Merge pull request #46 from qjing666/travis

add travis CI and pre-commit-config

.pre-commit-config.yaml

+repos:
+-   repo: https://github.com/Lucas-C/pre-commit-hooks.git
+    sha: v1.0.1
+    hooks:
+    -   id: remove-crlf
+        files: (?!.*third_party)^.*$ | (?!.*book)^.*$
+-   repo: https://github.com/PaddlePaddle/mirrors-yapf.git
+    sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
+    hooks:
+    -   id: yapf
+        files: (.*\.(py|bzl)|BUILD|.*\.BUILD|WORKSPACE)$
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    sha: 5bf6c09bfa1297d3692cadd621ef95f1284e33c0
+    hooks:
+    -   id: check-added-large-files
+    -   id: check-merge-conflict
+    -   id: check-symlinks
+    -   id: detect-private-key
+        files: (?!.*third_party)^.*$ | (?!.*book)^.*$
+    -   id: end-of-file-fixer
+-   repo: local
+    hooks:
+    -   id: clang-format-with-version-check
+        name: clang-format
+        description: Format files with ClangFormat.
+        entry: bash ./tools/codestyle/clang_format.hook -i
+        language: system
+        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
+-   repo: local
+    hooks:
+    -   id: cpplint-cpp-source
+        name: cpplint
+        description: Check C++ code style using cpplint.py.
+        entry: bash ./tools/codestyle/cpplint_pre_commit.hook
+        language: system
+        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx)$
+-   repo: local
+    hooks:
+    -   id: pylint-doc-string
+        name: pylint
+        description: Check python docstring style using docstring_checker.
+        entry: bash ./tools/codestyle/pylint_pre_commit.hook
+        language: system
+        files: \.(py)$
+-   repo: local
+    hooks:
+    -   id: copyright_checker
+        name: copyright_checker
+        entry: python ./tools/codestyle/copyright.hook
+        language: system
+        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
+        exclude: (?!.*third_party)^.*$ | (?!.*book)^.*$

.travis.yml

+language:python
+
+notifications:
+  email: 
+    on_success: change
+    on_failure: always
+
+sudo: false
+
+os:
+  - linux

AUTHORS.md

 | Github account | name |
 |---|---|
 | guru4elephant | Daxiang Dong |
-| frankwhzhang | Wenhui Zhang |
+| frankwhzhang | Wenhui Zhang |
\ No newline at end of file
+| qjing666     | Qinghe Jing |

contrib/data_safety_training/image_classification/server/receiver.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import zmq
 import socket
 import msgpack

contrib/data_safety_training/image_classification/server/server.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import print_function
 import os
 import paddle
@@ -12,14 +26,14 @@ import math
 import msgpack
 
 
-def data_generater(samples,r):
-        # data generater
+def data_generater(samples, r):
+    # data generater
     def train_data():
         for item in samples:
             sample = msgpack.loads(r.get(str(item)))
             conv = sample[0]
             label = sample[1]
-            yield conv,label
+            yield conv, label
         return train_data
 
 
@@ -67,7 +81,7 @@ class ResNet():
                 size=class_dim,
                 param_attr=fluid.param_attr.ParamAttr(
                     initializer=fluid.initializer.Uniform(-stdv, stdv)),
-                    act = "softmax")
+                act="softmax")
         else:
             for block in range(len(depth)):
                 for i in range(depth[block]):
@@ -87,7 +101,7 @@ class ResNet():
                 size=class_dim,
                 param_attr=fluid.param_attr.ParamAttr(
                     initializer=fluid.initializer.Uniform(-stdv, stdv)),
-                    act = "softmax")
+                act="softmax")
         return out
 
     def conv_bn_layer(self,
@@ -123,8 +137,6 @@ class ResNet():
             moving_mean_name=bn_name + '_mean',
             moving_variance_name=bn_name + '_variance', )
 
-
-
     def shortcut(self, input, ch_out, stride, is_first, name):
         ch_in = input.shape[1]
         if ch_in != ch_out or stride != 1 or is_first == True:
@@ -181,31 +193,33 @@ class ResNet():
             input, num_filters, stride, is_first, name=name + "_branch1")
         return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
 
+
 # local redis config
 redis_host = "127.0.0.1"
 redis_port = 6379
 redis_password = ""
-r = redis.StrictRedis(host=redis_host, port=redis_port, password=redis_password)
+r = redis.StrictRedis(
+    host=redis_host, port=redis_port, password=redis_password)
 
 # reader generation
-reader = fluid.layers.py_reader(capacity=64,
-                                shapes=[(-1,64, 8, 8), (-1,1)],
-                                dtypes=['float32', 'int64'])
+reader = fluid.layers.py_reader(
+    capacity=64, shapes=[(-1, 64, 8, 8), (-1, 1)],
+    dtypes=['float32', 'int64'])
 
 samples = r.keys()
-train_data = data_generater(samples,r)
+train_data = data_generater(samples, r)
 
-reader.decorate_paddle_reader(paddle.batch(
-                      paddle.reader.shuffle(
-                      train_data, buf_size=5000),
-                      batch_size=64))
+reader.decorate_paddle_reader(
+    paddle.batch(
+        paddle.reader.shuffle(
+            train_data, buf_size=5000), batch_size=64))
 
-conv1,label = fluid.layers.read_file(reader)
+conv1, label = fluid.layers.read_file(reader)
 
 # train program
 place = fluid.CUDAPlace(0)
 model = ResNet(layers=50)
-predicts = model.net(conv1,10)
+predicts = model.net(conv1, 10)
 cost = fluid.layers.cross_entropy(input=predicts, label=label)
 accuracy = fluid.layers.accuracy(input=predicts, label=label)
 loss = fluid.layers.mean(cost)
@@ -222,18 +236,20 @@ step = 0
 train_start = time.time()
 # start training 
 for pass_id in range(EPOCH_NUM):
-     reader.start()
-     try:
-         while True:
-             start_time = time.time()
-             loss_value,acc_value = exe.run(fetch_list=[loss.name,accuracy.name])
-             step += 1
-             if step % 10 == 0:
-                 print("epoch: "+ str(pass_id)+"step: "+str(step)+"loss: "+ str(loss_value)+"acc: "+str(acc_value))
-             end_time = time.time()
-             total_time += (end_time - start_time)
-     except fluid.core.EOFException:
-         reader.reset()
+    reader.start()
+    try:
+        while True:
+            start_time = time.time()
+            loss_value, acc_value = exe.run(
+                fetch_list=[loss.name, accuracy.name])
+            step += 1
+            if step % 10 == 0:
+                print("epoch: " + str(pass_id) + "step: " + str(step) +
+                      "loss: " + str(loss_value) + "acc: " + str(acc_value))
+            end_time = time.time()
+            total_time += (end_time - start_time)
+    except fluid.core.EOFException:
+        reader.reset()
 train_end = time.time()
 print("total time: %d" % (train_end - train_start))
 print("computation time: %d" % total_time)

contrib/data_safety_training/image_classification/server/user.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import print_function
 import os
 import paddle
@@ -5,10 +19,12 @@ import paddle.fluid as fluid
 import numpy
 import sys
 import redis
-import time 
+import time
 from paddle.fluid import layers
 from paddle.fluid.param_attr import ParamAttr
 import msgpack
+
+
 def conv_bn_layer(input,
                   num_filters,
                   filter_size,
@@ -16,30 +32,30 @@ def conv_bn_layer(input,
                   groups=1,
                   act=None,
                   name=None):
-        conv = fluid.layers.conv2d(
-            input=input,
-            num_filters=num_filters,
-            filter_size=filter_size,
-            stride=stride,
-            padding=(filter_size - 1) // 2,
-            groups=groups,
-            act=None,
-            param_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False,
-            name=name + '.conv2d.output.1')
-
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
-            act=act,
-            name=bn_name + '.output.1',
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance', )
+    conv = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        stride=stride,
+        padding=(filter_size - 1) // 2,
+        groups=groups,
+        act=None,
+        param_attr=ParamAttr(name=name + "_weights"),
+        bias_attr=False,
+        name=name + '.conv2d.output.1')
+
+    if name == "conv1":
+        bn_name = "bn_" + name
+    else:
+        bn_name = "bn" + name[3:]
+    return fluid.layers.batch_norm(
+        input=conv,
+        act=act,
+        name=bn_name + '.output.1',
+        param_attr=ParamAttr(name=bn_name + '_scale'),
+        bias_attr=ParamAttr(bn_name + '_offset'),
+        moving_mean_name=bn_name + '_mean',
+        moving_variance_name=bn_name + '_variance', )
 
 
 def load_conf(conf_file, local_dict):
@@ -51,6 +67,7 @@ def load_conf(conf_file, local_dict):
             local_dict[group[0]] = group[1]
     return local_dict
 
+
 # redis DB configuration
 redis_host = "127.0.0.1"
 redis_port = 6379
@@ -58,26 +75,39 @@ redis_password = ""
 
 start_time = time.time()
 # start a redis client and empty the DB
-r = redis.StrictRedis(host=redis_host, port=redis_port, password=redis_password)
+r = redis.StrictRedis(
+    host=redis_host, port=redis_port, password=redis_password)
 r.flushall()
 
 # encoding program
-images = fluid.layers.data(name='images', shape=[3,32,32], dtype='float32')
+images = fluid.layers.data(name='images', shape=[3, 32, 32], dtype='float32')
 label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 place = fluid.CPUPlace()
-conv1 = conv_bn_layer(input=images,num_filters=64,filter_size=7,stride=2,act='relu',name="conv1")
-pool = fluid.layers.pool2d(input=conv1,pool_size=3,pool_stride=2,pool_padding=1,pool_type='max')
-feeder = fluid.DataFeeder(place=place, feed_list=[images,label])
+conv1 = conv_bn_layer(
+    input=images,
+    num_filters=64,
+    filter_size=7,
+    stride=2,
+    act='relu',
+    name="conv1")
+pool = fluid.layers.pool2d(
+    input=conv1, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
 pretrained_model = 'ResNet50_pretrained'
 exe = fluid.Executor(place)
 exe.run(fluid.default_startup_program())
 
+
 # load pretrained mode and prepare datal
 def if_exist(var):
-        return os.path.exists(os.path.join(pretrained_model, var.name))
-fluid.io.load_vars(exe, pretrained_model, main_program=fluid.default_main_program(),
-                  predicate=if_exist)
+    return os.path.exists(os.path.join(pretrained_model, var.name))
+
 
+fluid.io.load_vars(
+    exe,
+    pretrained_model,
+    main_program=fluid.default_main_program(),
+    predicate=if_exist)
 
 train_data = paddle.dataset.cifar.train10()
 step = 0
@@ -86,11 +116,13 @@ step = 0
 for data in train_data():
     pre_data = []
     pre_data.append(data)
-    res = exe.run(program=fluid.default_main_program(),feed=feeder.feed(pre_data), fetch_list=[pool.name])
-    sample = [res[0][0].tolist(),data[1]]
+    res = exe.run(program=fluid.default_main_program(),
+                  feed=feeder.feed(pre_data),
+                  fetch_list=[pool.name])
+    sample = [res[0][0].tolist(), data[1]]
     step += 1
     file = msgpack.dumps(sample)
-    r.set(step,file)
+    r.set(step, file)
     if step % 100 == 0:
         print(numpy.array(sample[0]).shape)
         print("%dstart" % step)
@@ -99,6 +131,4 @@ files = r.keys()
 print("upload file numbers: %d" % len(files))
 end_time = time.time()
 total_time = end_time - start_time
-print("total time: %d"% total_time)
-
-
+print("total time: %d" % total_time)

contrib/data_safety_training/image_classification/submitter.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import zmq
 import socket
 import msgpack
 import os
-mission_dict = {"mission": "image classification", "image_size": [3,32,32]}
+mission_dict = {"mission": "image classification", "image_size": [3, 32, 32]}
 #send request
 context = zmq.Context()
 zmq_socket = context.socket(zmq.REQ)

docs/requirements.txt

@@ -3,4 +3,3 @@ mistune
 sphinx_rtd_theme
 paddlepaddle>=1.6
 zmq
-

docs/source/examples/md/dpsgd-example.md

@@ -181,4 +181,3 @@ while not trainer.stop():
 To show the effectiveness of DPSGD-based federated learning with PaddleFL, a simulated experiment is conducted on an open source dataset MNIST. From the figure given below, model evaluation results are similar between DPSGD-based federated learning and traditional parameter server training when the overall privacy budget *epsilon* is 1.3 or 0.13. 
 
 <img src="fl_dpsgd_benchmark.png" height=400 width=600 hspace='10'/> <br />
-

docs/source/examples/md/gru4rec_examples.md

@@ -103,6 +103,3 @@ wget https://paddle-zwh.bj.bcebos.com/gru4rec_paddlefl_benchmark/gru4rec_benchma
 | 1/4 of the whole dataset | private training | - | 0.282 | 
 
 <img src="fl_benchmark.png" height=300 width=500 hspace='10'/> <br />
-
-
-

docs/source/md/introduction.md

@@ -55,4 +55,3 @@ In PaddleFL, components for defining a federated learning task and training a fe
 - Federated Learning Systems deployment methods in Kubernetes.
 
