtime_folder)
logger.info("*** Loading executables ***")
self._load_executable_images(
executable_targets, self._app_id,
app_data_folder=self._app_data_runtime_folder)
logger.info("*** Loading buffers ***")
self._set_up_send_buffering()
# end of entire loading setup
if do_timing:
timer.take_sample()
if self._do_run is True:
logger.info("*** Running simulation... *** ")
if self._reports_states.transciever_report:
reports.re_load_script_running_aspects(
self._app_data_runtime_folder, executable_targets,
self._hostname, self._app_id, run_time)
# every thing is in sync0. load the initial buffers
self._send_buffer_manager.load_initial_buffers()
wait_on_confirmation = config.getboolean(
"Database", "wait_on_confirmation")
send_start_notification = config.getboolean(
"Database", "send_start_notification")
self._wait_for_cores_to_be_ready(executable_targets,
self._app_id)
# wait till external app is ready for us to start if required
if (self._database_interface is not None and
wait_on_confirmation):
logger.info(
"*** Awaiting for a response from an external source "
"to state its ready for the simulation to start ***")
self._database_interface.wait_for_confirmation()
self._start_all_cores(executable_targets, self._app_id)
if (self._database_interface is not None and
send_start_notification):
self._database_interface.send_start_notification()
if self._runtime is None:
logger.info("Application is set to run forever - exiting")
else:
self._wait_for_execution_to_complete(
executable_targets, self._app_id, self._runtime,
self._time_scale_factor)
self._has_ran = True
if self._retrieve_provance_data:
# retrieve provence data from central
file_path = os.path.join(self._report_default_directory,
"provance_data")
# check the directory doesnt already exist
if not os.path.exists(file_path):
os.mkdir(file_path)
self._write_provanence_data_in_xml(file_path)
# retrieve provenance data from any cores that provide data
for placement in self._placements:
if isinstance(placement.subvertex,
AbstractProvidesProvanenceData):
file_path = os.path.join(
self._report_default_directory,
"Provanence_data_for_core:{}:{}:{}"
.format(placement.x, placement.y, placement.p))
elif isinstance(self._machine, VirtualMachine):
logger.info(
"*** Using a Virtual Machine so no simulation will occur")
else:
logger.info("*** No simulation requested: Stopping. ***")
def run(self, run_time):
"""
:param run_time:
:return:
"""
# sort out config param to be valid types
width = config.get("Machine", "width")
height = config.get("Machine", "height")
if width == "None":
width = None
else:
width = int(width)
if height == "None":
height = None
else:
height = int(height)
number_of_boards = config.get("Machine", "number_of_boards")
if number_of_boards == "None":
number_of_boards = None
self.setup_interfaces(
hostname=self._hostname,
bmp_details=config.get("Machine", "bmp_names"),
downed_chips=config.get("Machine", "down_chips"),
downed_cores=config.get("Machine", "down_cores"),
board_version=config.getint("Machine", "version"),
number_of_boards=number_of_boards,
width=width,
height=height,
is_virtual=config.getboolean("Machine", "virtual_board"),
virtual_has_wrap_arounds=config.getboolean(
"Machine", "requires_wrap_arounds"),
auto_detect_bmp=config.getboolean("Machine", "auto_detect_bmp"))
# adds extra stuff needed by the reload script which cannot be given
# directly.
if self._reports_states.transciever_report:
self._reload_script.runtime = run_time
self._reload_script.time_scale_factor = self._time_scale_factor
# create network report if needed
if self._reports_states is not None:
reports.network_specification_partitionable_report(
self._report_default_directory, self._partitionable_graph,
self._hostname)
# calculate number of machine time steps
if run_time is not None:
self._no_machine_time_steps =\
int((run_time * 1000.0) / self._machine_time_step)
ceiled_machine_time_steps = \
math.ceil((run_time * 1000.0) / self._machine_time_step)
if self._no_machine_time_steps != ceiled_machine_time_steps:
raise common_exceptions.ConfigurationException(
"The runtime and machine time step combination result in "
"a factional number of machine runable time steps and "
"therefore spinnaker cannot determine how many to run for")
for vertex in self._partitionable_graph.vertices:
if isinstance(vertex, AbstractDataSpecableVertex):
vertex.set_no_machine_time_steps(
self._no_machine_time_steps)
else:
self._no_machine_time_steps = None
logger.warn("You have set a runtime that will never end, this may"
"cause the neural models to fail to partition "
"correctly")
for vertex in self._partitionable_graph.vertices:
if (isinstance(vertex, AbstractPopulationRecordableVertex)
and vertex.record):
raise common_exceptions.ConfigurationException(
"recording a population when set to infinite runtime "
"is not currently supportable in this tool chain."
