def test_raises_no_repeat_and_no_take(self):
with self.assertRaisesRegex(
ValueError, 'Argument client_epochs_per_round is set to -1'):
dataset.construct_character_level_datasets(
client_batch_size=10,
client_epochs_per_round=-1,
max_batches_per_client=-1)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tf.compat.v1.enable_v2_behavior()
tff.framework.set_default_executor(
tff.framework.local_executor_factory(max_fanout=25))
train_clientdata, _ = dataset.construct_character_level_datasets(
FLAGS.client_batch_size, epochs=1)
loss_fn_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
# Need to iterate until we find a client with data.
for client_id in train_clientdata.client_ids:
try:
sample_batch = next(
iter(train_clientdata.create_tf_dataset_for_client(client_id)))
break
except StopIteration:
pass # Client had no batches.
sample_batch = tf.nest.map_structure(lambda t: t.numpy(), sample_batch)
tff_model = tff.learning.from_keras_model(keras_model=model_builder(),
dummy_batch=sample_batch,
loss=loss_fn_builder())
yogi_init_accum_estimate = optimizer_utils.compute_yogi_init(
train_clientdata, tff_model, num_clients=FLAGS.num_clients)
logging.info('Yogi initializer: {:s}'.format(
format(yogi_init_accum_estimate, '10.6E')))
def test_take_with_repeat(self):
shakespeare_train = dataset.construct_character_level_datasets(
client_batch_size=10,
client_epochs_per_round=-1,
max_batches_per_client=10,
shuffle_buffer_size=0)
self.assertEqual(len(shakespeare_train.client_ids), 715)
for i in range(10):
client_ds = shakespeare_train.create_tf_dataset_for_client(
shakespeare_train.client_ids[i])
self.assertEqual(_compute_length_of_dataset(client_ds), 10)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tf.compat.v1.enable_v2_behavior()
tff.framework.set_default_executor(
tff.framework.local_executor_factory(max_fanout=25))
train_clientdata, test_dataset = dataset.construct_character_level_datasets(
FLAGS.client_batch_size, FLAGS.client_epochs_per_round,
FLAGS.sequence_length)
test_dataset = test_dataset.cache()
loss_fn_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
# Need to iterate until we find a client with data.
for client_id in train_clientdata.client_ids:
try:
sample_batch = next(
iter(train_clientdata.create_tf_dataset_for_client(client_id)))
break
except StopIteration:
pass # Client had no batches.
sample_batch = tf.nest.map_structure(lambda t: t.numpy(), sample_batch)
def client_weight_fn(local_outputs):
# Num_tokens is a tensor with type int64[1], to use as a weight need
# a float32 scalar.
return tf.cast(tf.squeeze(local_outputs['num_tokens']), tf.float32)
training_process = iterative_process_builder.from_flags(
dummy_batch=sample_batch,
model_builder=model_builder,
loss_builder=loss_fn_builder,
metrics_builder=metrics_builder,
client_weight_fn=client_weight_fn)
logging.info('Training model:')
logging.info(model_builder().summary())
training_loop.run(
iterative_process=training_process,
client_datasets_fn=training_utils.build_client_datasets_fn(
train_clientdata, FLAGS.clients_per_round),
evaluate_fn=training_utils.build_evaluate_fn(
eval_dataset=test_dataset,
model_builder=model_builder,
loss_builder=loss_fn_builder,
metrics_builder=metrics_builder),
)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tf.compat.v1.enable_v2_behavior()
train_clientdata, test_dataset = dataset.construct_character_level_datasets(
FLAGS.client_batch_size, FLAGS.client_epochs_per_round,
FLAGS.sequence_length)
test_dataset = test_dataset.cache()
loss_fn_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
input_spec = train_clientdata.create_tf_dataset_for_client(
train_clientdata.client_ids[0]).element_spec
def client_weight_fn(local_outputs):
# Num_tokens is a tensor with type int64[1], to use as a weight need
# a float32 scalar.
return tf.cast(tf.squeeze(local_outputs['num_tokens']), tf.float32)
training_process = iterative_process_builder.from_flags(
input_spec=input_spec,
model_builder=model_builder,
loss_builder=loss_fn_builder,
metrics_builder=metrics_builder,
client_weight_fn=client_weight_fn)
client_datasets_fn = training_utils.build_client_datasets_fn(
train_dataset=train_clientdata,
train_clients_per_round=FLAGS.clients_per_round,
random_seed=FLAGS.client_datasets_random_seed)
assign_weights_fn = fed_avg_schedule.ServerState.assign_weights_to_keras_model
evaluate_fn = training_utils.build_evaluate_fn(
eval_dataset=test_dataset,
model_builder=model_builder,
loss_builder=loss_fn_builder,
metrics_builder=metrics_builder,
assign_weights_to_keras_model=assign_weights_fn)
logging.info('Training model:')
logging.info(model_builder().summary())
training_loop.run(iterative_process=training_process,
client_datasets_fn=client_datasets_fn,
evaluate_fn=evaluate_fn)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tff.framework.set_default_executor(
tff.framework.local_executor_factory(max_fanout=25))
train_clientdata, _ = dataset.construct_character_level_datasets(
FLAGS.client_batch_size, epochs=1)
loss_fn_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
input_spec = train_clientdata.create_tf_dataset_for_client(
train_clientdata.client_ids[0])
tff_model = tff.learning.from_keras_model(keras_model=model_builder(),
input_spec=input_spec,
loss=loss_fn_builder())
yogi_init_accum_estimate = optimizer_utils.compute_yogi_init(
train_clientdata, tff_model, num_clients=FLAGS.num_clients)
logging.info('Yogi initializer: {:s}'.format(
format(yogi_init_accum_estimate, '10.6E')))