 - Vertical Federated Learning Strategies and more horizontal federated learning strategies will be open sourced.
-

docs/source/md/reference.md

@@ -14,4 +14,4 @@
 
 [7]. Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar. **Federated Multi-Task Learning** 2016
 
-[8]. Yang Liu, Tianjian Chen, Qiang Yang. **Secure Federated Transfer Learning.** 2018
+[8]. Yang Liu, Tianjian Chen, Qiang Yang. **Secure Federated Transfer Learning.** 2018
\ No newline at end of file

paddle_fl/common/__init__.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

paddle_fl/core/__init__.py

@@ -22,4 +22,3 @@ from .scheduler.agent_master import FLWorkerAgent
 from .scheduler.agent_master import FLScheduler
 from .submitter.client_base import HPCClient
 from .submitter.client_base import CloudClient
-

paddle_fl/core/master/fl_job.py

@@ -14,11 +14,13 @@
 import os
 import paddle.fluid as fluid
 
+
 class FLJobBase(object):
     """
     FLJobBase is fl job base class, responsible for save and load
     a federated learning job
     """
+
     def __init__(self):
         pass
 
@@ -64,6 +66,7 @@ class FLJobBase(object):
             return fluid.Program.parse_from_string(program_desc_str)
         return None
 
+
 class FLCompileTimeJob(FLJobBase):
     """
     FLCompileTimeJob is a container for compile time job in federated learning.
@@ -71,6 +74,7 @@ class FLCompileTimeJob(FLJobBase):
     are in FLCompileTimeJob. Also, server main programs and server startup programs
     are in this class. FLCompileTimeJob has server endpoints for debugging as well
     """
+
     def __init__(self):
         self._trainer_startup_programs = []
         self._trainer_recv_programs = []
@@ -101,69 +105,59 @@ class FLCompileTimeJob(FLJobBase):
             os.system("mkdir -p %s" % server_folder)
             server_startup = self._server_startup_programs[i]
             server_main = self._server_main_programs[i]
-            self._save_program(
-                server_startup,
-                "%s/server.startup.program" % server_folder)
-            self._save_program(
-                server_main,
-                "%s/server.main.program" % server_folder)
+            self._save_program(server_startup,
+                               "%s/server.startup.program" % server_folder)
+            self._save_program(server_main,
+                               "%s/server.main.program" % server_folder)
+            self._save_readable_program(server_startup,
+                                        "%s/server.startup.program.txt" %
+                                        server_folder)
             self._save_readable_program(
-                server_startup,
-                "%s/server.startup.program.txt" % server_folder)
-            self._save_readable_program(
-                server_main,
-                "%s/server.main.program.txt" % server_folder)
+                server_main, "%s/server.main.program.txt" % server_folder)
             self._save_str_list(self._feed_names,
-                            "%s/feed_names" % server_folder)
+                                "%s/feed_names" % server_folder)
             self._save_str_list(self._target_names,
-                            "%s/target_names" % server_folder)
+                                "%s/target_names" % server_folder)
             self._save_endpoints(self._server_endpoints,
-                            "%s/endpoints" % server_folder)
+                                 "%s/endpoints" % server_folder)
             self._save_strategy(self._strategy,
-                           "%s/strategy.pkl" % server_folder)
+                                "%s/strategy.pkl" % server_folder)
 
         for i in range(trainer_num):
             trainer_folder = "%s/trainer%d" % (folder, i)
             os.system("mkdir -p %s" % trainer_folder)
             trainer_startup = self._trainer_startup_programs[i]
             trainer_main = self._trainer_main_programs[i]
-            self._save_program(
-                trainer_startup,
-                "%s/trainer.startup.program" % trainer_folder)
-            self._save_program(
-                trainer_main,
-                "%s/trainer.main.program" % trainer_folder)
-            self._save_readable_program(
-                trainer_startup,
-                "%s/trainer.startup.program.txt" % trainer_folder)
+            self._save_program(trainer_startup,
+                               "%s/trainer.startup.program" % trainer_folder)
+            self._save_program(trainer_main,
+                               "%s/trainer.main.program" % trainer_folder)
+            self._save_readable_program(trainer_startup,
+                                        "%s/trainer.startup.program.txt" %
+                                        trainer_folder)
             self._save_readable_program(
-                trainer_main,
-                "%s/trainer.main.program.txt" % trainer_folder)
+                trainer_main, "%s/trainer.main.program.txt" % trainer_folder)
             self._save_str_list(self._feed_names,
-                            "%s/feed_names" % trainer_folder)
+                                "%s/feed_names" % trainer_folder)
             self._save_str_list(self._target_names,
-                            "%s/target_names" % trainer_folder)
+                                "%s/target_names" % trainer_folder)
             self._save_endpoints(self._server_endpoints,
-                            "%s/endpoints" % trainer_folder)
+                                 "%s/endpoints" % trainer_folder)
             self._save_strategy(self._strategy,
-                           "%s/strategy.pkl" % trainer_folder)
+                                "%s/strategy.pkl" % trainer_folder)
 
         for i in range(send_prog_num):
             trainer_folder = "%s/trainer%d" % (folder, i)
             trainer_send = self._trainer_send_programs[i]
             trainer_recv = self._trainer_recv_programs[i]
-            self._save_program(
-                trainer_send,
-                "%s/trainer.send.program" % trainer_folder)
-            self._save_program(
-                trainer_recv,
-                "%s/trainer.recv.program" % trainer_folder)
+            self._save_program(trainer_send,
+                               "%s/trainer.send.program" % trainer_folder)
+            self._save_program(trainer_recv,
+                               "%s/trainer.recv.program" % trainer_folder)
             self._save_readable_program(
-                trainer_send,
-                "%s/trainer.send.program.txt" % trainer_folder)
+                trainer_send, "%s/trainer.send.program.txt" % trainer_folder)
             self._save_readable_program(
-                trainer_recv,
-                "%s/trainer.recv.program.txt" % trainer_folder)
+                trainer_recv, "%s/trainer.recv.program.txt" % trainer_folder)
 
 
 class FLRunTimeJob(FLJobBase):
@@ -172,6 +166,7 @@ class FLRunTimeJob(FLJobBase):
     A trainer or a server can load FLRunTimeJob. Only necessary programs
      can be loaded in FLRunTimeJob
     """
+
     def __init__(self):
         self._trainer_startup_program = None
         self._trainer_recv_program = None

paddle_fl/core/master/job_generator.py

@@ -14,6 +14,7 @@
 import paddle.fluid as fluid
 from .fl_job import FLCompileTimeJob
 
+
 class JobGenerator(object):
     """
     A JobGenerator is responsible for generating distributed federated
@@ -21,6 +22,7 @@ class JobGenerator(object):
     need to define a deep learning model together to do horizontal federated
     learning.
     """
+
     def __init__(self):
         # worker num for federated learning
         self._worker_num = 0
@@ -32,7 +34,6 @@ class JobGenerator(object):
         self._feed_names = []
         self._target_names = []
 
-
     def set_optimizer(self, optimizer):
         """
         Set optimizer of current job
@@ -56,8 +57,10 @@ class JobGenerator(object):
         self._startup_prog = startup
 
     def set_infer_feed_and_target_names(self, feed_names, target_names):
-        if not isinstance(feed_names, list) or not isinstance(target_names, list):
-            raise ValueError("input should be list in set_infer_feed_and_target_names")
+        if not isinstance(feed_names, list) or not isinstance(target_names,
+                                                              list):
+            raise ValueError(
+                "input should be list in set_infer_feed_and_target_names")
         '''
         print(feed_names)
         print(target_names)
@@ -76,7 +79,6 @@ class JobGenerator(object):
                         server_endpoints=[],
                         worker_num=1,
                         output=None):
-
         """
         Generate Federated Learning Job, based on user defined configs
 
@@ -130,17 +132,66 @@ class JobGenerator(object):
             startup_program = self._startup_prog.clone()
             main_program = self._losses[0].block.program.clone()
             fl_strategy._build_trainer_program_for_job(
-                trainer_id, program=main_program,
-                ps_endpoints=server_endpoints, trainers=worker_num,
-                sync_mode=True, startup_program=startup_program,
+                trainer_id,
+                program=main_program,
+                ps_endpoints=server_endpoints,
+                trainers=worker_num,
+                sync_mode=True,
+                startup_program=startup_program,
+                job=local_job)
+
+        startup_program = self._startup_prog.clone()
+        main_program = self._losses[0].block.program.clone()
+        fl_strategy._build_server_programs_for_job(
+            program=main_program,
+            ps_endpoints=server_endpoints,
+            trainers=worker_num,
+            sync_mode=True,
+            startup_program=startup_program,
+            job=local_job)
+
+        local_job.set_feed_names(self._feed_names)
+        local_job.set_target_names(self._target_names)
+        local_job.set_strategy(fl_strategy)
+        local_job.save(output)
+
+    def generate_fl_job_for_k8s(self,
+                                fl_strategy,
+                                server_pod_endpoints=[],
+                                server_service_endpoints=[],
+                                worker_num=1,
+                                output=None):
+
+        local_job = FLCompileTimeJob()
+        assert len(self._losses) > 0
+        assert self._startup_prog != None
+        assert fl_strategy != None
+        assert output != None
+        fl_strategy.minimize(self._optimizer, self._losses)
+
+        # strategy can generate startup and main program
+        # of a single worker and servers
+        for trainer_id in range(worker_num):
+            startup_program = self._startup_prog.clone()
+            main_program = self._losses[0].block.program.clone()
+            fl_strategy._build_trainer_program_for_job(
+                trainer_id,
+                program=main_program,
+                ps_endpoints=server_service_endpoints,
+                trainers=worker_num,
+                sync_mode=True,
+                startup_program=startup_program,
                 job=local_job)
 
         startup_program = self._startup_prog.clone()
         main_program = self._losses[0].block.program.clone()
         fl_strategy._build_server_programs_for_job(
-            program=main_program, ps_endpoints=server_endpoints,
-            trainers=worker_num, sync_mode=True,
-            startup_program=startup_program, job=local_job)
+            program=main_program,
+            ps_endpoints=server_pod_endpoints,
+            trainers=worker_num,
+            sync_mode=True,
+            startup_program=startup_program,
+            job=local_job)
 
         local_job.set_feed_names(self._feed_names)
         local_job.set_target_names(self._target_names)

paddle_fl/core/scheduler/agent_master.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import zmq
 import time
 import random
 
+
 def recv_and_parse_kv(socket):
     message = socket.recv()
     group = message.decode().split("\t")
@@ -10,9 +25,11 @@ def recv_and_parse_kv(socket):
     else:
         return group[0], group[1]
 
+
 WORKER_EP = "WORKER_EP"
 SERVER_EP = "SERVER_EP"
 
+
 class FLServerAgent(object):
     def __init__(self, scheduler_ep, current_ep):
         self.scheduler_ep = scheduler_ep
@@ -29,6 +46,7 @@ class FLServerAgent(object):
             if group[0] == 'INIT':
                 break
 
+
 class FLWorkerAgent(object):
     def __init__(self, scheduler_ep, current_ep):
         self.scheduler_ep = scheduler_ep
@@ -64,7 +82,6 @@ class FLWorkerAgent(object):
         return False
 
 
-
 class FLScheduler(object):
     def __init__(self, worker_num, server_num, port=9091, socket=None):
         self.context = zmq.Context()

paddle_fl/core/server/fl_server.py

@@ -14,8 +14,8 @@
 import paddle.fluid as fluid
 from paddle_fl.core.scheduler.agent_master import FLServerAgent
 
-class FLServer(object):
 
+class FLServer(object):
     def __init__(self):
         self._startup_program = None
         self._main_program = None

paddle_fl/core/strategy/details/checkport.py

@@ -48,8 +48,8 @@ def wait_server_ready(endpoints):
                     not_ready_endpoints.append(ep)
         if not all_ok:
             sys.stderr.write("server not ready, wait 3 sec to retry...\n")
-            sys.stderr.write("not ready endpoints:" + str(not_ready_endpoints) +
-                             "\n")
+            sys.stderr.write("not ready endpoints:" + str(not_ready_endpoints)
+                             + "\n")
             sys.stderr.flush()
             time.sleep(3)
         else:

paddle_fl/core/strategy/details/program_utils.py

@@ -163,7 +163,8 @@ def block_to_code(block, block_idx, fout=None, skip_op_callstack=False):
     indent = 0
 
     print(
-        "{0}{1} // block {2}".format(get_indent_space(indent), '{', block_idx),
+        "{0}{1} // block {2}".format(
+            get_indent_space(indent), '{', block_idx),
         file=fout)
 
     indent += 1

paddle_fl/core/strategy/fl_distribute_transpiler.py

@@ -50,6 +50,7 @@ def log(*args):
     if PRINT_LOG:
         print(args)
 
+
 def same_or_split_var(p_name, var_name):
     return p_name == var_name or p_name.startswith(var_name + ".block")
 
@@ -113,7 +114,9 @@ class FLDistributeTranspiler(object):
 
     def _get_all_remote_sparse_update_op(self, main_program):
         sparse_update_ops = []
-        sparse_update_op_types = ["lookup_table", "nce", "hierarchical_sigmoid"]
+        sparse_update_op_types = [
+            "lookup_table", "nce", "hierarchical_sigmoid"
+        ]
         for op in main_program.global_block().ops:
             if op.type in sparse_update_op_types and op.attr(
                     'remote_prefetch') is True:
@@ -406,12 +409,13 @@ class FLDistributeTranspiler(object):
             # NOTE: single_trainer_var must be created for multi-trainer
             # case to merge grads from multiple trainers
             single_trainer_var = pserver_program.global_block().var(
-                    orig_var_name)
+                orig_var_name)
 
             if self.sync_mode and self.trainer_num > 1:
                 for trainer_id in range(self.trainer_num):
                     var = pserver_program.global_block().create_var(
-                        name="%s.opti.trainer_%d" % (orig_var_name, trainer_id),
+                        name="%s.opti.trainer_%d" %
+                        (orig_var_name, trainer_id),
                         persistable=False,
                         type=v.type,
                         dtype=v.dtype,
@@ -816,7 +820,6 @@ class FLDistributeTranspiler(object):
         iomap = collections.OrderedDict()
         return iomap
 
-
     def _get_lr_ops(self):
         lr_ops = []
         block = self.origin_program.global_block()

paddle_fl/core/strategy/fl_strategy_base.py

@@ -16,11 +16,13 @@ from .fl_distribute_transpiler import FLDistributeTranspiler
 from paddle.fluid.optimizer import SGD
 import paddle.fluid as fluid
 
+
 class FLStrategyFactory(object):
     """
     FLStrategyFactory is a FLStrategy builder
     Users can define strategy config to create different FLStrategy
     """
+
     def __init__(self):
         self._fed_avg = False
         self._dpsgd = False
@@ -86,6 +88,7 @@ class FLStrategyBase(object):
     """
     FLStrategyBase is federated learning algorithm container
     """
+
     def __init__(self):
         self._fed_avg = False
         self._dpsgd = False
@@ -105,17 +108,23 @@ class FLStrategyBase(object):
         for loss in losses:
             optimizer.minimize(loss)
 
-    def _build_trainer_program_for_job(
-            self, trainer_id=0, program=None,
-            ps_endpoints=[], trainers=0,
-            sync_mode=True, startup_program=None,
-            job=None):
+    def _build_trainer_program_for_job(self,
+                                       trainer_id=0,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         pass
 
-    def _build_server_programs_for_job(
-            self, program=None, ps_endpoints=[],
-            trainers=0, sync_mode=True,
-            startup_program=None, job=None):
+    def _build_server_programs_for_job(self,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         pass
 
 
@@ -123,6 +132,7 @@ class DPSGDStrategy(FLStrategyBase):
     """
     DPSGDStrategy: Deep Learning with Differential Privacy. 2016
     """
+
     def __init__(self):
         super(DPSGDStrategy, self).__init__()
 
@@ -162,29 +172,40 @@ class DPSGDStrategy(FLStrategyBase):
         """
         Define Dpsgd optimizer
         """
-        optimizer = fluid.optimizer.Dpsgd(self._learning_rate, clip=self._clip, batch_size=self._batch_size, sigma=self._sigma)
+        optimizer = fluid.optimizer.Dpsgd(
+            self._learning_rate,
+            clip=self._clip,
+            batch_size=self._batch_size,
+            sigma=self._sigma)
         optimizer.minimize(losses[0])
 
-    def _build_trainer_program_for_job(
-            self, trainer_id=0, program=None,
-            ps_endpoints=[], trainers=0,
-            sync_mode=True, startup_program=None,
-            job=None):
+    def _build_trainer_program_for_job(self,
+                                       trainer_id=0,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         transpiler = fluid.DistributeTranspiler()
-        transpiler.transpile(trainer_id,
-                             program=program,
-                             pservers=",".join(ps_endpoints),
-                             trainers=trainers,
-                             sync_mode=sync_mode,
-                             startup_program=startup_program)
+        transpiler.transpile(
+            trainer_id,
+            program=program,
+            pservers=",".join(ps_endpoints),
+            trainers=trainers,
+            sync_mode=sync_mode,
+            startup_program=startup_program)
         main = transpiler.get_trainer_program(wait_port=False)
         job._trainer_startup_programs.append(startup_program)
         job._trainer_main_programs.append(main)
 
-    def _build_server_programs_for_job(
-            self, program=None, ps_endpoints=[],
-            trainers=0, sync_mode=True,
-            startup_program=None, job=None):
+    def _build_server_programs_for_job(self,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         transpiler = fluid.DistributeTranspiler()
         trainer_id = 0
         transpiler.transpile(
@@ -207,6 +228,7 @@ class FedAvgStrategy(FLStrategyBase):
     FedAvgStrategy: this is model averaging optimization proposed in
     H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Aguera y Arcas. Federated Learning of Deep Networks using Model Averaging. 2017
     """
+
     def __init__(self):
         super(FedAvgStrategy, self).__init__()
 