"watch this space")
do_timing = config.getboolean("Reports", "outputTimesForSections")
if do_timing:
timer = Timer()
else:
timer = None
self.set_runtime(run_time)
logger.info("*** Running Mapper *** ")
if do_timing:
timer.start_timing()
self.map_model()
if do_timing:
timer.take_sample()
# add database generation if requested
needs_database = self._auto_detect_database(self._partitioned_graph)
user_create_database = config.get("Database", "create_database")
if ((user_create_database == "None" and needs_database)
or user_create_database == "True"):
wait_on_confirmation = config.getboolean("Database",
"wait_on_confirmation")
self._database_interface = SpynnakerDataBaseInterface(
self._app_data_runtime_folder, wait_on_confirmation,
self._database_socket_addresses)
self._database_interface.add_system_params(self._time_scale_factor,
self._machine_time_step,
self._runtime)
self._database_interface.add_machine_objects(self._machine)
self._database_interface.add_partitionable_vertices(
self._partitionable_graph)
self._database_interface.add_partitioned_vertices(
self._partitioned_graph, self._graph_mapper,
self._partitionable_graph)
self._database_interface.add_placements(self._placements,
self._partitioned_graph)
self._database_interface.add_routing_infos(self._routing_infos,
self._partitioned_graph)
self._database_interface.add_routing_tables(self._router_tables)
self._database_interface.add_tags(self._partitioned_graph,
self._tags)
execute_mapping = config.getboolean(
"Database", "create_routing_info_to_neuron_id_mapping")
if execute_mapping:
self._database_interface.create_neuron_to_key_mapping(
graph_mapper=self._graph_mapper,
partitionable_graph=self._partitionable_graph,
partitioned_graph=self._partitioned_graph,
routing_infos=self._routing_infos)
# if using a reload script, add if that needs to wait for
# confirmation
if self._reports_states.transciever_report:
self._reload_script.wait_on_confirmation = wait_on_confirmation
for socket_address in self._database_socket_addresses:
self._reload_script.add_socket_address(socket_address)
self._database_interface.send_read_notification()
# execute data spec generation
if do_timing:
timer.start_timing()
logger.info("*** Generating Output *** ")
logger.debug("")
executable_targets = self.generate_data_specifications()
if do_timing:
timer.take_sample()
# execute data spec execution
if do_timing:
timer.start_timing()
processor_to_app_data_base_address = \
self.execute_data_specification_execution(
config.getboolean("SpecExecution", "specExecOnHost"),
self._hostname, self._placements, self._graph_mapper,
write_text_specs=config.getboolean(
"Reports", "writeTextSpecs"),
runtime_application_data_folder=self._app_data_runtime_folder,
machine=self._machine)
if self._reports_states is not None:
reports.write_memory_map_report(
self._report_default_directory,
processor_to_app_data_base_address)
if do_timing:
timer.take_sample()
if (not isinstance(self._machine, VirtualMachine)
and config.getboolean("Execute", "run_simulation")):
if do_timing:
timer.start_timing()
logger.info("*** Loading tags ***")
self.load_tags(self._tags)
if self._do_load is True:
logger.info("*** Loading data ***")
self._load_application_data(
self._placements,
self._graph_mapper,
processor_to_app_data_base_address,
self._hostname,
app_data_folder=self._app_data_runtime_folder,
verify=config.getboolean("Mode", "verify_writes"))
self.load_routing_tables(self._router_tables, self._app_id)
logger.info("*** Loading executables ***")
self.load_executable_images(executable_targets, self._app_id)
logger.info("*** Loading buffers ***")
self.set_up_send_buffering(self._partitioned_graph,
self._placements, self._tags)
# end of entire loading setup
if do_timing:
timer.take_sample()
if self._do_run is True:
logger.info("*** Running simulation... *** ")
if do_timing:
timer.start_timing()
# every thing is in sync0. load the initial buffers
self._send_buffer_manager.load_initial_buffers()
if do_timing:
timer.take_sample()
wait_on_confirmation = config.getboolean(
"Database", "wait_on_confirmation")
send_start_notification = config.getboolean(
"Database", "send_start_notification")
self.wait_for_cores_to_be_ready(executable_targets,
self._app_id)
# wait till external app is ready for us to start if required
if (self._database_interface is not None
and wait_on_confirmation):
self._database_interface.wait_for_confirmation()
self.start_all_cores(executable_targets, self._app_id)
if (self._database_interface is not None
and send_start_notification):
self._database_interface.send_start_notification()
if self._runtime is None:
logger.info("Application is set to run forever - exiting")
else:
self.wait_for_execution_to_complete(
executable_targets, self._app_id, self._runtime,
self._time_scale_factor)
self._has_ran = True
if self._retrieve_provance_data:
progress = ProgressBar(self._placements.n_placements + 1,
"getting provenance data")
# retrieve provence data from central
file_path = os.path.join(self._report_default_directory,
"provance_data")
# check the directory doesnt already exist
if not os.path.exists(file_path):
os.mkdir(file_path)
# write provanence data
self.write_provenance_data_in_xml(file_path, self._txrx)
progress.update()
# retrieve provenance data from any cores that provide data
for placement in self._placements.placements:
if isinstance(placement.subvertex,
AbstractProvidesProvenanceData):
core_file_path = os.path.join(
file_path,
"Provanence_data_for_{}_{}_{}_{}.xml".format(
placement.subvertex.label, placement.x,
placement.y, placement.p))
placement.subvertex.write_provenance_data_in_xml(
core_file_path, self.transceiver, placement)
progress.update()
progress.end()
elif isinstance(self._machine, VirtualMachine):
logger.info(
"*** Using a Virtual Machine so no simulation will occur")
else:
logger.info("*** No simulation requested: Stopping. ***")
def generate_data_specifications(self):
""" generates the dsg for the graph.
