def main(argv):
if len(argv) > 1:
raise app.UsageError('Expected no command-line arguments, '
'got: {}'.format(argv))
client_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags('client')
server_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags('server')
client_lr_schedule = optimizer_utils.create_lr_schedule_from_flags('client')
server_lr_schedule = optimizer_utils.create_lr_schedule_from_flags('server')
def iterative_process_builder(
model_fn: Callable[[], tff.learning.Model],
client_weight_fn: Optional[Callable[[Any], tf.Tensor]] = None,
dataset_preprocess_comp: Optional[tff.Computation] = None,
) -> tff.templates.IterativeProcess:
"""Creates an iterative process using a given TFF `model_fn`.
Args:
model_fn: A no-arg function returning a `tff.learning.Model`.
client_weight_fn: Optional function that takes the output of
`model.report_local_outputs` and returns a tensor providing the weight
in the federated average of model deltas. If not provided, the default
is the total number of examples processed on device.
dataset_preprocess_comp: Optional `tff.Computation` that sets up a data
pipeline on the clients. The computation must take a squence of values
and return a sequence of values, or in TFF type shorthand `(U* -> V*)`.
If `None`, no dataset preprocessing is applied.
Returns:
A `tff.templates.IterativeProcess`.
"""
return fed_avg_schedule.build_fed_avg_process(
model_fn=model_fn,
client_optimizer_fn=client_optimizer_fn,
client_lr=client_lr_schedule,
server_optimizer_fn=server_optimizer_fn,
server_lr=server_lr_schedule,
client_weight_fn=client_weight_fn,
dataset_preprocess_comp=dataset_preprocess_comp)
assign_weights_fn = fed_avg_schedule.ServerState.assign_weights_to_keras_model
common_args = collections.OrderedDict([
('iterative_process_builder', iterative_process_builder),
('assign_weights_fn', assign_weights_fn),
('client_epochs_per_round', FLAGS.client_epochs_per_round),
('client_batch_size', FLAGS.client_batch_size),
('clients_per_round', FLAGS.clients_per_round),
('client_datasets_random_seed', FLAGS.client_datasets_random_seed)
])
if FLAGS.task == 'cifar100':
federated_cifar100.run_federated(
**common_args, crop_size=FLAGS.cifar100_crop_size)
elif FLAGS.task == 'emnist_cr':
federated_emnist.run_federated(
**common_args, emnist_model=FLAGS.emnist_cr_model)
elif FLAGS.task == 'emnist_ae':
federated_emnist_ae.run_federated(**common_args)
elif FLAGS.task == 'shakespeare':
federated_shakespeare.run_federated(
**common_args, sequence_length=FLAGS.shakespeare_sequence_length)
elif FLAGS.task == 'stackoverflow_nwp':
so_nwp_flags = collections.OrderedDict()
for flag_name in FLAGS:
if flag_name.startswith('so_nwp_'):
so_nwp_flags[flag_name[7:]] = FLAGS[flag_name].value
federated_stackoverflow.run_federated(**common_args, **so_nwp_flags)
elif FLAGS.task == 'stackoverflow_lr':
so_lr_flags = collections.OrderedDict()
for flag_name in FLAGS:
if flag_name.startswith('so_lr_'):
so_lr_flags[flag_name[6:]] = FLAGS[flag_name].value
federated_stackoverflow_lr.run_federated(**common_args, **so_lr_flags)
else:
raise ValueError(
'--task flag {} is not supported, must be one of {}.'.format(
FLAGS.task, _SUPPORTED_TASKS))
def main(argv):
if len(argv) > 1:
raise app.UsageError('Expected no command-line arguments, '
'got: {}'.format(argv))
client_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags(
'client')
server_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags(
'server')
client_lr_callback = callbacks.create_reduce_lr_on_plateau(
learning_rate=FLAGS.client_learning_rate,
decay_factor=FLAGS.client_decay_factor,
min_delta=FLAGS.min_delta,
min_lr=FLAGS.min_lr,
window_size=FLAGS.window_size,
patience=FLAGS.patience)
server_lr_callback = callbacks.create_reduce_lr_on_plateau(
learning_rate=FLAGS.server_learning_rate,
decay_factor=FLAGS.server_decay_factor,
min_delta=FLAGS.min_delta,
min_lr=FLAGS.min_lr,
window_size=FLAGS.window_size,
patience=FLAGS.patience)
def iterative_process_builder(
model_fn: Callable[[], tff.learning.Model],
client_weight_fn: Optional[Callable[[Any], tf.Tensor]] = None,
) -> tff.templates.IterativeProcess:
"""Creates an iterative process using a given TFF `model_fn`.
Args:
model_fn: A no-arg function returning a `tff.learning.Model`.
client_weight_fn: Optional function that takes the output of
`model.report_local_outputs` and returns a tensor providing the weight
in the federated average of model deltas. If not provided, the default
is the total number of examples processed on device.
Returns:
A `tff.templates.IterativeProcess`.