@@ -216,28 +238,35 @@ class FedAvgStrategy(FLStrategyBase):
         """
         optimizer.minimize(losses[0])
 
-    def _build_trainer_program_for_job(
-            self, trainer_id=0, program=None,
-            ps_endpoints=[], trainers=0,
-            sync_mode=True, startup_program=None,
-            job=None):
+    def _build_trainer_program_for_job(self,
+                                       trainer_id=0,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         transpiler = FLDistributeTranspiler()
-        transpiler.transpile(trainer_id,
-                             program=program,
-                             pservers=",".join(ps_endpoints),
-                             trainers=trainers,
-                             sync_mode=sync_mode,
-                             startup_program=startup_program)
+        transpiler.transpile(
+            trainer_id,
+            program=program,
+            pservers=",".join(ps_endpoints),
+            trainers=trainers,
+            sync_mode=sync_mode,
+            startup_program=startup_program)
         recv, main, send = transpiler.get_trainer_program()
         job._trainer_startup_programs.append(startup_program)
         job._trainer_main_programs.append(main)
         job._trainer_send_programs.append(send)
         job._trainer_recv_programs.append(recv)
 
-    def _build_server_programs_for_job(
-            self, program=None, ps_endpoints=[],
-            trainers=0, sync_mode=True,
-            startup_program=None, job=None):
+    def _build_server_programs_for_job(self,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         transpiler = FLDistributeTranspiler()
         trainer_id = 0
         transpiler.transpile(
@@ -262,6 +291,7 @@ class SecAggStrategy(FedAvgStrategy):
     Practical Secure Aggregation  for Privacy-Preserving Machine Learning,
     The 24th ACM Conference on Computer and Communications Security ( CCS2017 ).
     """
+
     def __init__(self):
         super(SecAggStrategy, self).__init__()
         self._param_name_list = []

paddle_fl/core/submitter/client_base.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import sys
 import os
 
+
 class CloudClient(object):
     def __init__(self):
         pass
-    
+
     def generate_submit_sh(self, job_dir):
         with open() as fout:
             pass
@@ -16,6 +31,7 @@ class CloudClient(object):
     def submit(self, **kwargs):
         pass
 
+
 class HPCClient(object):
     def __init__(self):
         self.conf_dict = {}
@@ -70,27 +86,20 @@ class HPCClient(object):
             fout.write("#!/bin/bash\n")
             fout.write("unset http_proxy\n")
             fout.write("unset https_proxy\n")
-            fout.write("export HADOOP_HOME={}\n".format(
-                self.hadoop_home))
+            fout.write("export HADOOP_HOME={}\n".format(self.hadoop_home))
             fout.write("$HADOOP_HOME/bin/hadoop fs -Dhadoop.job.ugi={}"
                        " -Dfs.default.name={} -rmr {}\n".format(
-                           self.ugi,
-                           self.hdfs_path,
-                           self.hdfs_output))
+                           self.ugi, self.hdfs_path, self.hdfs_output))
             fout.write("MPI_NODE_MEM={}\n".format(self.mpi_node_mem))
             fout.write("{}/bin/qsub_f -N {} --conf qsub.conf "
                        "--hdfs {} --ugi {} --hout {} --files ./package "
                        "-l nodes={},walltime=1000:00:00,pmem-hard={},"
                        "pcpu-soft={},pnetin-soft=1000,"
                        "pnetout-soft=1000 job.sh\n".format(
-                           self.hpc_home,
-                           self.task_name,
-                           self.hdfs_path,
-                           self.ugi,
-                           self.hdfs_output,
+                           self.hpc_home, self.task_name, self.hdfs_path,
+                           self.ugi, self.hdfs_output,
                            int(self.worker_nodes) + int(self.server_nodes),
-                           self.mpi_node_mem,
-                           self.pcpu))
+                           self.mpi_node_mem, self.pcpu))
 
     def generate_job_sh(self, job_dir):
         with open("{}/job.sh".format(job_dir), "w") as fout:
@@ -98,17 +107,23 @@ class HPCClient(object):
             fout.write("WORKDIR=`pwd`\n")
             fout.write("mpirun -npernode 1 mv package/* ./\n")
             fout.write("echo 'current dir: '$WORKDIR\n")
-            fout.write("mpirun -npernode 1 tar -zxvf python.tar.gz > /dev/null\n")
-            fout.write("export LIBRARY_PATH=$WORKDIR/python/lib:$LIBRARY_PATH\n")
+            fout.write(
+                "mpirun -npernode 1 tar -zxvf python.tar.gz > /dev/null\n")
+            fout.write(
+                "export LIBRARY_PATH=$WORKDIR/python/lib:$LIBRARY_PATH\n")
             fout.write("mpirun -npernode 1 python/bin/python -m pip install "
                        "{} --index-url=http://pip.baidu.com/pypi/simple "
                        "--trusted-host pip.baidu.com > /dev/null\n".format(
                            self.wheel))
             fout.write("export PATH=python/bin:$PATH\n")
             if self.monitor_cmd != "":
-                fout.write("mpirun -npernode 1 -timestamp-output -tag-output -machinefile "
-                           "${{PBS_NODEFILE}} python/bin/{} > monitor.log 2> monitor.elog &\n".format(self.monitor_cmd))
-            fout.write("mpirun -npernode 1 -timestamp-output -tag-output -machinefile ${PBS_NODEFILE} python/bin/python train_program.py\n")
+                fout.write(
+                    "mpirun -npernode 1 -timestamp-output -tag-output -machinefile "
+                    "${{PBS_NODEFILE}} python/bin/{} > monitor.log 2> monitor.elog &\n".
+                    format(self.monitor_cmd))
+            fout.write(
+                "mpirun -npernode 1 -timestamp-output -tag-output -machinefile ${PBS_NODEFILE} python/bin/python train_program.py\n"
+            )
             fout.write("if [[ $? -ne 0 ]]; then\n")
             fout.write("    echo 'Failed to run mpi!' 1>&2\n")
             fout.write("    exit 1\n")
@@ -150,4 +165,5 @@ class HPCClient(object):
         # generate job.sh
         self.generate_qsub_conf(jobdir)
         # run submit
-        os.system("cd {};sh submit.sh > submit.log 2> submit.elog &".format(jobdir))
+        os.system("cd {};sh submit.sh > submit.log 2> submit.elog &".format(
+            jobdir))

paddle_fl/core/trainer/diffiehellman/__init__.py

@@ -2,7 +2,6 @@
 
 #
 # (c) Chris von Csefalvay, 2015.
-
 """
 __init__.py is responsible for [brief description here].
 """

paddle_fl/core/trainer/diffiehellman/decorators.py

 # coding=utf-8
 
-
 #
 # The MIT License (MIT)
 #
@@ -21,8 +20,6 @@
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 #
-
-
 """
 decorators declares some decorators that ensure the object has the
 correct keys declared when need be.

paddle_fl/core/trainer/diffiehellman/diffiehellman.py

@@ -20,10 +20,6 @@
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 #
-
-
-
-
 """
 diffiehellmann declares the main key exchange class.
 """
@@ -41,18 +37,17 @@ import os
 try:
     from ssl import RAND_bytes
     rng = RAND_bytes
-except(AttributeError, ImportError):
+except (AttributeError, ImportError):
     rng = os.urandom
 
+
 class DiffieHellman:
     """
     Implements the Diffie-Hellman key exchange protocol.
 
     """
 
-    def __init__(self,
-                 group=18,
-                 key_length=640):
+    def __init__(self, group=18, key_length=640):
 
         self.key_length = max(200, key_length)
         self.generator = PRIMES[group]["generator"]
@@ -81,7 +76,8 @@ class DiffieHellman:
         self.private_key = key
 
     def verify_public_key(self, other_public_key):
-        return self.prime - 1 > other_public_key > 2 and pow(other_public_key, (self.prime - 1) // 2, self.prime) == 1
+        return self.prime - 1 > other_public_key > 2 and pow(
+            other_public_key, (self.prime - 1) // 2, self.prime) == 1
 
     @requires_private_key
     def generate_public_key(self):
@@ -91,9 +87,7 @@ class DiffieHellman:
         :return: void
         :rtype: void
         """
-        self.public_key = pow(self.generator,
-                              self.private_key,
-                              self.prime)
+        self.public_key = pow(self.generator, self.private_key, self.prime)
 
     @requires_private_key
     def generate_shared_secret(self, other_public_key, echo_return_key=False):
@@ -110,16 +104,17 @@ class DiffieHellman:
         if self.verify_public_key(other_public_key) is False:
             raise MalformedPublicKey
 
-        self.shared_secret = pow(other_public_key,
-                                 self.private_key,
+        self.shared_secret = pow(other_public_key, self.private_key,
                                  self.prime)
         try:
             #python3
-            shared_secret_as_bytes = self.shared_secret.to_bytes(self.shared_secret.bit_length() // 8 + 1, byteorder='big')
+            shared_secret_as_bytes = self.shared_secret.to_bytes(
+                self.shared_secret.bit_length() // 8 + 1, byteorder='big')
         except:
             #python2
             length = self.shared_secret.bit_length() // 8 + 1
-            shared_secret_as_bytes = ('%%0%dx' % (length << 1) % self.shared_secret).decode('hex')[-length:]
+            shared_secret_as_bytes = ('%%0%dx' % (
+                length << 1) % self.shared_secret).decode('hex')[-length:]
         _h = sha256()
         _h.update(bytes(shared_secret_as_bytes))
 

paddle_fl/core/trainer/diffiehellman/exceptions.py

@@ -2,7 +2,6 @@
 
 #
 # (c) Chris von Csefalvay, 2015.
-
 """
 exceptions is responsible for exception handling etc.
 """

paddle_fl/core/trainer/diffiehellman/primes.py

 # coding=utf-8
 
-
 #
 # The MIT License (MIT)
 #
@@ -25,34 +24,39 @@
 # Extracted from: Kivinen, T. and Kojo, M. (2003), _More Modular Exponential (MODP) Diffie-Hellman
 #                                                   groups for Internet Key Exchange (IKE)_.
 #
-
 """
 primes holds the RFC 3526 MODP primes and their generators.
 """
 
 PRIMES = {
     5: {
-        "prime": 0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF,
+        "prime":
+        0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF,
         "generator": 2
     },
     14: {
-        "prime": 0x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
+        "prime":
+        0x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
         "generator": 2
     },
     15: {
-        "prime": 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
+        "prime":
+        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
         "generator": 2
     },
     16: {
-        "prime": 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
+        "prime":
+        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
         "generator": 2
     },
     17: {
-        "prime": 0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF6955817183995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200CBBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C93402849236C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AACC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E6DCC4024FFFFFFFFFFFFFFFF,
+        "prime":
+        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
         "generator": 2
     },
     18: {
-        "prime": 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
+        "prime":
+        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
         "generator": 2
     },
 }

paddle_fl/core/trainer/fl_trainer.py

@@ -19,6 +19,7 @@ import hmac
 import hashlib
 from .diffiehellman.diffiehellman import DiffieHellman
 
+
 class FLTrainerFactory(object):
     def __init__(self):
         pass
@@ -65,9 +66,7 @@ class FLTrainer(object):
 
     def run(self, feed, fetch):
         self._logger.debug("begin to run")
-        self.exe.run(self._main_program,
-                      feed=feed,
-                      fetch_list=fetch)
+        self.exe.run(self._main_program, feed=feed, fetch_list=fetch)
         self._logger.debug("end to run current batch")
         self.cur_step += 1
 
@@ -119,37 +118,34 @@ class FedAvgTrainer(FLTrainer):
     def reset(self):
         self.cur_step = 0
 
-    def run_with_epoch(self,reader,feeder,fetch,num_epoch):
+    def run_with_epoch(self, reader, feeder, fetch, num_epoch):
         self._logger.debug("begin to run recv program")
         self.exe.run(self._recv_program)
         epoch = 0
         for i in range(num_epoch):
-                for data in reader():
-                    self.exe.run(self._main_program,
-                           feed=feeder.feed(data),
-                           fetch_list=fetch)
-                self.cur_step += 1
-                epoch += 1
+            for data in reader():
+                self.exe.run(self._main_program,
+                             feed=feeder.feed(data),
+                             fetch_list=fetch)
+            self.cur_step += 1
+            epoch += 1
         self._logger.debug("begin to run send program")
         self.exe.run(self._send_program)
+
     def run(self, feed, fetch):
-        self._logger.debug("begin to run FedAvgTrainer, cur_step=%d, inner_step=%d" %
-                           (self.cur_step, self._step))
+        self._logger.debug(
+            "begin to run FedAvgTrainer, cur_step=%d, inner_step=%d" %
+            (self.cur_step, self._step))
         if self.cur_step % self._step == 0:
             self._logger.debug("begin to run recv program")
             self.exe.run(self._recv_program)
         self._logger.debug("begin to run current step")
-        loss = self.exe.run(self._main_program,
-                     feed=feed,
-                     fetch_list=fetch)
+        loss = self.exe.run(self._main_program, feed=feed, fetch_list=fetch)
         if self.cur_step % self._step == 0:
             self._logger.debug("begin to run send program")
             self.exe.run(self._send_program)
         self.cur_step += 1
         return loss
-       
- 
-
 
 
 class SecAggTrainer(FLTrainer):
@@ -207,24 +203,24 @@ class SecAggTrainer(FLTrainer):
         self.cur_step = 0
 
     def run(self, feed, fetch):
-        self._logger.debug("begin to run SecAggTrainer, cur_step=%d, inner_step=%d" %
-                           (self.cur_step, self._step))
+        self._logger.debug(
+            "begin to run SecAggTrainer, cur_step=%d, inner_step=%d" %
+            (self.cur_step, self._step))
         if self.cur_step % self._step == 0:
             self._logger.debug("begin to run recv program")
             self.exe.run(self._recv_program)
         scope = fluid.global_scope()
         self._logger.debug("begin to run current step")
-        loss = self.exe.run(self._main_program,
-                     feed=feed,
-                     fetch_list=fetch)
+        loss = self.exe.run(self._main_program, feed=feed, fetch_list=fetch)
         if self.cur_step % self._step == 0:
             self._logger.debug("begin to run send program")
             noise = 0.0
             scale = pow(10.0, 5)
-            digestmod=hashlib.sha256
+            digestmod = hashlib.sha256
             # 1. load priv key and other's pub key
             dh = DiffieHellman(group=15, key_length=256)
-            dh.load_private_key(self._key_dir + str(self._trainer_id) + "_priv_key.txt")
+            dh.load_private_key(self._key_dir + str(self._trainer_id) +
+                                "_priv_key.txt")
             key = str(self._step_id).encode("utf-8")
             for i in range(self._trainer_num):
                 if i != self._trainer_id:
@@ -232,7 +228,8 @@ class SecAggTrainer(FLTrainer):
                     public_key = int(f.read())
                     dh.generate_shared_secret(public_key, echo_return_key=True)
                     msg = dh.shared_key.encode("utf-8")
-                    hex_res1 = hmac.new(key=key, msg=msg, digestmod=digestmod).hexdigest()
+                    hex_res1 = hmac.new(key=key, msg=msg,
+                                        digestmod=digestmod).hexdigest()
                     current_noise = int(hex_res1[0:8], 16) / scale
                     if i > self._trainer_id:
                         noise = noise + current_noise
@@ -241,9 +238,11 @@ class SecAggTrainer(FLTrainer):
 
             scope = fluid.global_scope()
             for param_name in self._param_name_list:
-                fluid.global_scope().var(param_name + str(self._trainer_id)).get_tensor().set(
-                    numpy.array(scope.find_var(param_name + str(self._trainer_id)).get_tensor()) + noise, fluid.CPUPlace())
+                fluid.global_scope().var(param_name + str(
+                    self._trainer_id)).get_tensor().set(
+                        numpy.array(
+                            scope.find_var(param_name + str(self._trainer_id))
+                            .get_tensor()) + noise, fluid.CPUPlace())
             self.exe.run(self._send_program)
         self.cur_step += 1
         return loss
-

paddle_fl/dataset/__init__.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

paddle_fl/dataset/femnist.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import requests
 import os
 import json
@@ -5,73 +19,84 @@ import tarfile
 import random
 
