使用Keras与Tensorflow后端，我正在尝试训练LSTM网络，并且在GPU上运行它比在CPU上运行要花费更长的时间。
我正在使用fit_generator函数训练LSTM网络。每个纪元需要CPU〜250秒，而每个纪元需要GPU〜900秒。我的GPU环境中的软件包包括
keras-applications 1.0.8 py_0 anaconda
keras-base 2.2.4 py36_0 anaconda
keras-gpu 2.2.4 0 anaconda
keras-preprocessing 1.1.0 py_1 anaconda
...
tensorflow 1.13.1 gpu_py36h3991807_0 anaconda
tensorflow-base 1.13.1 gpu_py36h8d69cac_0 anaconda
tensorflow-estimator 1.13.0 py_0 anaconda
tensorflow-gpu 1.13.1 pypi_0 pypi
我的Cuda编译工具的版本为9.1.85，而我的CUDA和驱动程序的版本为
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.67 Driver Version: 418.67 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2080 On | 00000000:0A:00.0 Off | N/A |
| 0% 39C P8 5W / 225W | 7740MiB / 7952MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce RTX 2080 On | 00000000:42:00.0 Off | N/A |
| 0% 33C P8 19W / 225W | 142MiB / 7951MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 49251 C .../whsu014/.conda/envs/whsuphd/bin/python 7729MiB |
| 1 1354 G /usr/lib/xorg/Xorg 16MiB |
| 1 49251 C .../whsu014/.conda/envs/whsuphd/bin/python 113MiB |
+-----------------------------------------------------------------------------+
当我插入此行代码
tf.Session(config = tf.configProto(log_device_placement = True)):
我在终端机中看到了以下内容
...
ining_1/Adam/Const_10: (Const)/job:localhost/replica:0/task:0/device:GPU:0
training_1/Adam/Const_11: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2019-06-25 11:27:31.720653: I tensorflow/core/common_runtime/placer.cc:1059] training_1/Adam/Const_11: (Const)/job:localhost/replica:0/task:0/device:GPU:0
training_1/Adam/add_15/y: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2019-06-25 11:27:31.720666: I tensorflow/core/common_runtime/placer.cc:1059] training_1/Adam/add_15/y: (Const)/job:localhost/replica:0/task:0/device:GPU:0
...
因此，看来Tensorflow正在使用GPU。
当我分析代码时， 在GPU上，这是前10行
10852017 function calls (10524203 primitive calls) in 184.768 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
16200 173.827 0.011 173.827 0.011 {built-in method _pywrap_tensorflow_internal.TF_SessionRunCallable}
6 0.926 0.154 0.926 0.154 {built-in method _pywrap_tensorflow_internal.TF_SessionMakeCallable}
62 0.813 0.013 0.813 0.013 {built-in method _pywrap_tensorflow_internal.TF_SessionRun_wrapper}
156954 0.414 0.000 0.415 0.000 {built-in method numpy.array}
16200 0.379 0.000 1.042 0.000 training.py:643(_standardize_user_data)
24300 0.338 0.000 0.338 0.000 {method 'partition' of 'numpy.ndarray' objects}
68 0.301 0.004 0.301 0.004 {built-in method _pywrap_tensorflow_internal.ExtendSession}
32458 0.223 0.000 2.122 0.000 tensorflow_backend.py:156(get_session)
3206 0.212 0.000 0.238 0.000 tf_stack.py:31(extract_stack)
76024 0.210 0.000 0.702 0.000 ops.py:5246(get_controller)
...
在CPU上，这是前10行
22123473 function calls (21647174 primitive calls) in 60.173 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
16269 42.491 0.003 42.491 0.003 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_Run}
16269 0.568 0.000 48.964 0.003 session.py:1042(_run)
56 0.532 0.010 0.532 0.010 {built-in method time.sleep}
153641 0.458 0.000 0.460 0.000 {built-in method numpy.core.multiarray.array}
183148/125354 0.447 0.000 1.316 0.000 python_message.py:469(init)
1226659 0.362 0.000 0.364 0.000 {built-in method builtins.getattr}
2302110/2301986 0.339 0.000 0.358 0.000 {built-in method builtins.isinstance}
8 0.285 0.036 0.285 0.036 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_ExtendGraph}
12150 0.267 0.000 0.271 0.000 callbacks.py:211(on_batch_end)
147026/49078 0.264 0.000 1.429 0.000 python_message.py:1008(ByteSize)
...
这是我的代码。
def train_generator(x_list, y_list):
# 0.1 validatioin split
train_length = (len(x_list)//10)*9
while True:
for i in range(train_length):
train_x = np.array([x_list[i]])
train_y = np.array([y_list[i]])
yield train_x, train_y
def val_generator(x_list, y_list):
# 0.1 validation split
val_length = len(x_list)//10
while True:
for i in range(-val_length, 0, 1):
val_x = np.array([x_list[i]])
val_y = np.array([y_list[i]])
yield val_x, val_y
with tf.Session(config = tf.ConfigProto(log_device_placement = True)):
model = Sequential()
model.add(LSTM(64, return_sequences=False,
input_shape=(None, 24)))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
checkpointer = ModelCheckpoint(filepath="weights.hdf5",
monitor='val_loss', verbose=1,
save_best_only=True)
history = model.fit_generator(generator=train_generator(train_x,
train_y),
steps_per_epoch=(len(train_x)//10)*9,
epochs=5,
validation_data=val_generator(train_x,
train_y),
validation_steps=len(train_x)//10,
callbacks=[checkpointer],
verbose=2, shuffle=False)
# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='validation')
pyplot.legend()
pyplot.show()
我希望使用GPU进行训练时速度会大大提高。我怎样才能解决这个问题？有人可以帮助我了解造成减速的原因吗？谢谢。
答案 0 :(得分：3)
观察对：
GPU应该用于高度密集的任务和计算（很大的LSTM /沉重的CNN网络）。但是，对于小型MLP甚至LSTM，您可能会发现网络在CPU上的训练速度比在GPU上更快。