def call(*curr):
""" Call 'func', replacing missing and defaulted arguments.
Args:
... Forwarded arguments
Returns:
Forwarded returned value
"""
assert len(args) <= len(curr) + len(defs), "Not enough parameters provided in native function call"
return func(*(curr + defs[len(curr) - len(args) + len(defs):]))
return call
# ---------------------------------------------------------------------------- #
# Automatic building and loading
# Register foreign import instance
_register = tools.ClassRegister("foreign import")
itemize_py = _register.itemize
register_py = _register.register
instantiate_py = _register.instantiate
del _register
def _loader_ctypes(so_path):
""" Post-building ctypes loading operations.
Args:
so_path Shared object path
"""
try:
lib = ctypes.CDLL(str(so_path))
register_py(so_path.stem[3:], lambda: lib)
except Exception as err:
with tools.Context(so_path.stem, "warning"):
Args:
nbworkers Total number of workers
nbbyzwrks Declared number of Byzantine workers
args Command line argument list
"""
raise NotImplementedError
def aggregate(self, gradients):
""" Build the gradient aggregation operation of the given gradients.
Args:
gradients Computed gradient tensors
Returns:
Aggregated gradient tensor
"""
raise NotImplementedError
# ---------------------------------------------------------------------------- #
# GAR script loader
# Register instance
_register = tools.ClassRegister("GAR")
itemize = _register.itemize
register = _register.register
instantiate = _register.instantiate
del _register
# Load all local modules
with tools.Context("aggregators", None):
tools.import_directory(pathlib.Path(__file__).parent, globals())
trace Whether to add trace prints for every important step of the computations
Returns:
List of loss tensors associated with 'device_models'
"""
raise NotImplementedError
def accuracy(self, device_dataset, device_model, trace=False):
""" Build an accuracy tensor on the specified devices, placement on parameter server by default.
Args:
device_dataset Dataset device name/function (same instance between calls if same task, i.e. can use 'is')
device_models Model device names/functions, one per worker on the associated task
trace Whether to add trace prints for every important step of the computations
Returns:
Map of metric string name -> aggregated metric tensor associated with 'device_models'
"""
raise NotImplementedError
# ---------------------------------------------------------------------------- #
# Experiment register and loader
# Register instance
_register = tools.ClassRegister("experiment")
itemize = _register.itemize
register = _register.register
instantiate = _register.instantiate
del _register
# Load all local modules
with tools.Context("experiments", None):
tools.import_directory(pathlib.Path(__file__).parent, globals())