 
-def download(url,tar_path):
-	r = requests.get(url)
-	with open(tar_path,'wb') as f:
-		f.write(r.content)
-
-def extract(tar_path,target_path):
-	tar = tarfile.open(tar_path, "r:gz")
-	file_names = tar.getnames()
-	for file_name in file_names:
-		tar.extract(file_name,target_path)
-
-	tar.close()
-
-def train(trainer_id,inner_step,batch_size,count_by_step):
-        target_path = "trainer%d_data" % trainer_id
-        data_path = target_path + "/femnist_data"
-        tar_path = data_path + ".tar.gz"
-        if not os.path.exists(target_path):
-            os.system("mkdir trainer%d_data" % trainer_id)
-        if not os.path.exists(data_path):
-            print("Preparing data...")
-            if not os.path.exists(tar_path):
-               download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",tar_path)
-               extract(tar_path,target_path)
-        def train_data():
-                train_file = open("./trainer%d_data/femnist_data/train/all_data_%d_niid_0_keep_0_train_9.json" % (trainer_id,trainer_id),'r')
-                json_train = json.load(train_file)
-                users = json_train["users"]
-                rand = random.randrange(0,len(users)) # random choose a user from each trainer
-                cur_user = users[rand]
-                print('training using '+cur_user)
-                train_images = json_train["user_data"][cur_user]['x']
-                train_labels = json_train["user_data"][cur_user]['y']
-                if count_by_step:
-                        for i in range(inner_step*batch_size):
-                                yield train_images[i%(len(train_images))], train_labels[i%(len(train_images))]
-                else:
-                        for i in range(len(train_images)):
-                                yield train_images[i], train_labels[i]
-
-                train_file.close()
-
-        return train_data
-
-def test(trainer_id,inner_step,batch_size,count_by_step):
-        target_path = "trainer%d_data" % trainer_id
-        data_path = target_path + "/femnist_data"
-        tar_path = data_path + ".tar.gz"
-        if not os.path.exists(target_path):
-            os.system("mkdir trainer%d_data" % trainer_id)
-        if not os.path.exists(data_path):
-                print("Preparing data...")
-                if not os.path.exists(tar_path):
-                        download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",tar_path)
-                extract(tar_path,target_path)
-        def test_data():
-                test_file = open("./trainer%d_data/femnist_data/test/all_data_%d_niid_0_keep_0_test_9.json" % (trainer_id,trainer_id), 'r')
-                json_test = json.load(test_file)
-                users = json_test["users"]
-                for user in users:
-                        test_images = json_test['user_data'][user]['x']
-                        test_labels = json_test['user_data'][user]['y']
-                        for i in range(len(test_images)):
-                                yield test_images[i], test_labels[i]
-
-                test_file.close()
-
-        return test_data
-
-	
+def download(url, tar_path):
+    r = requests.get(url)
+    with open(tar_path, 'wb') as f:
+        f.write(r.content)
+
+
+def extract(tar_path, target_path):
+    tar = tarfile.open(tar_path, "r:gz")
+    file_names = tar.getnames()
+    for file_name in file_names:
+        tar.extract(file_name, target_path)
+
+    tar.close()
+
+
+def train(trainer_id, inner_step, batch_size, count_by_step):
+    target_path = "trainer%d_data" % trainer_id
+    data_path = target_path + "/femnist_data"
+    tar_path = data_path + ".tar.gz"
+    if not os.path.exists(target_path):
+        os.system("mkdir trainer%d_data" % trainer_id)
+    if not os.path.exists(data_path):
+        print("Preparing data...")
+        if not os.path.exists(tar_path):
+            download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",
+                     tar_path)
+            extract(tar_path, target_path)
+
+    def train_data():
+        train_file = open(
+            "./trainer%d_data/femnist_data/train/all_data_%d_niid_0_keep_0_train_9.json"
+            % (trainer_id, trainer_id), 'r')
+        json_train = json.load(train_file)
+        users = json_train["users"]
+        rand = random.randrange(
+            0, len(users))  # random choose a user from each trainer
+        cur_user = users[rand]
+        print('training using ' + cur_user)
+        train_images = json_train["user_data"][cur_user]['x']
+        train_labels = json_train["user_data"][cur_user]['y']
+        if count_by_step:
+            for i in range(inner_step * batch_size):
+                yield train_images[i % (len(train_images))], train_labels[i % (
+                    len(train_images))]
+        else:
+            for i in range(len(train_images)):
+                yield train_images[i], train_labels[i]
+
+        train_file.close()
+
+    return train_data
+
+
+def test(trainer_id, inner_step, batch_size, count_by_step):
+    target_path = "trainer%d_data" % trainer_id
+    data_path = target_path + "/femnist_data"
+    tar_path = data_path + ".tar.gz"
+    if not os.path.exists(target_path):
+        os.system("mkdir trainer%d_data" % trainer_id)
+    if not os.path.exists(data_path):
+        print("Preparing data...")
+        if not os.path.exists(tar_path):
+            download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",
+                     tar_path)
+        extract(tar_path, target_path)
+
+    def test_data():
+        test_file = open(
+            "./trainer%d_data/femnist_data/test/all_data_%d_niid_0_keep_0_test_9.json"
+            % (trainer_id, trainer_id), 'r')
+        json_test = json.load(test_file)
+        users = json_test["users"]
+        for user in users:
+            test_images = json_test['user_data'][user]['x']
+            test_labels = json_test['user_data'][user]['y']
+            for i in range(len(test_images)):
+                yield test_images[i], test_labels[i]
+
+        test_file.close()
+
+    return test_data

paddle_fl/examples/ctr_demo/fl_master.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.fluid as fluid
 import paddle_fl as fl
 from paddle_fl.core.master.job_generator import JobGenerator
 from paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
 
+
 class Model(object):
     def __init__(self):
         pass
@@ -12,7 +27,8 @@ class Model(object):
         self.fc1 = fluid.layers.fc(input=self.concat, size=256, act='relu')
         self.fc2 = fluid.layers.fc(input=self.fc1, size=128, act='relu')
         self.predict = fluid.layers.fc(input=self.fc2, size=2, act='softmax')
-        self.sum_cost = fluid.layers.cross_entropy(input=self.predict, label=label)
+        self.sum_cost = fluid.layers.cross_entropy(
+            input=self.predict, label=label)
         self.accuracy = fluid.layers.accuracy(input=self.predict, label=label)
         self.loss = fluid.layers.reduce_mean(self.sum_cost)
         self.startup_program = fluid.default_startup_program()
@@ -34,8 +50,8 @@ 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])
+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

paddle_fl/examples/ctr_demo/fl_scheduler.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 2
 server_num = 1
 # Define the number of worker/server and the port for scheduler
-scheduler = FLScheduler(worker_num,server_num,port=9091) 
+scheduler = FLScheduler(worker_num, server_num, port=9091)
 scheduler.set_sample_worker_num(worker_num)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/ctr_demo/fl_server.py

@@ -21,8 +21,8 @@ server_id = 0
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_server_job(job_path, server_id)
-job._scheduler_ep = "127.0.0.1:9091" # IP address for scheduler
+job._scheduler_ep = "127.0.0.1:9091"  # IP address for scheduler
 server.set_server_job(job)
-server._current_ep = "127.0.0.1:8181" # IP address for server
+server._current_ep = "127.0.0.1:8181"  # IP address for server
 server.start()
 print("connect")

paddle_fl/examples/ctr_demo/fl_trainer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
 from paddle_fl.core.master.fl_job import FLRunTimeJob
 import numpy as np
 import sys
 import logging
 import time
-logging.basicConfig(filename="test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
+logging.basicConfig(
+    filename="test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
 
 
 def reader():
@@ -15,13 +34,14 @@ def reader():
         data_dict["label"] = np.random.randint(2, size=(1, 1)).astype('int64')
         yield data_dict
 
-trainer_id = int(sys.argv[1]) # trainer id for each guest
+
+trainer_id = int(sys.argv[1])  # trainer id for each guest
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_trainer_job(job_path, trainer_id)
-job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
+job._scheduler_ep = "127.0.0.1:9091"  # Inform the scheduler IP to trainer
 trainer = FLTrainerFactory().create_fl_trainer(job)
-trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
 trainer.start()
 print(trainer._scheduler_ep, trainer._current_ep)
 output_folder = "fl_model"
@@ -37,4 +57,3 @@ while not trainer.stop():
     epoch_id += 1
     if epoch_id % 5 == 0:
         trainer.save_inference_program(output_folder)
-    

paddle_fl/examples/dpsgd_demo/fl_master.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.fluid as fluid
 import paddle_fl as fl
 from paddle_fl.core.master.job_generator import JobGenerator
 from paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
 import math
 
+
 class Model(object):
     def __init__(self):
         pass
 
     def lr_network(self):
-        self.inputs = fluid.layers.data(name='img', shape=[1, 28, 28], dtype="float32")
-        self.label = fluid.layers.data(name='label', shape=[1],dtype='int64')
-        self.predict = fluid.layers.fc(input=self.inputs, size=10, act='softmax')
-        self.sum_cost = fluid.layers.cross_entropy(input=self.predict, label=self.label)
-        self.accuracy = fluid.layers.accuracy(input=self.predict, label=self.label)
+        self.inputs = fluid.layers.data(
+            name='img', shape=[1, 28, 28], dtype="float32")
+        self.label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        self.predict = fluid.layers.fc(input=self.inputs,
+                                       size=10,
+                                       act='softmax')
+        self.sum_cost = fluid.layers.cross_entropy(
+            input=self.predict, label=self.label)
+        self.accuracy = fluid.layers.accuracy(
+            input=self.predict, label=self.label)
         self.loss = fluid.layers.mean(self.sum_cost)
         self.startup_program = fluid.default_startup_program()
 
@@ -23,7 +43,7 @@ model.lr_network()
 
 STEP_EPSILON = 0.1
 DELTA = 0.00001
-SIGMA = math.sqrt(2.0 * math.log(1.25/DELTA)) / STEP_EPSILON
+SIGMA = math.sqrt(2.0 * math.log(1.25 / DELTA)) / STEP_EPSILON
 CLIP = 4.0
 batch_size = 64
 
@@ -33,7 +53,8 @@ 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(
-    [model.inputs.name, model.label.name], [model.loss.name, model.accuracy.name])
+    [model.inputs.name, model.label.name],
+    [model.loss.name, model.accuracy.name])
 
 build_strategy = FLStrategyFactory()
 build_strategy.dpsgd = True

paddle_fl/examples/dpsgd_demo/fl_scheduler.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 4
 server_num = 1
 #Define number of worker/server and the port for scheduler
-scheduler = FLScheduler(worker_num,server_num,port=9091) 
+scheduler = FLScheduler(worker_num, server_num, port=9091)
 scheduler.set_sample_worker_num(4)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/dpsgd_demo/fl_server.py

@@ -21,7 +21,7 @@ server_id = 0
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_server_job(job_path, server_id)
-job._scheduler_ep = "127.0.0.1:9091" # IP address for scheduler
+job._scheduler_ep = "127.0.0.1:9091"  # IP address for scheduler
 server.set_server_job(job)
-server._current_ep = "127.0.0.1:8181" # IP address for server
+server._current_ep = "127.0.0.1:8181"  # IP address for server
 server.start()

paddle_fl/examples/dpsgd_demo/fl_trainer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
 from paddle_fl.core.master.fl_job import FLRunTimeJob
 import numpy
@@ -7,44 +21,51 @@ import paddle.fluid as fluid
 import logging
 import math
 
-logging.basicConfig(filename="test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
+logging.basicConfig(
+    filename="test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
 
-trainer_id = int(sys.argv[1]) # trainer id for each guest
+trainer_id = int(sys.argv[1])  # trainer id for each guest
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_trainer_job(job_path, trainer_id)
-job._scheduler_ep = "127.0.0.1:9091" # Inform scheduler IP address to trainer
+job._scheduler_ep = "127.0.0.1:9091"  # Inform scheduler IP address to trainer
 trainer = FLTrainerFactory().create_fl_trainer(job)
-trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
 trainer.start()
 
 test_program = trainer._main_program.clone(for_test=True)
 
 train_reader = paddle.batch(
-        paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500),
-        batch_size=64)
-test_reader = paddle.batch(
-        paddle.dataset.mnist.test(), batch_size=64)
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=500),
+    batch_size=64)
+test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
 
 img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
 label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 feeder = fluid.DataFeeder(feed_list=[img, label], place=fluid.CPUPlace())
 
+
 def train_test(train_test_program, train_test_feed, train_test_reader):
     acc_set = []
     for test_data in train_test_reader():
-        acc_np = trainer.exe.run(
-            program=train_test_program,
-            feed=train_test_feed.feed(test_data),
-            fetch_list=["accuracy_0.tmp_0"])
+        acc_np = trainer.exe.run(program=train_test_program,
+                                 feed=train_test_feed.feed(test_data),
+                                 fetch_list=["accuracy_0.tmp_0"])
         acc_set.append(float(acc_np[0]))
     acc_val_mean = numpy.array(acc_set).mean()
     return acc_val_mean
 
+
 def compute_privacy_budget(sample_ratio, epsilon, step, delta):
     E = 2 * epsilon * math.sqrt(step * sample_ratio)
     print("({0}, {1})-DP".format(E, delta))
 
