def __init__(self,
project_dir=None,
max_training_processes=1,
response_log=None,
emulation_mode=None,
remote_storage=None,
component_builder=None,
model_server=None,
wait_time_between_pulls=None):
self._training_processes = max(max_training_processes, 1)
self._current_training_processes = 0
self.responses = self._create_query_logger(response_log)
self.project_dir = config.make_path_absolute(project_dir)
self.emulator = self._create_emulator(emulation_mode)
self.remote_storage = remote_storage
self.model_server = model_server
self.wait_time_between_pulls = wait_time_between_pulls
if component_builder:
self.component_builder = component_builder
else:
self.component_builder = ComponentBuilder(use_cache=True)
self.project_store = self._create_project_store(project_dir)
# tensorflow sessions are not fork-safe,
# and training processes have to be spawned instead of forked. See
# https://github.com/tensorflow/tensorflow/issues/5448#issuecomment
# -258934405
multiprocessing.set_start_method('spawn', force=True)
self.pool = ProcessPool(self._training_processes)
def __init__(self,
project_dir=None,
max_training_processes=1,
response_log=None,
emulation_mode=None,
remote_storage=None,
component_builder=None,
model_server=None,
wait_time_between_pulls=None):
self._training_processes = max(max_training_processes, 1)
self._current_training_processes = 0
self.responses = self._create_query_logger(response_log)
self.project_dir = config.make_path_absolute(project_dir)
self.emulator = self._create_emulator(emulation_mode)
self.remote_storage = remote_storage
self.model_server = model_server
self.wait_time_between_pulls = wait_time_between_pulls
if component_builder:
self.component_builder = component_builder
else:
self.component_builder = ComponentBuilder(use_cache=True)
self.project_store = self._create_project_store(project_dir)
self.pool = ProcessPool(self._training_processes)
def __init__(self,
project_dir=None,
max_training_processes=1,
response_log=None,
emulation_mode=None,
remote_storage=None,
component_builder=None):
self._training_processes = max(max_training_processes, 1)
self.responses = self._create_query_logger(response_log)
self.project_dir = config.make_path_absolute(project_dir)
self.emulator = self._create_emulator(emulation_mode)
self.remote_storage = remote_storage
if component_builder:
self.component_builder = component_builder
else:
self.component_builder = ComponentBuilder(use_cache=True)
self.project_manager = ProjectManager.create(project_dir,
remote_storage,
component_builder)
self.project_store = self.project_manager.get_projects()
self.pool = ProcessPool(self._training_processes)
def __init__(self, config, component_builder):
self._training_processes = config['max_training_processes'] if config['max_training_processes'] > 0 else 1
self.config = config
self.responses = self._create_query_logger(config)
self.model_dir = config['path']
self.emulator = self._create_emulator()
self.component_builder = component_builder if component_builder else ComponentBuilder(use_cache=True)
self.project_store = self._create_project_store()
self.pool = ProcessPool(self._training_processes)
def __init__(self, config, component_builder):
self._training_processes = config['max_training_processes'] if config['max_training_processes'] > 0 else 1
self.config = config
self.responses = DataRouter._create_query_logger(config['response_log'])
self._trainings_queued = 0
self.model_dir = config['path']
self.token = config['token']
self.emulator = self.__create_emulator()
self.component_builder = component_builder if component_builder else ComponentBuilder(use_cache=True)
self.model_store = self.__create_model_store()
self.pool = ProcessPool(self._training_processes)
def __init__(
self,
project_dir=None,
max_training_processes=1,
response_log=None,
emulation_mode=None,
remote_storage=None,
component_builder=None,
model_server=None,
wait_time_between_pulls=None,
):
self._training_processes = max(max_training_processes, 1)
self._current_training_processes = 0
self.responses = self._create_query_logger(response_log)
self.project_dir = config.make_path_absolute(project_dir)
self.emulator = self._create_emulator(emulation_mode)
self.remote_storage = remote_storage
self.model_server = model_server
self.wait_time_between_pulls = wait_time_between_pulls
if component_builder:
self.component_builder = component_builder
else:
self.component_builder = ComponentBuilder(use_cache=True)
# TODO: Should be moved to separate method
loop = asyncio.get_event_loop()
if loop.is_closed():
loop = asyncio.new_event_loop()
self.project_store = loop.run_until_complete(
self._create_project_store(self.project_dir)
)
loop.close()
# tensorflow sessions are not fork-safe,
# and training processes have to be spawned instead of forked. See
# https://github.com/tensorflow/tensorflow/issues/5448#issuecomment
# -258934405
multiprocessing.set_start_method("spawn", force=True)
self.pool = ProcessPool(self._training_processes)