:return:
"""
# iterate though subvertexes and call generate_data_spec for each
# vertex
executable_targets = dict()
no_processors = config.getint("Threading", "dsg_threads")
thread_pool = ThreadPool(processes=no_processors)
# create a progress bar for end users
progress_bar = ProgressBar(len(list(self._placements.placements)),
"on generating data specifications")
data_generator_interfaces = list()
for placement in self._placements.placements:
associated_vertex =\
self._graph_mapper.get_vertex_from_subvertex(
placement.subvertex)
# if the vertex can generate a DSG, call it
if isinstance(associated_vertex, AbstractDataSpecableVertex):
ip_tags = self._tags.get_ip_tags_for_vertex(
placement.subvertex)
reverse_ip_tags = self._tags.get_reverse_ip_tags_for_vertex(
placement.subvertex)
data_generator_interface = DataGeneratorInterface(
associated_vertex, placement.subvertex, placement,
self._partitioned_graph, self._partitionable_graph,
self._routing_infos, self._hostname, self._graph_mapper,
self._report_default_directory, ip_tags, reverse_ip_tags,
self._writeTextSpecs, self._app_data_runtime_folder,
progress_bar)
data_generator_interfaces.append(data_generator_interface)
thread_pool.apply_async(data_generator_interface.start)
# Get name of binary from vertex
binary_name = associated_vertex.get_binary_file_name()
# Attempt to find this within search paths
binary_path = executable_finder.get_executable_path(
binary_name)
if binary_path is None:
raise exceptions.ExecutableNotFoundException(binary_name)
if binary_path in executable_targets:
executable_targets[binary_path].add_processor(placement.x,
placement.y,
placement.p)
else:
processors = [placement.p]
initial_core_subset = CoreSubset(placement.x, placement.y,
processors)
list_of_core_subsets = [initial_core_subset]
executable_targets[binary_path] = \
CoreSubsets(list_of_core_subsets)
for data_generator_interface in data_generator_interfaces:
data_generator_interface.wait_for_finish()
thread_pool.close()
thread_pool.join()
# finish the progress bar
progress_bar.end()
return executable_targets
def _set_up_timings(self, timestep, min_delay, max_delay):
self._machine_time_step = config.getint("Machine", "machineTimeStep")
# deal with params allowed via the setup options
if timestep is not None:
# convert into milliseconds from microseconds
timestep *= 1000
self._machine_time_step = timestep
if min_delay is not None and float(min_delay * 1000) < 1.0 * timestep:
raise common_exceptions.ConfigurationException(
"Pacman does not support min delays below {} ms with the "
"current machine time step".format(
constants.MIN_SUPPORTED_DELAY * timestep))
natively_supported_delay_for_models = \
constants.MAX_SUPPORTED_DELAY_TICS
delay_extension_max_supported_delay = \
constants.MAX_DELAY_BLOCKS \
* constants.MAX_TIMER_TICS_SUPPORTED_PER_BLOCK
max_delay_tics_supported = \
natively_supported_delay_for_models + \
delay_extension_max_supported_delay
if max_delay is not None\
and float(max_delay * 1000) > max_delay_tics_supported * timestep:
raise common_exceptions.ConfigurationException(
"Pacman does not support max delays above {} ms with the "
"current machine time step".format(0.144 * timestep))
if min_delay is not None:
self._min_supported_delay = min_delay
else:
self._min_supported_delay = timestep / 1000.0
if max_delay is not None:
self._max_supported_delay = max_delay
else:
self._max_supported_delay = (max_delay_tics_supported *
(timestep / 1000.0))
if (config.has_option("Machine", "timeScaleFactor")
and config.get("Machine", "timeScaleFactor") != "None"):
self._time_scale_factor = \
config.getint("Machine", "timeScaleFactor")
if timestep * self._time_scale_factor < 1000:
if config.getboolean("Mode",
"violate_1ms_wall_clock_restriction"):
logger.warn(
"****************************************************")
logger.warn(
"*** The combination of simulation time step and ***")