+
 output_folder = "model_node%d" % trainer_id
 epoch_id = 0
 step = 0
@@ -64,7 +85,8 @@ while not trainer.stop():
         train_test_feed=feeder)
 
     print("Test with epoch %d, accuracy: %s" % (epoch_id, acc_val))
-    compute_privacy_budget(sample_ratio=0.001, epsilon=0.1, step=step, delta=0.00001)
+    compute_privacy_budget(
+        sample_ratio=0.001, epsilon=0.1, step=step, delta=0.00001)
 
     save_dir = (output_folder + "/epoch_%d") % epoch_id
     trainer.save_inference_program(output_folder)

paddle_fl/examples/femnist_demo/fl_master.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.fluid as fluid
 import paddle_fl as fl
 from paddle_fl.core.master.job_generator import JobGenerator
@@ -9,14 +23,31 @@ class Model(object):
         pass
 
     def cnn(self):
-        self.inputs = fluid.layers.data(name='img', shape=[1, 28, 28], dtype="float32")
-        self.label = fluid.layers.data(name='label', shape=[1],dtype='int64')
-        self.conv_pool_1 = fluid.nets.simple_img_conv_pool(input=self.inputs,num_filters=20,filter_size=5,pool_size=2,pool_stride=2,act='relu')
-        self.conv_pool_2 = fluid.nets.simple_img_conv_pool(input=self.conv_pool_1,num_filters=50,filter_size=5,pool_size=2,pool_stride=2,act='relu')
-
-        self.predict = self.predict = fluid.layers.fc(input=self.conv_pool_2, size=62, act='softmax')
-        self.cost = fluid.layers.cross_entropy(input=self.predict, label=self.label)
-        self.accuracy = fluid.layers.accuracy(input=self.predict, label=self.label)
+        self.inputs = fluid.layers.data(
+            name='img', shape=[1, 28, 28], dtype="float32")
+        self.label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        self.conv_pool_1 = fluid.nets.simple_img_conv_pool(
+            input=self.inputs,
+            num_filters=20,
+            filter_size=5,
+            pool_size=2,
+            pool_stride=2,
+            act='relu')
+        self.conv_pool_2 = fluid.nets.simple_img_conv_pool(
+            input=self.conv_pool_1,
+            num_filters=50,
+            filter_size=5,
+            pool_size=2,
+            pool_stride=2,
+            act='relu')
+
+        self.predict = self.predict = fluid.layers.fc(input=self.conv_pool_2,
+                                                      size=62,
+                                                      act='softmax')
+        self.cost = fluid.layers.cross_entropy(
+            input=self.predict, label=self.label)
+        self.accuracy = fluid.layers.accuracy(
+            input=self.predict, label=self.label)
         self.loss = fluid.layers.mean(self.cost)
         self.startup_program = fluid.default_startup_program()
 
@@ -30,8 +61,8 @@ 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(
-    [model.inputs.name, model.label.name], [model.loss.name, model.accuracy.name])
-
+    [model.inputs.name, model.label.name],
+    [model.loss.name, model.accuracy.name])
 
 build_strategy = FLStrategyFactory()
 build_strategy.fed_avg = True

paddle_fl/examples/femnist_demo/fl_scheduler.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 4
 server_num = 1
 # Define the number of worker/server and the port for scheduler
-scheduler = FLScheduler(worker_num,server_num,port=9091)
+scheduler = FLScheduler(worker_num, server_num, port=9091)
 scheduler.set_sample_worker_num(4)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/femnist_demo/fl_server.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle_fl as fl
 import paddle.fluid as fluid
 from paddle_fl.core.server.fl_server import FLServer
@@ -7,7 +21,7 @@ server_id = 0
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_server_job(job_path, server_id)
-job._scheduler_ep = "127.0.0.1:9091" # IP address for scheduler
+job._scheduler_ep = "127.0.0.1:9091"  # IP address for scheduler
 server.set_server_job(job)
-server._current_ep = "127.0.0.1:8181" # IP address for server
+server._current_ep = "127.0.0.1:8181"  # IP address for server
 server.start()

paddle_fl/examples/femnist_demo/fl_trainer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
 from paddle_fl.core.master.fl_job import FLRunTimeJob
 import paddle_fl.dataset.femnist
@@ -8,16 +22,21 @@ import paddle.fluid as fluid
 import logging
 import math
 
-logging.basicConfig(filename="test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
+logging.basicConfig(
+    filename="test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
 
-trainer_id = int(sys.argv[1]) # trainer id for each guest
+trainer_id = int(sys.argv[1])  # trainer id for each guest
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_trainer_job(job_path, trainer_id)
-job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
+job._scheduler_ep = "127.0.0.1:9091"  # Inform the scheduler IP to trainer
 print(job._target_names)
 trainer = FLTrainerFactory().create_fl_trainer(job)
-trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
 trainer.start()
 print(trainer._step)
 test_program = trainer._main_program.clone(for_test=True)
@@ -26,26 +45,26 @@ img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
 label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 feeder = fluid.DataFeeder(feed_list=[img, label], place=fluid.CPUPlace())
 
+
 def train_test(train_test_program, train_test_feed, train_test_reader):
-        acc_set = []
-        for test_data in train_test_reader():
-            acc_np = trainer.exe.run(
-                program=train_test_program,
-                feed=train_test_feed.feed(test_data),
-                fetch_list=["accuracy_0.tmp_0"])
-            acc_set.append(float(acc_np[0]))
-        acc_val_mean = numpy.array(acc_set).mean()
-        return acc_val_mean
+    acc_set = []
+    for test_data in train_test_reader():
+        acc_np = trainer.exe.run(program=train_test_program,
+                                 feed=train_test_feed.feed(test_data),
+                                 fetch_list=["accuracy_0.tmp_0"])
+        acc_set.append(float(acc_np[0]))
+    acc_val_mean = numpy.array(acc_set).mean()
+    return acc_val_mean
+
 
 epoch_id = 0
 step = 0
 epoch = 3000
 count_by_step = False
 if count_by_step:
-	output_folder = "model_node%d" % trainer_id
-else: 
-	output_folder = "model_node%d_epoch" % trainer_id
-	
+    output_folder = "model_node%d" % trainer_id
+else:
+    output_folder = "model_node%d_epoch" % trainer_id
 
 while not trainer.stop():
     count = 0
@@ -55,24 +74,35 @@ while not trainer.stop():
     print("epoch %d start train" % (epoch_id))
     #train_data,test_data= data_generater(trainer_id,inner_step=trainer._step,batch_size=64,count_by_step=count_by_step)
     train_reader = paddle.batch(
-        paddle.reader.shuffle(paddle_fl.dataset.femnist.train(trainer_id,inner_step=trainer._step,batch_size=64,count_by_step=count_by_step), buf_size=500),
+        paddle.reader.shuffle(
+            paddle_fl.dataset.femnist.train(
+                trainer_id,
+                inner_step=trainer._step,
+                batch_size=64,
+                count_by_step=count_by_step),
+            buf_size=500),
         batch_size=64)
 
     test_reader = paddle.batch(
-        paddle_fl.dataset.femnist.test(trainer_id,inner_step=trainer._step,batch_size=64,count_by_step=count_by_step), batch_size=64) 
-    
+        paddle_fl.dataset.femnist.test(
+            trainer_id,
+            inner_step=trainer._step,
+            batch_size=64,
+            count_by_step=count_by_step),
+        batch_size=64)
+
     if count_by_step:
-    	for step_id, data in enumerate(train_reader()):
+        for step_id, data in enumerate(train_reader()):
             acc = trainer.run(feeder.feed(data), fetch=["accuracy_0.tmp_0"])
             step += 1
             count += 1
             print(count)
-            if count % trainer._step == 0: 
+            if count % trainer._step == 0:
                 break
     # print("acc:%.3f" % (acc[0]))
     else:
-        trainer.run_with_epoch(train_reader,feeder,fetch=["accuracy_0.tmp_0"],num_epoch=1) 
-    
+        trainer.run_with_epoch(
+            train_reader, feeder, fetch=["accuracy_0.tmp_0"], num_epoch=1)
 
     acc_val = train_test(
         train_test_program=test_program,
@@ -80,6 +110,6 @@ while not trainer.stop():
         train_test_feed=feeder)
 
     print("Test with epoch %d, accuracy: %s" % (epoch_id, acc_val))
-    if trainer_id == 0:  
+    if trainer_id == 0:
         save_dir = (output_folder + "/epoch_%d") % epoch_id
         trainer.save_inference_program(output_folder)

paddle_fl/examples/gru4rec_demo/fl_master.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.fluid as fluid
 import paddle_fl as fl
 from paddle_fl.core.master.job_generator import JobGenerator
 from paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
 
+
 class Model(object):
     def __init__(self):
         pass
@@ -34,7 +49,8 @@ class Model(object):
                               size=hid_size * 3,
                               param_attr=fluid.ParamAttr(
                                   initializer=fluid.initializer.Uniform(
-                                      low=init_low_bound, high=init_high_bound),
+                                      low=init_low_bound,
+                                      high=init_high_bound),
                                   learning_rate=gru_lr_x))
         gru_h0 = fluid.layers.dynamic_gru(
             input=fc0,
@@ -45,12 +61,13 @@ class Model(object):
                 learning_rate=gru_lr_x))
 
         self.fc = fluid.layers.fc(input=gru_h0,
-                             size=vocab_size,
-                             act='softmax',
-                             param_attr=fluid.ParamAttr(
-                                 initializer=fluid.initializer.Uniform(
-                                     low=init_low_bound, high=init_high_bound),
-                                 learning_rate=fc_lr_x))
+                                  size=vocab_size,
+                                  act='softmax',
+                                  param_attr=fluid.ParamAttr(
+                                      initializer=fluid.initializer.Uniform(
+                                          low=init_low_bound,
+                                          high=init_high_bound),
+                                      learning_rate=fc_lr_x))
         cost = fluid.layers.cross_entropy(
             input=self.fc, label=self.dst_wordseq)
         self.acc = fluid.layers.accuracy(
@@ -59,7 +76,6 @@ class Model(object):
         self.startup_program = fluid.default_startup_program()
 
 
-
 model = Model()
 model.gru4rec_network()
 
@@ -69,7 +85,8 @@ 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(
-    [model.src_wordseq.name, model.dst_wordseq.name], [model.loss.name, model.acc.name])
+    [model.src_wordseq.name, model.dst_wordseq.name],
+    [model.loss.name, model.acc.name])
 
 build_strategy = FLStrategyFactory()
 build_strategy.fed_avg = True

paddle_fl/examples/gru4rec_demo/fl_scheduler.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 4
 server_num = 1
 # Define the number of worker/server and the port for scheduler
-scheduler = FLScheduler(worker_num,server_num,port=9091)
+scheduler = FLScheduler(worker_num, server_num, port=9091)
 scheduler.set_sample_worker_num(4)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/gru4rec_demo/fl_server.py

@@ -21,7 +21,7 @@ server_id = 0
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_server_job(job_path, server_id)
-job._scheduler_ep = "127.0.0.1:9091" # IP address for scheduler
+job._scheduler_ep = "127.0.0.1:9091"  # IP address for scheduler
 server.set_server_job(job)
-server._current_ep = "127.0.0.1:8181" # IP address for server
+server._current_ep = "127.0.0.1:8181"  # IP address for server
 server.start()

paddle_fl/examples/gru4rec_demo/fl_trainer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
 from paddle_fl.core.master.fl_job import FLRunTimeJob
 from paddle_fl.reader.gru4rec_reader import Gru4rec_Reader
@@ -6,21 +20,26 @@ import numpy as np
 import sys
 import os
 import logging
-logging.basicConfig(filename="test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
+logging.basicConfig(
+    filename="test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
 
-trainer_id = int(sys.argv[1]) # trainer id for each guest
+trainer_id = int(sys.argv[1])  # trainer id for each guest
 place = fluid.CPUPlace()
 train_file_dir = "mid_data/node4/%d/" % trainer_id
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_trainer_job(job_path, trainer_id)
-job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
+job._scheduler_ep = "127.0.0.1:9091"  # Inform the scheduler IP to trainer
 trainer = FLTrainerFactory().create_fl_trainer(job)
-trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
 trainer.start()
 
 r = Gru4rec_Reader()
-train_reader = r.reader(train_file_dir, place, batch_size = 125)
+train_reader = r.reader(train_file_dir, place, batch_size=125)
 
 output_folder = "model_node4"
 step_i = 0
@@ -30,8 +49,7 @@ while not trainer.stop():
     train_step = 0
     for data in train_reader():
         #print(np.array(data['src_wordseq']))
-        ret_avg_cost = trainer.run(feed=data,
-                    fetch=["mean_0.tmp_0"])
+        ret_avg_cost = trainer.run(feed=data, fetch=["mean_0.tmp_0"])
         train_step += 1
         if train_step == trainer._step:
             break

paddle_fl/examples/k8s_deployment/master.yaml

+apiVersion: v1
+kind: Service
+metadata:
+  name: fl-master
+spec:
+  type: LoadBalancer
+  ports:
+    - name: fl-master
+      port: 8000
+      targetPort: 8000
+  selector:
+    app: fl-master
+
+---
+
+apiVersion: v1
+kind: Service
+metadata:
+  name: fl-scheduler
+spec:
+  type: LoadBalancer
+  ports:
+    - name: fl-scheduler
+      port: 9091
+      targetPort: 9091
+  selector:
+    app: fl-scheduler
+
+---
+
+apiVersion: v1
+kind: Service
+metadata:
+  name: fl-server
+spec:
+  type: LoadBalancer
+  ports:
+    - name: fl-server
+      port: 8181
+      targetPort: 8181
+  selector:
+    app: fl-server
+
+---
+
+apiVersion: v1
+kind: Service
+metadata:
+  name: trainer0
+spec:
+  type: LoadBalancer
+  ports:
+    - name: trainer0
+      port: 9000
+      targetPort: 9000
+  selector:
+    app: trainer0
+
+---
+
+apiVersion: v1
+kind: Service
+metadata:
+  name: trainer1
+spec:
+  type: LoadBalancer
+  ports:
+    - name: trainer1
+      port: 9001
+      targetPort: 9001
+  selector:
+    app: trainer1
+
+---
+
+apiVersion: apps/v1beta1
+kind: Deployment
+metadata:
+  name: fl-master
+  labels:
+    app: fl-master
+spec:
+  replicas: 1
+  template:
+    metadata:
+      name: fl-master
+      labels:
+        app: fl-master
+    spec:
+      containers:
+      - name: fl-master
+        image: hub.baidubce.com/paddlefl/paddlefl:v3
+        imagePullPolicy: Always
+        ports:
+        - containerPort: 8000
+        workingDir: /root/k8s_deployment/master
+        command: ['/bin/bash']
+        args: ['run_master.sh']
+
+---
+
+apiVersion: apps/v1beta1
+kind: Deployment
+metadata:
+  name: fl-scheduler
+  labels:
+    app: fl-scheduler
+spec:
+  replicas: 1
+  template:
+    metadata:
+      name: fl-scheduler
+      labels:
+        app: fl-scheduler
+    spec:
+      containers:
+      - name: fl-scheduler
+        image: hub.baidubce.com/paddlefl/paddlefl:v3
+        imagePullPolicy: Always
+        ports:
+        - containerPort: 9091
+        workingDir: /root/k8s_deployment/scheduler
+        command: ['/bin/bash']
+        args: ['run_scheduler.sh']
+
+---
+
+apiVersion: apps/v1beta1
+kind: Deployment
+metadata:
+  name: fl-server
+  labels:
+    app: fl-server
+spec:
+  replicas: 1
+  template:
+    metadata:
+      name: fl-server
+      labels:
+        app: fl-server
+    spec:
+      containers:
+      - name: fl-server
+        image: hub.baidubce.com/paddlefl/paddlefl:v3
+        imagePullPolicy: Always
+        ports:
+        - containerPort: 8181
+        workingDir: /root/k8s_deployment/server
+        command: ['/bin/bash']
+        args: ['run_server.sh']
+        env:
+          - name: POD_IP
+            valueFrom:
+              fieldRef:
+                apiVersion: v1
+                fieldPath: status.podIP
+---
+
+apiVersion: apps/v1beta1
+kind: Deployment
+metadata:
+  name: trainer0
+  labels:
+    app: trainer0
+spec:
+  replicas: 1
+  template:
+    metadata:
+      name: trainer0
+      labels:
+        app: trainer0
+    spec:
+      containers:
+      - name: trainer0
+        image: hub.baidubce.com/paddlefl/paddlefl:v3
+        imagePullPolicy: Always
+        ports:
+        - containerPort: 9000
+        workingDir: /root/k8s_deployment/trainer0
+        command: ['/bin/bash']
+        args: ['test_trainer.sh']
+
+---
+
+apiVersion: apps/v1beta1
+kind: Deployment
+metadata:
+  name: trainer1
+  labels:
+    app: trainer1
+spec:
+  replicas: 1
+  template:
+    metadata:
+      name: trainer1
+      labels:
+        app: trainer1
+    spec:
+      containers:
+      - name: trainer1
+        image: hub.baidubce.com/paddlefl/paddlefl:v3
+        imagePullPolicy: Always
+        ports:
+        - containerPort: 9001
+        workingDir: /root/k8s_deployment/trainer1
+        command: ['/bin/bash']
+        args: ['test_trainer.sh']
+
+---