"""
return adaptive_fed_avg.build_fed_avg_process(
model_fn,
client_lr_callback,
server_lr_callback,
client_optimizer_fn=client_optimizer_fn,
server_optimizer_fn=server_optimizer_fn,
client_weight_fn=client_weight_fn)
hparam_dict = utils_impl.lookup_flag_values(utils_impl.get_hparam_flags())
shared_args = utils_impl.lookup_flag_values(shared_flags)
shared_args['iterative_process_builder'] = iterative_process_builder
if FLAGS.task == 'cifar100':
hparam_dict['cifar100_crop_size'] = FLAGS.cifar100_crop_size
federated_cifar100.run_federated(**shared_args,
crop_size=FLAGS.cifar100_crop_size,
hparam_dict=hparam_dict)
elif FLAGS.task == 'emnist_cr':
federated_emnist.run_federated(**shared_args,
emnist_model=FLAGS.emnist_cr_model,
hparam_dict=hparam_dict)
elif FLAGS.task == 'emnist_ae':
federated_emnist_ae.run_federated(**shared_args,
hparam_dict=hparam_dict)
elif FLAGS.task == 'shakespeare':
federated_shakespeare.run_federated(
**shared_args,
sequence_length=FLAGS.shakespeare_sequence_length,
hparam_dict=hparam_dict)
elif FLAGS.task == 'stackoverflow_nwp':
so_nwp_flags = collections.OrderedDict()
for flag_name in task_flags:
if flag_name.startswith('so_nwp_'):
so_nwp_flags[flag_name[7:]] = FLAGS[flag_name].value
federated_stackoverflow.run_federated(**shared_args,
**so_nwp_flags,
hparam_dict=hparam_dict)
elif FLAGS.task == 'stackoverflow_lr':
so_lr_flags = collections.OrderedDict()
for flag_name in task_flags:
if flag_name.startswith('so_lr_'):
so_lr_flags[flag_name[6:]] = FLAGS[flag_name].value
federated_stackoverflow_lr.run_federated(**shared_args,
**so_lr_flags,
hparam_dict=hparam_dict)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Expected no command-line arguments, '
'got: {}'.format(argv))
client_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags('client')
server_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags('server')
client_lr_schedule = optimizer_utils.create_lr_schedule_from_flags('client')
server_lr_schedule = optimizer_utils.create_lr_schedule_from_flags('server')
def iterative_process_builder(
model_fn: Callable[[], tff.learning.Model],
client_weight_fn: Optional[Callable[[Any], tf.Tensor]] = None,
) -> tff.templates.IterativeProcess:
"""Creates an iterative process using a given TFF `model_fn`.
Args:
model_fn: A no-arg function returning a `tff.learning.Model`.
client_weight_fn: Optional function that takes the output of
`model.report_local_outputs` and returns a tensor providing the weight
in the federated average of model deltas. If not provided, the default
is the total number of examples processed on device.
Returns:
A `tff.templates.IterativeProcess`.
"""
return fed_avg_schedule.build_fed_avg_process(
model_fn=model_fn,
client_optimizer_fn=client_optimizer_fn,
client_lr=client_lr_schedule,
server_optimizer_fn=server_optimizer_fn,
server_lr=server_lr_schedule,
client_weight_fn=client_weight_fn)
assign_weights_fn = fed_avg_schedule.ServerState.assign_weights_to_keras_model
hparam_dict = utils_impl.lookup_flag_values(utils_impl.get_hparam_flags())
shared_args = utils_impl.lookup_flag_values(shared_flags)
shared_args['iterative_process_builder'] = iterative_process_builder
shared_args['assign_weights_fn'] = assign_weights_fn
if FLAGS.task == 'cifar100':
hparam_dict['cifar100_crop_size'] = FLAGS.cifar100_crop_size
federated_cifar100.run_federated(
**shared_args,
crop_size=FLAGS.cifar100_crop_size,
hparam_dict=hparam_dict)
elif FLAGS.task == 'emnist_cr':
federated_emnist.run_federated(
**shared_args,
emnist_model=FLAGS.emnist_cr_model,
hparam_dict=hparam_dict)
elif FLAGS.task == 'emnist_ae':
federated_emnist_ae.run_federated(**shared_args, hparam_dict=hparam_dict)
elif FLAGS.task == 'shakespeare':
federated_shakespeare.run_federated(
**shared_args,
sequence_length=FLAGS.shakespeare_sequence_length,
hparam_dict=hparam_dict)
elif FLAGS.task == 'stackoverflow_nwp':
so_nwp_flags = collections.OrderedDict()
for flag_name in task_flags:
if flag_name.startswith('so_nwp_'):
so_nwp_flags[flag_name[7:]] = FLAGS[flag_name].value
federated_stackoverflow.run_federated(
**shared_args, **so_nwp_flags, hparam_dict=hparam_dict)
elif FLAGS.task == 'stackoverflow_lr':
so_lr_flags = collections.OrderedDict()
for flag_name in task_flags:
if flag_name.startswith('so_lr_'):
so_lr_flags[flag_name[6:]] = FLAGS[flag_name].value
federated_stackoverflow_lr.run_federated(
**shared_args, **so_lr_flags, hparam_dict=hparam_dict)
else:
raise ValueError(
'--task flag {} is not supported, must be one of {}.'.format(
FLAGS.task, _SUPPORTED_TASKS))
