def __init__(
self,
hook: "FrameworkHook",
id: Union[int, str] = 0,
data: Union[List, tuple] = None,
is_client_worker: bool = False,
log_msgs: bool = False,
verbose: bool = None,
auto_add: bool = True,
message_pending_time: Union[int, float] = 0,
):
"""Initializes a BaseWorker."""
super().__init__()
self.hook = hook
self.object_store = ObjectStore(owner=self)
self.message_handlers = []
self.message_handlers.append(
BaseMessageHandler(self.object_store, self))
self.id = id
self.is_client_worker = is_client_worker
self.log_msgs = log_msgs
if verbose is None:
self.verbose = hook.verbose if hasattr(hook, "verbose") else False
else:
self.verbose = verbose
if isinstance(hook, sy.TorchHook) and hasattr(hook, "_syft_workers"):
hook._syft_workers.add(self)
self.auto_add = auto_add
self._message_pending_time = message_pending_time
self.msg_history = []
self.load_data(data)
# Declare workers as appropriate
self._known_workers = {}
if auto_add:
if hook is not None and hook.local_worker is not None:
known_workers = self.hook.local_worker._known_workers
if self.id in known_workers:
if isinstance(known_workers[self.id], type(self)):
# If a worker with this id already exists and it has the
# same type as the one being created, we copy all the attributes
# of the existing worker to this one.
self.__dict__.update(known_workers[self.id].__dict__)
else:
raise RuntimeError(
"Worker initialized with the same id and different types."
)
else:
hook.local_worker.add_worker(self)
for worker_id, worker in hook.local_worker._known_workers.items(
):
if worker_id not in self._known_workers:
self.add_worker(worker)
if self.id not in worker._known_workers:
worker.add_worker(self)
else:
# Make the local worker aware of itself
# self is the to-be-created local worker
self.add_worker(self)
if hook is None:
self.framework = None
else:
# TODO[jvmancuso]: avoid branching here if possible, maybe by changing code in
# execute_tensor_command or command_guard to not expect an attribute named "torch"
# (#2530)
self.framework = hook.framework
if hasattr(hook, "torch"):
self.torch = self.framework
self.remote = Remote(self, "torch")
elif hasattr(hook, "tensorflow"):
self.tensorflow = self.framework
self.remote = Remote(self, "tensorflow")
# storage object for crypto primitives
self.crypto_store = PrimitiveStorage(owner=self)
# Register the specific handlers for each framework
for _, message_handler_constructor in BaseWorker._framework_message_handler.items(
):
self.message_handlers.append(
message_handler_constructor(self.object_store, self))
self.syft = sy
def __init__(
self,
hook: "FrameworkHook",
id: Union[int, str] = 0,
data: Union[List, tuple] = None,
is_client_worker: bool = False,
log_msgs: bool = False,
verbose: bool = False,
auto_add: bool = True,
message_pending_time: Union[int, float] = 0,
):
"""Initializes a BaseWorker."""
super().__init__()
self.hook = hook
self.object_store = ObjectStore(owner=self)
self.id = id
self.is_client_worker = is_client_worker
self.log_msgs = log_msgs
self.verbose = verbose
self.auto_add = auto_add
self._message_pending_time = message_pending_time
self.msg_history = list()
# For performance, we cache all possible message types
self._message_router = {
TensorCommandMessage: self.execute_tensor_command,
PlanCommandMessage: self.execute_plan_command,
WorkerCommandMessage: self.execute_worker_command,
ObjectMessage: self.handle_object_msg,
ObjectRequestMessage: self.respond_to_obj_req,
ForceObjectDeleteMessage: self.handle_delete_object_msg, # FIXME: there is no ObjectDeleteMessage
ForceObjectDeleteMessage: self.handle_force_delete_object_msg,
IsNoneMessage: self.is_object_none,
GetShapeMessage: self.handle_get_shape_message,
SearchMessage: self.respond_to_search,
}
self._plan_command_router = {
codes.PLAN_CMDS.FETCH_PLAN: self._fetch_plan_remote,
codes.PLAN_CMDS.FETCH_PROTOCOL: self._fetch_protocol_remote,
}
self.load_data(data)
# Declare workers as appropriate
self._known_workers = {}
if auto_add:
if hook is not None and hook.local_worker is not None:
known_workers = self.hook.local_worker._known_workers
if self.id in known_workers:
if isinstance(known_workers[self.id], type(self)):
# If a worker with this id already exists and it has the
# same type as the one being created, we copy all the attributes
# of the existing worker to this one.
self.__dict__.update(known_workers[self.id].__dict__)
else:
raise RuntimeError(
"Worker initialized with the same id and different types."
)
else:
hook.local_worker.add_worker(self)
for worker_id, worker in hook.local_worker._known_workers.items():
if worker_id not in self._known_workers:
self.add_worker(worker)
if self.id not in worker._known_workers:
worker.add_worker(self)
else:
# Make the local worker aware of itself
# self is the to-be-created local worker
self.add_worker(self)
if hook is None:
self.framework = None
else:
# TODO[jvmancuso]: avoid branching here if possible, maybe by changing code in
# execute_tensor_command or command_guard to not expect an attribute named "torch"
# (#2530)
self.framework = hook.framework
if hasattr(hook, "torch"):
self.torch = self.framework
self.remote = Remote(self, "torch")
elif hasattr(hook, "tensorflow"):
self.tensorflow = self.framework
self.remote = Remote(self, "tensorflow")
# storage object for crypto primitives
self.crypto_store = PrimitiveStorage(owner=self)
def __init__(self, datasets=None):
super().__init__()
self.datasets = datasets if datasets is not None else dict()
self.optimizer = None
self.train_config = None
self.object_store = ObjectStore(owner=self)