paddle_fl/examples/k8s_deployment/master/fl_master.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import paddle.fluid as fluid
+import os
+import paddle_fl as fl
+from paddle_fl.core.master.job_generator import JobGenerator
+from paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="master")
+    parser.add_argument(
+        '--trainer_num',
+        type=int,
+        default=2,
+        help='number of trainer(default: 2)')
+
+    return parser.parse_args()
+
+
+class Model(object):
+    def __init__(self):
+        pass
+
+    def mlp(self, inputs, label, hidden_size=128):
+        self.concat = fluid.layers.concat(inputs, axis=1)
+        self.fc1 = fluid.layers.fc(input=self.concat, size=256, act='relu')
+        self.fc2 = fluid.layers.fc(input=self.fc1, size=128, act='relu')
+        self.predict = fluid.layers.fc(input=self.fc2, size=2, act='softmax')
+        self.sum_cost = fluid.layers.cross_entropy(
+            input=self.predict, label=label)
+        self.accuracy = fluid.layers.accuracy(input=self.predict, label=label)
+        self.loss = fluid.layers.reduce_mean(self.sum_cost)
+        self.startup_program = fluid.default_startup_program()
+
+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_service_ip = os.environ['FL_SERVER_SERVICE_HOST'] + ":" + os.environ[
+    'FL_SERVER_SERVICE_PORT_FL_SERVER']
+service_endpoints = [server_service_ip]
+pod_endpoints = ["0.0.0.0:8181"]
+output = "fl_job_config"
+args = parse_args()
+num_trainer = args.trainer_num
+#job_generator.generate_fl_job(
+#    strategy, server_endpoints=endpoints, worker_num=num_trainer, output=output)
+# fl_job_config will  be dispatched to workers
+
+job_generator.generate_fl_job_for_k8s(
+    strategy,
+    server_pod_endpoints=pod_endpoints,
+    server_service_endpoints=service_endpoints,
+    worker_num=2,
+    output=output)

paddle_fl/examples/k8s_deployment/master/run_master.sh

+python fl_master.py --trainer_num 2 
+tar -zcvf fl_job_config.tar.gz fl_job_config
+python -m SimpleHTTPServer 8000 

paddle_fl/examples/k8s_deployment/scheduler/fl_scheduler.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+from paddle_fl.core.scheduler.agent_master import FLScheduler
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="scheduler")
+    parser.add_argument(
+        '--trainer_num',
+        type=int,
+        default=2,
+        help='number trainers(default: 2)')
+
+    return parser.parse_args()
+
+
+args = parse_args()
+num_trainer = args.trainer_num
+worker_num = num_trainer
+server_num = 1
+# Define the number of worker/server and the port for scheduler
+scheduler = FLScheduler(worker_num, server_num, port=9091)
+scheduler.set_sample_worker_num(worker_num)
+scheduler.init_env()
+print("init env done.")
+scheduler.start_fl_training()

paddle_fl/examples/k8s_deployment/scheduler/run_scheduler.sh

+python fl_scheduler.py --trainer_num 2  
+                        
+                        

paddle_fl/examples/k8s_deployment/server/fl_server.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle_fl as fl
+import os
+import paddle.fluid as fluid
+from paddle_fl.core.server.fl_server import FLServer
+from paddle_fl.core.master.fl_job import FLRunTimeJob
+import time
+server = FLServer()
+server_id = 0
+job_path = "fl_job_config"
+job = FLRunTimeJob()
+job.load_server_job(job_path, server_id)
+job._scheduler_ep = os.environ['FL_SCHEDULER_SERVICE_HOST'] + ":" + os.environ[
+    'FL_SCHEDULER_SERVICE_PORT_FL_SCHEDULER']  # IP address for scheduler
+#job._endpoints = os.environ['POD_IP'] + ":" + os.environ['FL_SERVER_SERVICE_PORT_FL_SERVER']  # IP address for server
+server.set_server_job(job)
+server._current_ep = os.environ['FL_SERVER_SERVICE_HOST'] + ":" + os.environ[
+    'FL_SERVER_SERVICE_PORT_FL_SERVER']  # IP address for server
+print(job._scheduler_ep, server._current_ep)
+server.start()
+print("connect")

paddle_fl/examples/k8s_deployment/server/run_server.sh

+export GLOG_v=3
+wget ${FL_MASTER_SERVICE_HOST}:${FL_MASTER_SERVICE_PORT_FL_MASTER}/fl_job_config.tar.gz
+while [ $? -ne 0 ]
+do
+    sleep 3
+    wget ${FL_MASTER_SERVICE_HOST}:${FL_MASTER_SERVICE_PORT_FL_MASTER}/fl_job_config.tar.gz
+done
+tar -xf fl_job_config.tar.gz
+python -u fl_server.py > server.log 2>&1 

paddle_fl/examples/k8s_deployment/trainer0/fl_trainer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
+from paddle_fl.core.master.fl_job import FLRunTimeJob
+import numpy as np
+import sys
+import os
+import logging
+import time
+logging.basicConfig(
+    filename="test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
+
+
+def reader():
+    for i in range(1000):
+        data_dict = {}
+        for i in range(3):
+            data_dict[str(i)] = np.random.rand(1, 5).astype('float32')
+        data_dict["label"] = np.random.randint(2, size=(1, 1)).astype('int64')
+        yield data_dict
+
+
+trainer_id = int(sys.argv[1])  # trainer id for each guest
+job_path = "fl_job_config"
+job = FLRunTimeJob()
+job.load_trainer_job(job_path, trainer_id)
+#job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
+job._scheduler_ep = os.environ['FL_SCHEDULER_SERVICE_HOST'] + ":" + os.environ[
+    'FL_SCHEDULER_SERVICE_PORT_FL_SCHEDULER']
+trainer = FLTrainerFactory().create_fl_trainer(job)
+#trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = os.environ['TRAINER0_SERVICE_HOST'] + ":" + os.environ[
+    'TRAINER0_SERVICE_PORT_TRAINER0']
+trainer.start()
+print(trainer._scheduler_ep, trainer._current_ep)
+output_folder = "fl_model"
+epoch_id = 0
+while not trainer.stop():
+    print("batch %d start train" % (epoch_id))
+    train_step = 0
+    for data in reader():
+        trainer.run(feed=data, fetch=[])
+        train_step += 1
+        if train_step == trainer._step:
+            break
+    epoch_id += 1
+    if epoch_id % 5 == 0:
+        trainer.save_inference_program(output_folder)

paddle_fl/examples/k8s_deployment/trainer0/test_trainer.sh

+wget ${FL_MASTER_SERVICE_HOST}:${FL_MASTER_SERVICE_PORT_FL_MASTER}/fl_job_config.tar.gz
+while [ $? -ne 0 ]
+do
+    sleep 3
+    wget ${FL_MASTER_SERVICE_HOST}:${FL_MASTER_SERVICE_PORT_FL_MASTER}/fl_job_config.tar.gz
+done
+tar -xf fl_job_config.tar.gz
+sleep 10
+python -u fl_trainer.py 0 

paddle_fl/examples/k8s_deployment/trainer1/fl_trainer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
+from paddle_fl.core.master.fl_job import FLRunTimeJob
+import numpy as np
+import sys
+import os
+import logging
+import time
+logging.basicConfig(
+    filename="test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
+
+
+def reader():
+    for i in range(1000):
+        data_dict = {}
+        for i in range(3):
+            data_dict[str(i)] = np.random.rand(1, 5).astype('float32')
+        data_dict["label"] = np.random.randint(2, size=(1, 1)).astype('int64')
+        yield data_dict
+
+
+trainer_id = int(sys.argv[1])  # trainer id for each guest
+job_path = "fl_job_config"
+job = FLRunTimeJob()
+job.load_trainer_job(job_path, trainer_id)
+#job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
+job._scheduler_ep = os.environ['FL_SCHEDULER_SERVICE_HOST'] + ":" + os.environ[
+    'FL_SCHEDULER_SERVICE_PORT_FL_SCHEDULER']
+trainer = FLTrainerFactory().create_fl_trainer(job)
+#trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = os.environ['TRAINER1_SERVICE_HOST'] + ":" + os.environ[
+    'TRAINER1_SERVICE_PORT_TRAINER1']
+trainer.start()
+print(trainer._scheduler_ep, trainer._current_ep)
+output_folder = "fl_model"
+epoch_id = 0
+while not trainer.stop():
+    print("batch %d start train" % (epoch_id))
+    train_step = 0
+    for data in reader():
+        trainer.run(feed=data, fetch=[])
+        train_step += 1
+        if train_step == trainer._step:
+            break
+    epoch_id += 1
+    if epoch_id % 5 == 0:
+        trainer.save_inference_program(output_folder)

paddle_fl/examples/k8s_deployment/trainer1/test_trainer.sh

+wget ${FL_MASTER_SERVICE_HOST}:${FL_MASTER_SERVICE_PORT_FL_MASTER}/fl_job_config.tar.gz
+while [ $? -ne 0 ]
+do
+    sleep 3
+    wget ${FL_MASTER_SERVICE_HOST}:${FL_MASTER_SERVICE_PORT_FL_MASTER}/fl_job_config.tar.gz
+done
+tar -xf fl_job_config.tar.gz
+sleep 10
+python -u fl_trainer.py 1 

paddle_fl/examples/secagg_demo/fl_master.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.fluid as fluid
 import paddle_fl as fl
 from paddle_fl.core.master.job_generator import JobGenerator
 from paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
 
+
 class Model(object):
     def __init__(self):
         pass
@@ -14,12 +29,17 @@ class Model(object):
         param_attrs = fluid.ParamAttr(
             name="fc_0.w_0",
             initializer=fluid.initializer.ConstantInitializer(0.0))
-        self.predict = fluid.layers.fc(input=inputs, size=10, act='softmax', param_attr=param_attrs)
-        self.sum_cost = fluid.layers.cross_entropy(input=self.predict, label=label)
+        self.predict = fluid.layers.fc(input=inputs,
+                                       size=10,
+                                       act='softmax',
+                                       param_attr=param_attrs)
+        self.sum_cost = fluid.layers.cross_entropy(
+            input=self.predict, label=label)
         self.loss = fluid.layers.mean(self.sum_cost)
         self.accuracy = fluid.layers.accuracy(input=self.predict, label=label)
         self.startup_program = fluid.default_startup_program()
 
+
 inputs = fluid.layers.data(name='x', shape=[1, 28, 28], dtype='float32')
 label = fluid.layers.data(name='y', shape=[1], dtype='int64')
 
@@ -31,15 +51,16 @@ 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])
+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.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
 

paddle_fl/examples/secagg_demo/fl_scheduler.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 2
 server_num = 1
 
-scheduler = FLScheduler(worker_num,server_num,port=9091)
+scheduler = FLScheduler(worker_num, server_num, port=9091)
 scheduler.set_sample_worker_num(worker_num)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/secagg_demo/fl_server.py

@@ -21,8 +21,8 @@ server_id = 0
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_server_job(job_path, server_id)
-job._scheduler_ep = "127.0.0.1:9091" # IP address for scheduler
+job._scheduler_ep = "127.0.0.1:9091"  # IP address for scheduler
 server.set_server_job(job)
-server._current_ep = "127.0.0.1:8181" # IP address for server
+server._current_ep = "127.0.0.1:8181"  # IP address for server
 server.start()
 print("connect")

paddle_fl/examples/secagg_demo/fl_trainer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
 from paddle_fl.core.master.fl_job import FLRunTimeJob
 import numpy
@@ -11,27 +25,32 @@ import math
 import hashlib
 import hmac
 
-logging.basicConfig(filename="log/test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
+logging.basicConfig(
+    filename="log/test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
 logger = logging.getLogger("FLTrainer")
 
 BATCH_SIZE = 64
 
 train_reader = paddle.batch(
-    paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500),
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=500),
     batch_size=BATCH_SIZE)
-test_reader = paddle.batch(
-            paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
+test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 
 trainer_num = 2
-trainer_id = int(sys.argv[1]) # trainer id for each guest
+trainer_id = int(sys.argv[1])  # trainer id for each guest
 
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_trainer_job(job_path, trainer_id)
-job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
+job._scheduler_ep = "127.0.0.1:9091"  # Inform the scheduler IP to trainer
 trainer = FLTrainerFactory().create_fl_trainer(job)
 trainer.trainer_id = trainer_id
-trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
 trainer.trainer_num = trainer_num
 trainer.key_dir = "./keys/"
 trainer.start()
@@ -47,8 +66,8 @@ feeder = fluid.DataFeeder(feed_list=[inputs, label], place=fluid.CPUPlace())
 # for test
 test_program = trainer._main_program.clone(for_test=True)
 
-def train_test(train_test_program,
-                   train_test_feed, train_test_reader):
+
+def train_test(train_test_program, train_test_feed, train_test_reader):
     acc_set = []
     avg_loss_set = []
     for test_data in train_test_reader():
@@ -61,6 +80,8 @@ def train_test(train_test_program,
     acc_val_mean = numpy.array(acc_set).mean()
     avg_loss_val_mean = numpy.array(avg_loss_set).mean()
     return avg_loss_val_mean, acc_val_mean
+
+
 # for test
 
 while not trainer.stop():
@@ -71,15 +92,18 @@ while not trainer.stop():
         step_i += 1
         trainer.step_id = step_i
         accuracy, = trainer.run(feed=feeder.feed(data),
-            fetch=["accuracy_0.tmp_0"])
+                                fetch=["accuracy_0.tmp_0"])
         if step_i % 100 == 0:
-            print("Epoch: {0}, step: {1}, accuracy: {2}".format(epoch_id, step_i, accuracy[0]))
+            print("Epoch: {0}, step: {1}, accuracy: {2}".format(
+                epoch_id, step_i, accuracy[0]))
 
     print(step_i)
-    avg_loss_val, acc_val = train_test(train_test_program=test_program,
-                                       train_test_reader=test_reader,
-                                       train_test_feed=feeder)
-    print("Test with Epoch %d, avg_cost: %s, acc: %s" %(epoch_id, avg_loss_val, acc_val))
+    avg_loss_val, acc_val = train_test(
+        train_test_program=test_program,
+        train_test_reader=test_reader,
+        train_test_feed=feeder)
+    print("Test with Epoch %d, avg_cost: %s, acc: %s" %
+          (epoch_id, avg_loss_val, acc_val))
 
     if epoch_id > 40:
         break

paddle_fl/examples/secagg_demo/keys/0_pub_key.txt

-2438748580808349511143047663636683775879288034436941526695550498623461587527621346172907651006831789701999970929529915459467532662545948308044143788306668377086821294492459623439935894424167712515718436351900091777957477710004777078638317806960364609629258387413979203403741893205419691425902518810085451041187685334971769087054033027561974230347468587825700834108657561999305482311897914109364221430821533207693682979777541616125499682380618775029176238891407643926372043660610226672413497764635239787000143341827693941253721638947580506197728500367325524850325027980531066702962726949006217630290236644410746181942256812170056772600756232506116738493114591218127323741133163913140583529684827066023347088796194846253682954154504336640429027403657831470993825621749318372332546269811820953216261135662418531598954663771775691648448615131802158937156803324423733802071166119966224716088242291968098450309032800335049617861465
+2438748580808349511143047663636683775879288034436941526695550498623461587527621346172907651006831789701999970929529915459467532662545948308044143788306668377086821294492459623439935894424167712515718436351900091777957477710004777078638317806960364609629258387413979203403741893205419691425902518810085451041187685334971769087054033027561974230347468587825700834108657561999305482311897914109364221430821533207693682979777541616125499682380618775029176238891407643926372043660610226672413497764635239787000143341827693941253721638947580506197728500367325524850325027980531066702962726949006217630290236644410746181942256812170056772600756232506116738493114591218127323741133163913140583529684827066023347088796194846253682954154504336640429027403657831470993825621749318372332546269811820953216261135662418531598954663771775691648448615131802158937156803324423733802071166119966224716088242291968098450309032800335049617861465
\ No newline at end of file

paddle_fl/examples/secagg_demo/keys/1_pub_key.txt

-2514645349791449916465355128335954929464444612258498884322250411584328344530925790221013632799576102047787468232441470392901627580493383471719532612816534848391408601920539266665550346246343040368608757429591392784807798812848893304745441721044204602414415725300562075953290154457726382683020925406187524758708694161689285261293920782115270550960717687322240202298426363733065475031448888618026711435993053991653780694485897784243346859087028197560255857091562150381619708471192080403868115055265681423866891707972356449212236920210992128063075734725292359699578940877165584175851667803049667020005978253573912096358469050409541237248593428640028573437783747958382446666712845099931003578559688134007238753677011257181086592677636834099341020870502521085827878680362572013623469761170961943916356175726242515624843837354222489536469472365937707615959657846428001149463801728084949088483783942894784080451399167982957006474558
+2514645349791449916465355128335954929464444612258498884322250411584328344530925790221013632799576102047787468232441470392901627580493383471719532612816534848391408601920539266665550346246343040368608757429591392784807798812848893304745441721044204602414415725300562075953290154457726382683020925406187524758708694161689285261293920782115270550960717687322240202298426363733065475031448888618026711435993053991653780694485897784243346859087028197560255857091562150381619708471192080403868115055265681423866891707972356449212236920210992128063075734725292359699578940877165584175851667803049667020005978253573912096358469050409541237248593428640028573437783747958382446666712845099931003578559688134007238753677011257181086592677636834099341020870502521085827878680362572013623469761170961943916356175726242515624843837354222489536469472365937707615959657846428001149463801728084949088483783942894784080451399167982957006474558
\ No newline at end of file

paddle_fl/examples/submitter_demo/conf.txt

@@ -21,4 +21,3 @@ server=yq01-hpc-lvliang01-smart-master.dmop.baidu.com
 
 python_tar=./python.tar.gz
 wheel=./paddlepaddle-0.0.0-cp27-cp27mu-linux_x86_64.whl
-

paddle_fl/examples/submitter_demo/model.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.fluid as fluid
 
+
 class Model(object):
     def __init__(self):
         pass
@@ -9,8 +24,8 @@ class Model(object):
         self.fc1 = fluid.layers.fc(input=self.concat, size=256, act='relu')
         self.fc2 = fluid.layers.fc(input=self.fc1, size=128, act='relu')
         self.predict = fluid.layers.fc(input=self.fc2, size=2, act='softmax')
-        self.sum_cost = fluid.layers.cross_entropy(input=self.predict, label=label)
+        self.sum_cost = fluid.layers.cross_entropy(
+            input=self.predict, label=label)
         self.accuracy = fluid.layers.accuracy(input=self.predict, label=label)
         self.loss = fluid.layers.reduce_mean(self.sum_cost)
         self.startup_program = fluid.default_startup_program()
-

paddle_fl/examples/submitter_demo/scheduler_client.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 import socket
 import random
@@ -49,6 +63,7 @@ default_dict = {
     "wheel": "./paddlepaddle-0.0.0-cp27-cp27mu-linux_x86_64-0.whl"
 }
 
+
 def load_conf(conf_file, local_dict):
     with open(conf_file) as fin:
         for line in fin:
@@ -58,6 +73,7 @@ def load_conf(conf_file, local_dict):
             local_dict[group[0]] = group[1]
     return local_dict
 
+
 client = HPCClient()
 default_dict = load_conf(sys.argv[1], default_dict)
 
@@ -94,9 +110,11 @@ all_ips_ready = False
 
 ip_list = []
 
-scheduler = FLScheduler(int(default_dict["worker_nodes"]),
-                        int(default_dict["server_nodes"]),
-                        port=random_port, socket=zmq_socket)
+scheduler = FLScheduler(
+    int(default_dict["worker_nodes"]),
+    int(default_dict["server_nodes"]),
+    port=random_port,
+    socket=zmq_socket)
 
 scheduler.set_sample_worker_num(int(default_dict["worker_nodes"]))
 
@@ -121,12 +139,14 @@ print(ip_list)
 #allocate the role of each endpoint and their ids
 ip_role = {}
 for i in range(len(ip_list)):
-        if i < int(default_dict["server_nodes"]):
-                ip_role[ip_list[i]] = 'server%d' % i
-        else:
-                ip_role[ip_list[i]] = 'trainer%d' % (i-int(default_dict["server_nodes"]))
+    if i < int(default_dict["server_nodes"]):
+        ip_role[ip_list[i]] = 'server%d' % i
+    else:
+        ip_role[ip_list[i]] = 'trainer%d' % (
+            i - int(default_dict["server_nodes"]))
 print(ip_role)
 
+
 def job_generate():
     #generate a fl job which is the same as fl_master
     inputs = [fluid.layers.data( \
@@ -146,8 +166,8 @@ def job_generate():
     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])
+    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
@@ -157,20 +177,24 @@ def job_generate():
     # 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)
-    
+        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 = 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()
 
+
 job_generate()
 
 #send the allocated rolls to the remote endpoints

paddle_fl/examples/submitter_demo/train_program.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import socket
 import random
 import zmq
@@ -13,7 +27,6 @@ import sys
 import logging
 import time
 
-
 random_port = 60001
 scheduler_conf = {}
 
@@ -31,8 +44,7 @@ download_url = "{}:8080".format(scheduler_ip[0])
 print(download_url)
 context = zmq.Context()
 zmq_socket = context.socket(zmq.REQ)
-zmq_socket.connect(
-    "tcp://{}".format(scheduler_conf["ENDPOINT"]))
+zmq_socket.connect("tcp://{}".format(scheduler_conf["ENDPOINT"]))
 zmq_socket.send("ENDPOINT\t{}".format(endpoint))
 message = zmq_socket.recv()
 print(message)
@@ -47,7 +59,7 @@ while True:
     if group[0] == "WAIT":
         continue
     else:
-        os.system("wget {}/job_config/{}.tar.gz".format(download_url,message))
+        os.system("wget {}/job_config/{}.tar.gz".format(download_url, message))
         print(message)
         break
 
@@ -71,6 +83,7 @@ if 'server' in message:
     server._current_ep = endpoint
     server.start()
 else:
+
     def reader():
         for i in range(1000):
             data_dict = {}
@@ -96,7 +109,7 @@ else:
         for data in reader():
             trainer.run(feed=data, fetch=[])
             step_i += 1
-            if step_i  == trainer._step:
+            if step_i == trainer._step:
                 break
         epoch_id += 1
         if epoch_id % 5 == 0:

paddle_fl/reader/gru4rec_reader.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.fluid as fluid
 import numpy as np
 import os
 
+
 class Gru4rec_Reader:
     def __init__(self):
         pass
@@ -21,7 +36,6 @@ class Gru4rec_Reader:
         res.set_lod([lod])
         return res
 
-
     def lod_reader(self, reader, place):
         def feed_reader():
             for data in reader():
@@ -33,12 +47,14 @@ class Gru4rec_Reader:
                 fe_data["src_wordseq"] = lod_src_wordseq
                 fe_data["dst_wordseq"] = lod_dst_wordseq
                 yield fe_data
+
         return feed_reader
 
     def sort_batch(self, reader, batch_size, sort_group_size, drop_last=False):
         """
         Create a batched reader.
         """
+
         def batch_reader():
             r = reader()
             b = []
@@ -66,11 +82,11 @@ class Gru4rec_Reader:
         # Batch size check
         batch_size = int(batch_size)
         if batch_size <= 0:
-            raise ValueError("batch_size should be a positive integeral value, "
-                         "but got batch_size={}".format(batch_size))
+            raise ValueError(
+                "batch_size should be a positive integeral value, "
+                "but got batch_size={}".format(batch_size))
         return batch_reader
 
-
     def reader_creator(self, file_dir):
         def reader():
             files = os.listdir(file_dir)
@@ -82,10 +98,12 @@ class Gru4rec_Reader:
                         src_seq = l[:len(l) - 1]
                         trg_seq = l[1:]
                         yield src_seq, trg_seq
+
         return reader
 
     def reader(self, file_dir, place, batch_size=5):
         """ prepare the English Pann Treebank (PTB) data """
         print("start constuct word dict")
-        reader = self.sort_batch(self.reader_creator(file_dir), batch_size, batch_size * 20)
+        reader = self.sort_batch(
+            self.reader_creator(file_dir), batch_size, batch_size * 20)
         return self.lod_reader(reader, place)

paddle_fl/version.py

@@ -12,6 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ PaddleFL version string """
-fl_version = "0.1.10"
-module_proto_version = "0.1.10"
-
+fl_version = "0.1.11"
+module_proto_version = "0.1.11"

setup.py

@@ -26,10 +26,12 @@ from paddle_fl.version import fl_version
 def python_version():
     return [int(v) for v in platform.python_version().split(".")]
 
+
 max_version, mid_version, min_version = python_version()
 
 REQUIRED_PACKAGES = [
-    'six >= 1.10.0', 'protobuf >= 3.1.0','paddlepaddle >= 1.6', 'zmq', 'paddlepaddle-gpu >= 1.6' 
+    'six >= 1.10.0', 'protobuf >= 3.1.0', 'paddlepaddle >= 1.6', 'zmq',
+    'paddlepaddle-gpu >= 1.6'
 ]
 
 if max_version < 3:
@@ -42,8 +44,7 @@ REQUIRED_PACKAGES += ["unittest2"]
 setup(
     name='paddle_fl',
     version=fl_version.replace('-', ''),
-    description=
-    ('Federated Deep Learning Package Based on PaddlePaddle.'),
+    description=('Federated Deep Learning Package Based on PaddlePaddle.'),
     long_description='',
     url='https://github.com/PaddlePaddle/PaddleFL',
     author='PaddlePaddle Author',
@@ -70,4 +71,5 @@ setup(
         'Topic :: Software Development :: Libraries :: Python Modules',
     ],
     license='Apache 2.0',
-    keywords=('paddle_fl paddlepaddle multi-task transfer distributed-training'))
+    keywords=(
+        'paddle_fl paddlepaddle multi-task transfer distributed-training'))

tools/codestyle/clang_format.hook

+#!/bin/bash
+set -e
+
+readonly VERSION="3.8"
+
+version=$(clang-format -version)
+
+if ! [[ $version == *"$VERSION"* ]]; then
+    echo "clang-format version check failed."
+    echo "a version contains '$VERSION' is needed, but get '$version'"
+    echo "you can install the right version, and make an soft-link to '\$PATH' env"
+    exit -1
+fi
+
+clang-format $@

tools/codestyle/copyright.hook

+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import io, re
+import sys, os
+import subprocess
+import platform
+
+COPYRIGHT = '''
+Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+'''
+
+LANG_COMMENT_MARK = None
+
+NEW_LINE_MARK = None
+
+COPYRIGHT_HEADER = None
+
+if platform.system() == "Windows":
+    NEW_LINE_MARK = "\r\n"
+else:
+    NEW_LINE_MARK = '\n'
+    COPYRIGHT_HEADER = COPYRIGHT.split(NEW_LINE_MARK)[1]
+    p = re.search('(\d{4})', COPYRIGHT_HEADER).group(0)
+    process = subprocess.Popen(["date", "+%Y"], stdout=subprocess.PIPE)
+    date, err = process.communicate()
+    date = date.decode("utf-8").rstrip("\n")
+    COPYRIGHT_HEADER = COPYRIGHT_HEADER.replace(p, date)
+
+
+def generate_copyright(template, lang='C'):
+    if lang == 'Python':
+        LANG_COMMENT_MARK = '#'
+    else:
+        LANG_COMMENT_MARK = "//"
+
+    lines = template.split(NEW_LINE_MARK)
+    BLANK = " "
+    ans = LANG_COMMENT_MARK + BLANK + COPYRIGHT_HEADER + NEW_LINE_MARK
+    for lino, line in enumerate(lines):
+        if lino == 0 or lino == 1 or lino == len(lines) - 1: continue
+        if len(line)  == 0:
+            BLANK = ""
+        else:
+            BLANK = " "
+        ans += LANG_COMMENT_MARK + BLANK + line + NEW_LINE_MARK
+
+    return ans + "\n"
+
+
+def lang_type(filename):
+    if filename.endswith(".py"):
+        return "Python"
+    elif filename.endswith(".h"):
+        return "C"
+    elif filename.endswith(".c"):
+        return "C"
+    elif filename.endswith(".hpp"):
+        return "C"
+    elif filename.endswith(".cc"):
+        return "C"
+    elif filename.endswith(".cpp"):
+        return "C"
+    elif filename.endswith(".cu"):
+        return "C"
+    elif filename.endswith(".cuh"):
+        return "C"
+    elif filename.endswith(".go"):
+        return "C"
+    elif filename.endswith(".proto"):
+        return "C"
+    else:
+        print("Unsupported filetype %s", filename)
+        exit(0)
+
+
+PYTHON_ENCODE = re.compile("^[ \t\v]*#.*?coding[:=][ \t]*([-_.a-zA-Z0-9]+)")
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(
+        description='Checker for copyright declaration.')
+    parser.add_argument('filenames', nargs='*', help='Filenames to check')
+    args = parser.parse_args(argv)
+
+    retv = 0
+    for filename in args.filenames:
+        fd = io.open(filename, encoding="utf-8")
+        first_line = fd.readline()
+        second_line = fd.readline()
+        if "COPYRIGHT (C)" in first_line.upper(): continue
+        if first_line.startswith("#!") or PYTHON_ENCODE.match(
+                second_line) != None or PYTHON_ENCODE.match(first_line) != None:
+            continue
+        original_contents = io.open(filename, encoding="utf-8").read()
+        new_contents = generate_copyright(
+            COPYRIGHT, lang_type(filename)) + original_contents
+        print('Auto Insert Copyright Header {}'.format(filename))
+        retv = 1
+        with io.open(filename, 'w') as output_file:
+            output_file.write(new_contents)
+
+    return retv
+
+
+if __name__ == '__main__':
+    exit(main())

tools/codestyle/cpplint_pre_commit.hook

+#!/bin/bash
+
+TOTAL_ERRORS=0
+if [[ ! $TRAVIS_BRANCH ]]; then
+  # install cpplint on local machine.
+  if [[ ! $(which cpplint) ]]; then
+    pip install cpplint
+  fi
+  # diff files on local machine. 
+  files=$(git diff --cached --name-status | awk '$1 != "D" {print $2}')
+else
+  # diff files between PR and latest commit on Travis CI. 
+  branch_ref=$(git rev-parse "$TRAVIS_BRANCH")
+  head_ref=$(git rev-parse HEAD)
+  files=$(git diff --name-status $branch_ref $head_ref | awk '$1 != "D" {print $2}')
+fi
+# The trick to remove deleted files: https://stackoverflow.com/a/2413151
+for file in $files; do
+    if [[ $file =~ ^(patches/grpc/.*) ]]; then
+        continue;
+    else
+        cpplint --filter=-readability/fn_size $file;
+        TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?);
+    fi
+done
+
+exit $TOTAL_ERRORS

tools/codestyle/docstring_checker.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""DocstringChecker is used to check python doc string's style."""
+
+import six
+import astroid
+
+from pylint.checkers import BaseChecker, utils
+from pylint.interfaces import IAstroidChecker
+
+from collections import defaultdict
+import re
+
+
+def register(linter):
+    """Register checkers."""
+    linter.register_checker(DocstringChecker(linter))
+
+
+class Docstring(object):
+    """Docstring class holds the parsed doc string elements.
+    """
+
+    def __init__(self):
+        self.d = defaultdict(list)  #name->[]
+        self.clear()
+
+    def clear(self):
+        self.d['Args'] = []
+        self.d['Examples'] = []
+        self.d['Returns'] = []
+        self.d['Raises'] = []
+        self.args = {}  #arg_name->arg_type
+
+    def get_level(self, string, indent='    '):
+        level = 0
+        unit_size = len(indent)
+        while string[:unit_size] == indent:
+            string = string[unit_size:]
+            level += 1
+
+        return level
+
+    def parse(self, doc):
+        """parse gets sections from doc
+        Such as Args, Returns, Raises, Examples s
+        Args:
+            doc (string): is the astroid node doc string.
+        Returns:
+            True if doc is parsed successfully.
+        """
+        self.clear()
+
+        lines = doc.splitlines()
+        state = ("others", -1)
+        for l in lines:
+            c = l.strip()
+            if len(c) <= 0:
+                continue
+
+            level = self.get_level(l)
+            if c.startswith("Args:"):
+                state = ("Args", level)
+            elif c.startswith("Returns:"):
+                state = ("Returns", level)
+            elif c.startswith("Raises:"):
+                state = ("Raises", level)
+            elif c.startswith("Examples:"):
+                state = ("Examples", level)
+            else:
+                if level > state[1]:
+                    self.d[state[0]].append(c)
+                    continue
+
+                state = ("others", -1)
+                self.d[state[0]].append(c)
+
+        self._arg_with_type()
+        return True
+
+    def get_returns(self):
+        return self.d['Returns']
+
+    def get_raises(self):
+        return self.d['Raises']
+
+    def get_examples(self):
+        return self.d['Examples']
+
+    def _arg_with_type(self):
+
+        for t in self.d['Args']:
+            m = re.search('([A-Za-z0-9_-]+)\s{0,4}(\(.+\))\s{0,4}:', t)
+            if m:
+                self.args[m.group(1)] = m.group(2)
+
+        return self.args
+
+
+class DocstringChecker(BaseChecker):
+    """DosstringChecker is pylint checker to
+    check docstring style.
+    """
+    __implements__ = (IAstroidChecker, )
+
+    POSITIONAL_MESSAGE_ID = 'str-used-on-positional-format-argument'
+    KEYWORD_MESSAGE_ID = 'str-used-on-keyword-format-argument'
+
+    name = 'doc-string-checker'
+    symbol = "doc-string"
+    priority = -1
+    msgs = {
+        'W9001': ('One line doc string on > 1 lines', symbol + "-one-line",
+                  'Used when a short doc string is on multiple lines'),
+        'W9002':
+        ('Doc string does not end with "." period', symbol + "-end-with",
+         'Used when a doc string does not end with a period'),
+        'W9003':
+        ('All args with their types must be mentioned in doc string %s',
+         symbol + "-with-all-args",
+         'Used when not all arguments are in the doc string '),
+        'W9005': ('Missing docstring or docstring is too short',
+                  symbol + "-missing", 'Add docstring longer >=10'),
+        'W9006': ('Docstring indent error, use 4 space for indent',
+                  symbol + "-indent-error", 'Use 4 space for indent'),
+        'W9007': ('You should add `Returns` in comments',
+                  symbol + "-with-returns",
+                  'There should be a `Returns` section in comments'),
+        'W9008': ('You should add `Raises` section in comments',
+                  symbol + "-with-raises",
+                  'There should be a `Raises` section in comments'),
+    }
+    options = ()
+
+    def visit_functiondef(self, node):
+        """visit_functiondef checks Function node docstring style.
+        Args:
+            node (astroid.node): The visiting node.
+        Returns:
+            True if successful other wise False.
+        """
+
+        self.check_doc_string(node)
+
+        if node.tolineno - node.fromlineno <= 10:
+            return True
+
+        if not node.doc:
+            return True
+
+        doc = Docstring()
+        doc.parse(node.doc)
+
+        self.all_args_in_doc(node, doc)
+        self.with_returns(node, doc)
+        self.with_raises(node, doc)
+
+    def visit_module(self, node):
+        self.check_doc_string(node)
+
+    def visit_classdef(self, node):
+        self.check_doc_string(node)
+
+    def check_doc_string(self, node):
+        self.missing_doc_string(node)
+        self.one_line(node)
+        self.has_period(node)
+        self.indent_style(node)
+
+    def missing_doc_string(self, node):
+        if node.name.startswith("__") or node.name.startswith("_"):
+            return True
+        if node.tolineno - node.fromlineno <= 10:
+            return True
+
+        if node.doc is None or len(node.doc) < 10:
+            self.add_message('W9005', node=node, line=node.fromlineno)
+        return False
+
+    # FIXME(gongwb): give the docstring line-no
+    def indent_style(self, node, indent=4):
+        """indent_style checks docstring's indent style
+        Args:
+            node (astroid.node): The visiting node.
+            indent (int): The default indent of style
+        Returns:
+            True if successful other wise False.
+        """
+        if node.doc is None:
+            return True
+
+        doc = node.doc
+        lines = doc.splitlines()
+        line_num = 0
+
+        for l in lines:
+            if line_num == 0:
+                continue
+            cur_indent = len(l) - len(l.lstrip())
+            if cur_indent % indent != 0:
+                self.add_message('W9006', node=node, line=node.fromlineno)
+                return False
+            line_num += 1
+
+        return True
+
+    def one_line(self, node):
+        """one_line checks if docstring (len < 40) is on one line.
+        Args:
+            node (astroid.node): The node visiting.
+        Returns:
+            True if successful otherwise False.
+        """
+
+        doc = node.doc
+        if doc is None:
+            return True
+
+        if len(doc) > 40:
+            return True
+        elif sum(doc.find(nl) for nl in ('\n', '\r', '\n\r')) == -3:
+            return True
+        else:
+            self.add_message('W9001', node=node, line=node.fromlineno)
+            return False
+
+        return True
+
+    def has_period(self, node):
+        """has_period checks if one line doc end-with '.' .
+        Args:
+            node (astroid.node): the node is visiting.
+        Returns:
+            True if successful otherwise False.
+        """
+        if node.doc is None:
+            return True
+
+        if len(node.doc.splitlines()) > 1:
+            return True
+
+        if not node.doc.strip().endswith('.'):
+            self.add_message('W9002', node=node, line=node.fromlineno)
+            return False
+
+        return True
+
+    def with_raises(self, node, doc):
+        """with_raises checks if one line doc end-with '.' .
+        Args:
+            node (astroid.node): the node is visiting.
+            doc (Docstring): Docstring object.
+        Returns:
+            True if successful otherwise False.
+        """
+
+        find = False
+        for t in node.body:
+            if not isinstance(t, astroid.Raise):
+                continue
+
+            find = True
+            break
+
+        if not find:
+            return True
+
+        if len(doc.get_raises()) == 0:
+            self.add_message('W9008', node=node, line=node.fromlineno)
+            return False
+
+        return True
+
+    def with_returns(self, node, doc):
+        """with_returns checks if docstring comments what are returned .
+        Args:
+            node (astroid.node): the node is visiting.
+            doc (Docstring): Docstring object.
+        Returns:
+            True if successful otherwise False.
+        """
+
+        if node.name.startswith("__") or node.name.startswith("_"):
+            return True
+        find = False
+        for t in node.body:
+            if not isinstance(t, astroid.Return):
+                continue
+
+            find = True
+            break
+
+        if not find:
+            return True
+
+        if len(doc.get_returns()) == 0:
+            self.add_message('W9007', node=node, line=node.fromlineno)
+            return False
+
+        return True
+
+    def all_args_in_doc(self, node, doc):
+        """all_args_in_doc checks if arguments are mentioned in doc
+        Args:
+            node (astroid.node): the node is visiting.
+            doc (Docstring): Docstring object
+        Returns:
+            True if successful otherwise False.
+        """
+        if node.name.startswith("__") or node.name.startswith("_"):
+            return True
+        args = []
+        for arg in node.args.get_children():
+            if (not isinstance(arg, astroid.AssignName)) \
+                or arg.name == "self":
+                continue
+            args.append(arg.name)
+
+        if len(args) <= 0:
+            return True
+
+        parsed_args = doc.args
+        args_not_documented = set(args) - set(parsed_args)
+        if len(args) > 0 and len(parsed_args) <= 0:
+            self.add_message(
+                'W9003',
+                node=node,
+                line=node.fromlineno,
+                args=list(args_not_documented))
+            return False
+
+        for t in args:
+            if t not in parsed_args:
+                self.add_message(
+                    'W9003', node=node, line=node.fromlineno, args=[t, ])
+                return False
+
+        return True

tools/codestyle/pylint_pre_commit.hook

+#!/bin/bash
+
+TOTAL_ERRORS=0
+
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+export PYTHONPATH=$DIR:$PYTHONPATH
+
+# The trick to remove deleted files: https://stackoverflow.com/a/2413151
+for file in $(git diff --name-status | awk '$1 != "D" {print $2}'); do
+    pylint --disable=all --load-plugins=docstring_checker \
+    --enable=doc-string-one-line,doc-string-end-with,doc-string-with-all-args,doc-string-triple-quotes,doc-string-missing,doc-string-indent-error,doc-string-with-returns,doc-string-with-raises $file;
+    TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?);
+done
+
+exit $TOTAL_ERRORS
+#For now, just warning:
+#exit 0
+

tools/codestyle/test_docstring_checker.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import docstring_checker
+import pylint.testutils
+import astroid
+import pytest
+import sys
+
+
+class TestDocstring(pylint.testutils.CheckerTestCase):
+    CHECKER_CLASS = docstring_checker.DocstringChecker
+
+    def test_one_line(self):
+        func_node = astroid.extract_node('''
+        def test(): 
+            """get 
+            news.
+            """
+            if True:
+                return 5
+            return 5
+        ''')
+
+        self.checker.visit_functiondef(func_node)
+        got = self.linter.release_messages()
+        assert len(got) == 1
+        assert 'W9001' == got[0][0]
+
+    def test_one_line(self):
+        func_node = astroid.extract_node('''
+        def test(): 
+            """get news"""
+            if True:
+                return 5
+            return 5
+        ''')
+
+        self.checker.visit_functiondef(func_node)
+        got = self.linter.release_messages()
+        assert len(got) == 1
+        assert 'W9002' == got[0][0]
+
+    def test_args(self):
+        func_node = astroid.extract_node('''
+        def test(scale, mean): 
+            """get news.
+            Args:
+                scale (int): scale is the number.
+            """
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+        ''')
+
+        self.checker.visit_functiondef(func_node)
+        got = self.linter.release_messages()
+        assert len(got) == 1
+        assert 'W9003' == got[0][0]
+
+    def test_missing(self):
+        func_node = astroid.extract_node('''
+        def test(): 
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+        ''')
+
+        self.checker.visit_functiondef(func_node)
+        got = self.linter.release_messages()
+        assert len(got) == 1
+        assert 'W9005' == got[0][0]
+
+    def test_indent(self):
+        func_node = astroid.extract_node('''
+        def test(): 
+            """ get get get get get get get get
+              get get get get get get get get.
+            """
+            pass 
+        ''')
+
+        self.checker.visit_functiondef(func_node)
+        got = self.linter.release_messages()
+        assert len(got) == 1
+        assert 'W9006' == got[0][0]
+
+    def test_with_resturns(self):
+        func_node = astroid.extract_node('''
+        def test(): 
+            """get news.
+            Args:
+                scale (int): scale is the number.
+            """
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            return mean
+        ''')
+
+        self.checker.visit_functiondef(func_node)
+        got = self.linter.release_messages()
+        assert len(got) == 1
+        assert 'W9007' == got[0][0]
+
+    def test_with_raises(self):
+        func_node = astroid.extract_node('''
+        def test(): 
+            """get news.
+            Args:
+                scale (int): scale is the number.
+            """
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            mean=scale
+            raise ValueError('A very specific bad thing happened.')
+        ''')
+
+        self.checker.visit_functiondef(func_node)
+        got = self.linter.release_messages()
+        assert len(got) == 1
+        assert 'W9008' == got[0][0]
+
+    def test_no_message(self):
+        p = '''
+def fc(input,
+       size,
+       num_flatten_dims=1,
+       param_attr=None,
+       bias_attr=None,
+       act=None,
+       name=None):
+    """
+    **Fully Connected Layer**
+    The fully connected layer can take multiple tensors as its inputs. It
+    creates a variable called weights for each input tensor, which represents
+    a fully connected weight matrix from each input unit to each output unit.
+    The fully connected layer multiplies each input tensor with its coresponding
+    weight to produce an output Tensor. If multiple input tensors are given,
+    the results of multiple multiplications will be sumed up. If bias_attr is
+    not None, a bias variable will be created and added to the output. Finally,
+    if activation is not None, it will be applied to the output as well.
+    This process can be formulated as follows:
+
+    Args:
+        input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
+            the input tensor(s) is at least 2.
+        size(int): The number of output units in this layer.
+        num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
+            two dimensions. If this happens, the multidimensional tensor will first be flattened
+            into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
+            tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
+            dimensions will be flatten to form the first dimension of the final matrix (height of
+            the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
+            form the second dimension of the final matrix (width of the matrix). For example, suppose
+            `X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
+            Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
+        param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
+            parameters/weights of this layer.
+        bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
+            of this layer. If it is set to None, no bias will be added to the output units.
+        act (str, default None): Activation to be applied to the output of this layer.
+        name (str, default None): The name of this layer.
+    Returns:
+        A tensor variable storing the transformation result.
+    Raises:
+        ValueError: If rank of the input tensor is less than 2.
+    Examples:
+        .. code-block:: python
+            data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+            fc = fluid.layers.fc(input=data, size=1000, act="tanh")
+    """
+    raise ValueError('A very specific bad thing happened.')
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    size = 1
+    return size
+    '''
+
+        func_node = astroid.extract_node(p)
+        self.checker.visit_functiondef(func_node)
+        got = self.linter.release_messages()
+        assert len(got) == 0