config with gpu

01.fit_a_line/train.py

+import os
 import paddle.v2 as paddle
 import paddle.v2.dataset.uci_housing as uci_housing
 
+with_gpu = os.getenv('WITH_GPU', '0') != '0'
 
 def main():
     # init
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=with_gpu, trainer_count=1)
 
     # network config
     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))

02.recognize_digits/train.py

@@ -3,6 +3,7 @@ from PIL import Image
 import numpy as np
 import paddle.v2 as paddle
 
+with_gpu = os.getenv('WITH_GPU', '0') != '0'
 
 def softmax_regression(img):
     predict = paddle.layer.fc(
@@ -49,7 +50,7 @@ def convolutional_neural_network(img):
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=with_gpu, trainer_count=1)
 
     # define network topology
     images = paddle.layer.data(

03.image_classification/train.py

@@ -12,20 +12,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License
 
-import sys
+import sys, os
 
 import paddle.v2 as paddle
 
 from vgg import vgg_bn_drop
 from resnet import resnet_cifar10
 
+with_gpu = os.getenv('WITH_GPU', '0') != '0'
 
 def main():
     datadim = 3 * 32 * 32
     classdim = 10
 
     # PaddlePaddle init
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=with_gpu, trainer_count=1)
 
     image = paddle.layer.data(
         name="image", type=paddle.data_type.dense_vector(datadim))

04.word2vec/train.py

-import math
+import math, os
 
 import paddle.v2 as paddle
 
+with_gpu = os.getenv('WITH_GPU', '0') != '0'
+
 embsize = 32
 hiddensize = 256
 N = 5
@@ -17,7 +19,7 @@ def wordemb(inlayer):
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=3)
+    paddle.init(use_gpu=with_gpu, trainer_count=3)
     word_dict = paddle.dataset.imikolov.build_dict()
     dict_size = len(word_dict)
     # Every layer takes integer value of range [0, dict_size)

05.recommender_system/train.py

 import paddle.v2 as paddle
 import cPickle
 import copy
+import os
 
+with_gpu = os.getenv('WITH_GPU', '0') != '0'
 
 def get_usr_combined_features():
     uid = paddle.layer.data(
@@ -67,7 +69,7 @@ def get_mov_combined_features():
 
 
 def main():
-    paddle.init(use_gpu=False)
+    paddle.init(use_gpu=with_gpu)
     usr_combined_features = get_usr_combined_features()
     mov_combined_features = get_mov_combined_features()
     inference = paddle.layer.cos_sim(

06.understand_sentiment/train.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import sys
+import sys, os
 import paddle.v2 as paddle
 
+with_gpu = os.getenv('WITH_GPU', '0') != '0'
 
 def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
     data = paddle.layer.data("word",
@@ -102,7 +103,7 @@ def stacked_lstm_net(input_dim,
 
 if __name__ == '__main__':
     # init
-    paddle.init(use_gpu=False)
+    paddle.init(use_gpu=with_gpu)
 
     #data
     print 'load dictionary...'

07.label_semantic_roles/train.py

-import math
+import math, os
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.dataset.conll05 as conll05
 import paddle.v2.evaluator as evaluator
 
+with_gpu = os.getenv('WITH_GPU', '0') != '0'
+
 word_dict, verb_dict, label_dict = conll05.get_dict()
 word_dict_len = len(word_dict)
 label_dict_len = len(label_dict)
@@ -118,7 +120,7 @@ def load_parameter(file_name, h, w):
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=with_gpu, trainer_count=1)
 
     # define network topology
     feature_out = db_lstm()

08.machine_translation/train.py

-import sys
+import sys, os
 import numpy as np
-
 import paddle.v2 as paddle
 
+with_gpu = os.getenv('WITH_GPU', '0') != '0'
 
 def save_model(parameters, save_path):
     with open(save_path, 'w') as f:
@@ -135,7 +135,7 @@ def seq_to_seq_net(source_dict_dim,
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=with_gpu, trainer_count=1)
     is_generating = False
 
     # source and target dict dim.