logger.warn(
"*** the machine time scale factor results in a ***")
logger.warn(
"*** wall clock timer tick that is currently not ***")
logger.warn(
"*** reliably supported by the spinnaker machine. ***")
logger.warn(
"****************************************************")
else:
raise common_exceptions.ConfigurationException(
"The combination of simulation time step and the"
" machine time scale factor results in a wall clock "
"timer tick that is currently not reliably supported "
"by the spinnaker machine. If you would like to "
"override this behaviour (at your own risk), please "
"add violate_1ms_wall_clock_restriction = True to the "
"[Mode] section of your .spynnaker.cfg file")
else:
self._time_scale_factor = max(1,
math.ceil(1000.0 / float(timestep)))
if self._time_scale_factor > 1:
logger.warn(
"A timestep was entered that has forced sPyNNaker "
"to automatically slow the simulation down from "
"real time by a factor of {}. To remove this "
"automatic behaviour, please enter a "
"timescaleFactor value in your .spynnaker.cfg".format(
self._time_scale_factor))
def _set_up_machine_specifics(self, timestep, min_delay, max_delay,
hostname):
self._machine_time_step = config.getint("Machine", "machineTimeStep")
# deal with params allowed via the setup optimals
if timestep is not None:
timestep *= 1000 # convert into ms from microseconds
config.set("Machine", "machineTimeStep", timestep)
self._machine_time_step = timestep
if min_delay is not None and float(min_delay * 1000) < 1.0 * timestep:
raise exceptions.ConfigurationException(
"Pacman does not support min delays below {} ms with the "
"current machine time step".format(1.0 * timestep))
natively_supported_delay_for_models = \
constants.MAX_SUPPORTED_DELAY_TICS
delay_extention_max_supported_delay = \
constants.MAX_DELAY_BLOCKS \
* constants.MAX_TIMER_TICS_SUPPORTED_PER_BLOCK
max_delay_tics_supported = \
natively_supported_delay_for_models + \
delay_extention_max_supported_delay
if max_delay is not None\
and float(max_delay * 1000) > max_delay_tics_supported * timestep:
raise exceptions.ConfigurationException(
"Pacman does not support max delays above {} ms with the "
"current machine time step".format(0.144 * timestep))
if min_delay is not None:
if not config.has_section("Model"):
config.add_section("Model")
config.set("Model", "min_delay", (min_delay * 1000) / timestep)
if max_delay is not None:
if not config.has_section("Model"):
config.add_section("Model")
config.set("Model", "max_delay", (max_delay * 1000) / timestep)
if (config.has_option("Machine", "timeScaleFactor")
and config.get("Machine", "timeScaleFactor") != "None"):
self._time_scale_factor = \
config.getint("Machine", "timeScaleFactor")
if timestep * self._time_scale_factor < 1000:
logger.warn("the combination of machine time step and the "
"machine time scale factor results in a real "
"timer tick that is currently not reliably "
"supported by the spinnaker machine.")
else:
self._time_scale_factor = max(1,
math.ceil(1000.0 / float(timestep)))
if self._time_scale_factor > 1:
logger.warn("A timestep was entered that has forced pacman103 "
"to automatically slow the simulation down from "
"real time by a factor of {}. To remove this "
"automatic behaviour, please enter a "
"timescaleFactor value in your .pacman.cfg".format(
self._time_scale_factor))
if hostname is not None:
self._hostname = hostname
logger.warn("The machine name from PYNN setup is overriding the "
"machine name defined in the spynnaker.cfg file")
elif config.has_option("Machine", "machineName"):
self._hostname = config.get("Machine", "machineName")
else:
raise Exception("A SpiNNaker machine must be specified in "
"spynnaker.cfg.")
use_virtual_board = config.getboolean("Machine", "virtual_board")
if self._hostname == 'None' and not use_virtual_board:
raise Exception("A SpiNNaker machine must be specified in "
"spynnaker.cfg.")