Question

我正在尝试在大型数据集上训练相当大的LSTM，并具有4个GPU来分配负载。如果我尝试只训练其中的一个（其中的每个，我都尝试过），它就可以正常运行，但是在添加multi_gpu_model代码后，当我尝试运行它时，它会使我的整个系统崩溃。这是我的多GPU代码

batch_size = 8
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(len(inputData[0]), len(inputData[0][0])) ))
model.add(LSTM(256,  return_sequences=True))
model.add(Dropout(.2))
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(.2))
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(.2))
model.add(Dense(len(outputData[0][0]),  activation='softmax'))
rms = RMSprop()
p_model = multi_gpu_model(model, gpus=4)
p_model.compile(loss='categorical_crossentropy',optimizer=rms, metrics=['categorical_accuracy'])

print("Fitting")
p_model.fit_generator(songBatchGenerator(songList,batch_size), epochs=250,  verbose=1,  shuffle=False, steps_per_epoch=math.ceil(len(songList)/batch_size))
pickleSave('kerasTrained.pickle', parallel_model)
print("Saved")

将此更改为

batch_size = 8
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(len(inputData[0]), len(inputData[0][0])) ))
model.add(LSTM(256,  return_sequences=True))
model.add(Dropout(.2))
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(.2))
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(.2))
model.add(Dense(len(outputData[0][0]),  activation='softmax'))
rms = RMSprop()

model.compile(loss='categorical_crossentropy',optimizer=rms, metrics=['categorical_accuracy'])

print("Fitting")
model.fit_generator(songBatchGenerator(songList,batch_size), epochs=250,  verbose=1,  shuffle=False, steps_per_epoch=math.ceil(len(songList)/batch_size))
pickleSave('kerasTrained.pickle', parallel_model)
print("Saved")

功能完美

3个GPU是Nvidia 1060 3GB，1个是6GB，系统具有大约4GB的内存（尽管我怀疑这是问题，因为我使用的是发生器）。

Answer 1

Keras使用所有4个GPU计算，并且可以使用CPU进行代码编译。您可以尝试以下代码。有关更多信息，请参见tensorflow网站链接https://www.tensorflow.org/api_docs/python/tf/keras/utils/multi_gpu_model

def create_model():
   batch_size = 8
   model = Sequential()
   model.add(Masking(mask_value=0., input_shape=(len(inputData[0]), len(inputData[0][0])) ))
   model.add(LSTM(256,  return_sequences=True))
   model.add(Dropout(.2))
   model.add(LSTM(128, return_sequences=True))
   model.add(Dropout(.2))
   model.add(LSTM(128, return_sequences=True))
   model.add(Dropout(.2))
   model.add(Dense(len(outputData[0][0]),  activation='softmax'))

   return model


# we'll store a copy of the model on *every* GPU and then combine
# the results from the gradient updates on the CPU
# initialize the model
with tf.device("/cpu:0"):
     model = create_model()

# make the model parallel
p_model = multi_gpu_model(model, gpus=4)


rms = RMSprop()
p_model.compile(loss='categorical_crossentropy',optimizer=rms, metrics=['categorical_accuracy'])
print("Fitting")
p_model.fit_generator(songBatchGenerator(songList,batch_size), epochs=250,  verbose=1,  shuffle=False, steps_per_epoch=math.ceil(len(songList)/batch_size))
pickleSave('kerasTrained.pickle', parallel_model)
print("Saved")

Keras multi_gpu_model导致系统崩溃

1 个答案: