fix styles

benchmark/cluster/vgg16/fluid/README.md

@@ -12,4 +12,5 @@ Check the logs for the distributed training progress and analyze the performance
 
 ## Enable verbos logs
 
-Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
+Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
\ No newline at end of file
+

benchmark/cluster/vgg16/fluid/k8s_tools.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+# 
+# 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.
+
 #!/bin/env python
 import os
 import sys
@@ -33,6 +47,7 @@ def wait_pods_running(label_selector, desired):
         print 'current cnt: %d sleep for 5 seconds...' % count
         time.sleep(5)
 
+
 def count_pods_by_phase(label_selector, phase):
     pod_list = fetch_pods_info(label_selector)
     filtered_pod_list = filter(lambda x: x[0] == phase, pod_list)
@@ -45,12 +60,14 @@ def fetch_pserver_ips():
     pserver_ips = [item[1] for item in pod_list]
     return ",".join(pserver_ips)
 
+
 def fetch_master_ip():
     label_selector = "paddle-job-master=%s" % PADDLE_JOB_NAME
     pod_list = fetch_pods_info(label_selector)
     master_ips = [item[1] for item in pod_list]
     return master_ips[0]
 
+
 def fetch_trainer_id():
     label_selector = "paddle-job=%s" % PADDLE_JOB_NAME
     pod_list = fetch_pods_info(label_selector)
@@ -75,4 +92,3 @@ if __name__ == "__main__":
         print count_pods_by_phase(sys.argv[2], sys.argv[3])
     elif command == "wait_pods_running":
         wait_pods_running(sys.argv[2], sys.argv[3])
-

benchmark/cluster/vgg16/fluid/reader.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+# 
+# 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.v2 as paddle
 paddle.dataset.cifar.train10()

benchmark/cluster/vgg16/fluid/vgg16.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+# 
+# 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.
+
 """VGG16 benchmark in Fluid"""
 from __future__ import print_function
 
@@ -11,6 +25,7 @@ import argparse
 import functools
 import os
 
+
 def str2bool(v):
     if v.lower() in ('yes', 'true', 't', 'y', '1'):
         return True
@@ -19,6 +34,7 @@ def str2bool(v):
     else:
         raise argparse.ArgumentTypeError('Boolean value expected.')
 
+
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     '--batch_size', type=int, default=128, help="Batch size for training.")
@@ -122,7 +138,6 @@ def main():
     place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
     exe = fluid.Executor(place)
 
-
     # test
     def test(exe):
         accuracy.reset(exe)
@@ -148,20 +163,21 @@ def main():
             accuracy.reset(exe)
             for batch_id, data in enumerate(train_reader()):
                 ts = time.time()
-                img_data = np.array(map(lambda x: x[0].reshape(data_shape),
-                                        data)).astype("float32")
+                img_data = np.array(
+                    map(lambda x: x[0].reshape(data_shape), data)).astype(
+                        "float32")
                 y_data = np.array(map(lambda x: x[1], data)).astype("int64")
                 y_data = y_data.reshape([-1, 1])
 
                 loss, acc = exe.run(trainer_prog,
                                     feed={"pixel": img_data,
-                                        "label": y_data},
+                                          "label": y_data},
                                     fetch_list=[avg_cost] + accuracy.metrics)
                 iters += 1
                 num_samples += len(data)
                 print(
-                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f" %
-                    (pass_id, iters, loss, acc, time.time() - ts)
+                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f"
+                    % (pass_id, iters, loss, acc, time.time() - ts)
                 )  # The accuracy is the accumulation of batches, but not the current batch.
 
             pass_elapsed = time.time() - start_time
@@ -170,7 +186,7 @@ def main():
             print(
                 "Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
                 % (pass_id, num_samples / pass_elapsed, pass_train_acc,
-                pass_test_acc))
+                   pass_test_acc))
 
     if args.local:
         # Parameter initialization
@@ -179,8 +195,8 @@ def main():
         # data reader
         train_reader = paddle.batch(
             paddle.reader.shuffle(
-                paddle.dataset.cifar.train10()
-                if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
+                paddle.dataset.cifar.train10() if args.data_set == 'cifar10'
+                else paddle.dataset.flowers.train(),
                 buf_size=5120),
             batch_size=args.batch_size)
         test_reader = paddle.batch(
@@ -196,19 +212,25 @@ def main():
         pserver_endpoints = ",".join(eplist)
         print("pserver endpoints: ", pserver_endpoints)
         trainers = int(os.getenv("TRAINERS"))  # total trainer count
-        current_endpoint = os.getenv("POD_IP") + ":6174"  # current pserver endpoint
-        training_role = os.getenv("TRAINING_ROLE",
-                                "TRAINER")  # get the training role: trainer/pserver
+        current_endpoint = os.getenv(
+            "POD_IP") + ":6174"  # current pserver endpoint
+        training_role = os.getenv(
+            "TRAINING_ROLE",
+            "TRAINER")  # get the training role: trainer/pserver
         t = fluid.DistributeTranspiler()
         t.transpile(
-            optimize_ops, params_grads, pservers=pserver_endpoints, trainers=trainers)
+            optimize_ops,
+            params_grads,
+            pservers=pserver_endpoints,
+            trainers=trainers)
 
         if training_role == "PSERVER":
             if not current_endpoint:
                 print("need env SERVER_ENDPOINT")
                 exit(1)
             pserver_prog = t.get_pserver_program(current_endpoint)
-            pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
+            pserver_startup = t.get_startup_program(current_endpoint,
+                                                    pserver_prog)
             print("starting server side startup")
             exe.run(pserver_startup)
             print("starting parameter server...")
@@ -220,13 +242,13 @@ def main():
             # data reader
             train_reader = paddle.batch(
                 paddle.reader.shuffle(
-                    paddle.dataset.cifar.train10()
-                    if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
+                    paddle.dataset.cifar.train10() if args.data_set == 'cifar10'
+                    else paddle.dataset.flowers.train(),
                     buf_size=5120),
                 batch_size=args.batch_size)
             test_reader = paddle.batch(
-                paddle.dataset.cifar.test10()
-                if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
+                paddle.dataset.cifar.test10() if args.data_set == 'cifar10' else
+                paddle.dataset.flowers.test(),
                 batch_size=args.batch_size)
 
             trainer_prog = t.get_trainer_program()