def main(argv=None):
'''Run NVCaffe service over RPyC. Launch this service with NVCaffe
istalled on the system. If using containers, then launch this service from
within the container.
'''
desc = '{}{}'.format(__doc__, main.__doc__)
argv = sys.argv if argv is None else sys.argv.extend(argv)
# CLI parser
args = parser_(desc)
debug_flag = args.debug
gpus_on_system = args.ngpus
# import caffe as cf # @UnresolvedImport
# cfserv_alias = 'Caffe-{}'.format(cf.__version__) # @UndefinedVariable
cfver = subprocess.check_output(shlex.split('caffe --version')).split()[-1]
cfserv_alias = 'NVCaffe-{}'.format(cfver)
NVCaffeService.ALIASES = ['NVCaffe', cfserv_alias]
log = None
if debug_flag:
# print('DEBUGGING TF')
log = get_logger(__file__, level=logging.DEBUG)
server = server_builder(NVCaffeService,
log_obj=log,
gpus_on_system=gpus_on_system)
server.start()
def on_connect(self):
'''Code that runs when a connection is created to init the service.
According to documentation:
Try to avoid overriding the __init__ method of the service. Place
all initialization-related code in on_connect.
'''
# refer to: rpyc/core/protocol.py for various config options
# self._conn._config.update(dict(
# # allow_all_attrs=True,
# # allow_pickle=True,
# # allow_getattr=True,
# # allow_setattr=False,
# # allow_delattr=False,
# # import_custom_exceptions=True,
# # instantiate_custom_exceptions=True,
# # instantiate_oldstyle_exceptions=True,
# ))
# https://stackoverflow.com/questions/13247956/python-django-spawn-background-process-and-avoid-zombie-process @IgnorePep8
# cleanup spawn background process and avoid zombie process
active_children()
try:
# If the logger was set via classmethod set_log
self._log = self._log
except AttributeError:
log = get_logger(__file__, level=logging.INFO)
# log = get_logger(__file__, level=logging.DEBUG)
self._log = log
log = self._log
pid = os.getpid()
log.debug('STARTING PROCESS: {} SERVICE: {}'.format(
pid, self.get_service_name()))
def main(argv=None):
'''Run Tensorflow service over RPyC. Launch this service with Tensorflow
istalled on the system. If using containers, then launch this service from
within the container.
'''
desc = '{}{}'.format(__doc__, main.__doc__)
argv = sys.argv if argv is None else sys.argv.extend(argv)
# CLI parser
args = parser_(desc)
debug_flag = args.debug
gpus_on_system = args.ngpus
import tensorflow as tf
tfserv_alias = 'Tensorflow-{}'.format(tf.__version__) # @UndefinedVariable
TensorflowService.ALIASES = ['Tensorflow', tfserv_alias]
log = None
if debug_flag:
# print('DEBUGGING TF')
log = get_logger(__file__, level=logging.DEBUG)
server = server_builder(TensorflowService,
log_obj=log,
gpus_on_system=gpus_on_system)
server.start()
def main(argv=None):
'''Run DeepLearning service over RPyC.'''
desc = '{}{}'.format(__doc__, main.__doc__)
argv = sys.argv if argv is None else sys.argv.extend(argv)
# CLI parser
args = parser_(desc)
debug_flag = args.debug
gpus_on_system = args.ngpus
log = None
if debug_flag:
# print('DEBUGGING TF')
log = get_logger(__file__, level=logging.DEBUG)
server = server_builder(DeepLearningService,
log_obj=log,
gpus_on_system=gpus_on_system)
server.start()
def __init__(self, ngpus, log=None, gpus_on_system=None):
'''
:param gpus_on_system: Only used if pynvml is not installed.
'''
log = get_logger(__file__, level=logging.INFO) if log is None else log
self._log = log
self._ngpus = ngpus
try:
import pynvml
pynvml.nvmlInit()
self._gpus_on_system = pynvml.nvmlDeviceGetCount()
pynvml.nvmlShutdown()
except Exception:
self._gpus_on_system = gpus_on_system
if self._gpus_on_system is None:
warnings.warn(
'UKNOWN NUMBER OF GPUS ON SYSTEM. INSTALL NVML AND PYNVML. '
'SETTING TO 4', RuntimeWarning)
self._gpus_on_system = 4
self._lock_list = {}
self._acquired = False
def main(argv=None):
'''Run Caffe service over RPyC. Launch this service with Caffe
istalled on the system. If using containers, then launch this service from
within the container.
'''
desc = '{}{}'.format(__doc__, main.__doc__)
argv = sys.argv if argv is None else sys.argv.extend(argv)
# CLI parser
args = parser_(desc)
debug_flag = args.debug
gpus_on_system = args.ngpus
# import caffe as cf # @UnresolvedImport
# cfserv_alias = 'Caffe-{}'.format(cf.__version__) # @UndefinedVariable
cfver = subprocess.check_output(shlex.split('caffe --version')).split()[-1]
cfserv_alias = 'Caffe-{}'.format(cfver)
CaffeService.ALIASES = ['Caffe', cfserv_alias]
# refer to: rpyc/core/protocol.py for various config options
# protocol_config = {
# # 'allow_pickle': True,
# # 'allow_public_attrs': True
# } # pass customized protocol_config to server_builder
log = None
if debug_flag:
# print('DEBUGGING TF')
log = get_logger(__file__, level=logging.DEBUG)
server = server_builder(CaffeService,
log_obj=log,
gpus_on_system=gpus_on_system)
server.start()