def job_generate():
#generate a fl job which is the same as fl_master
inputs = [fluid.layers.data( \
name=str(slot_id), shape=[5],
dtype="float32")
for slot_id in range(3)]
label = fluid.layers.data( \
name="label",
shape=[1],
dtype='int64')
model = Model()
model.mlp(inputs, label)
job_generator = JobGenerator()
optimizer = fluid.optimizer.SGD(learning_rate=0.1)
job_generator.set_optimizer(optimizer)
job_generator.set_losses([model.loss])
job_generator.set_startup_program(model.startup_program)
job_generator.set_infer_feed_and_target_names([x.name for x in inputs],
[model.predict.name])
build_strategy = FLStrategyFactory()
build_strategy.fed_avg = True
build_strategy.inner_step = 10
strategy = build_strategy.create_fl_strategy()
# endpoints will be collected through the cluster
# in this example, we suppose endpoints have been collected
server_ip = ["{}".format(ip_list[0])]
output = "job_config"
job_generator.generate_fl_job(
strategy,
server_endpoints=server_ip,
worker_num=int(default_dict["worker_nodes"]),
output=output)
file_list = os.listdir(output)
for file in file_list:
tar = tarfile.open('{}/{}.tar.gz'.format(output, file), 'w:gz')
for root, dir, files in os.walk("{}/{}".format(output, file)):
for f in files:
fullpath = os.path.join(root, f)
tar.add(fullpath)
tar.close()
label = fluid.layers.data( \
name="label",
shape=[1],
dtype='int64')
model = Model()
model.mlp(inputs, label)
job_generator = JobGenerator()
optimizer = fluid.optimizer.SGD(learning_rate=0.1)
job_generator.set_optimizer(optimizer)
job_generator.set_losses([model.loss])
job_generator.set_startup_program(model.startup_program)
job_generator.set_infer_feed_and_target_names([x.name for x in inputs],
[model.predict.name])
build_strategy = FLStrategyFactory()
build_strategy.fed_avg = True
build_strategy.inner_step = 10
strategy = build_strategy.create_fl_strategy()
# endpoints will be collected through the cluster
# in this example, we suppose endpoints have been collected
endpoints = ["127.0.0.1:8181"]
output = "fl_job_config"
job_generator.generate_fl_job(strategy,
server_endpoints=endpoints,
worker_num=2,
output=output)
# fl_job_config will be dispatched to workers
STEP_EPSILON = 0.1
DELTA = 0.00001
SIGMA = math.sqrt(2.0 * math.log(1.25 / DELTA)) / STEP_EPSILON
CLIP = 4.0
batch_size = 64
job_generator = JobGenerator()
optimizer = fluid.optimizer.SGD(learning_rate=0.1)
job_generator.set_optimizer(optimizer)
job_generator.set_losses([model.avg_cost])
job_generator.set_startup_program(model.startup_program)
job_generator.set_infer_feed_and_target_names(
[model.inputs.name, model.label.name], [model.avg_cost.name])
build_strategy = FLStrategyFactory()
build_strategy.dpsgd = True
build_strategy.inner_step = 1
strategy = build_strategy.create_fl_strategy()
strategy.learning_rate = 0.1
strategy.clip = CLIP
strategy.batch_size = float(batch_size)
strategy.sigma = CLIP * SIGMA
# endpoints will be collected through the cluster
# in this example, we suppose endpoints have been collected
endpoints = ["127.0.0.1:8181"]
output = "fl_job_config"
job_generator.generate_fl_job(strategy,
server_endpoints=endpoints,
worker_num=2,
inputs = fluid.layers.data(name='x', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
model = Model()
model.linear_regression(inputs, label)
job_generator = JobGenerator()
optimizer = fluid.optimizer.SGD(learning_rate=0.01)
job_generator.set_optimizer(optimizer)
job_generator.set_losses([model.loss])
job_generator.set_startup_program(model.startup_program)
job_generator.set_infer_feed_and_target_names([inputs.name, label.name],
[model.loss.name])
build_strategy = FLStrategyFactory()
#build_strategy.fed_avg = True
build_strategy.sec_agg = True
param_name_list = []
param_name_list.append(
"fc_0.w_0.opti.trainer_") # need trainer_id when running
param_name_list.append("fc_0.b_0.opti.trainer_")
build_strategy.param_name_list = param_name_list
build_strategy.inner_step = 10
strategy = build_strategy.create_fl_strategy()
# endpoints will be collected through the cluster
# in this example, we suppose endpoints have been collected
endpoints = ["127.0.0.1:8181"]