Keras multi_gpu_model 导致系统崩溃答案

【问题标题】：Keras multi_gpu_model causes system to crashKeras multi_gpu_model 导致系统崩溃
【发布时间】：2019-07-12 14:47:00
【问题描述】：

我正在尝试在大型数据集上训练一个相当大的 LSTM，并使用 4 个 GPU 来分配负载。如果我尝试只训练其中一个（其中任何一个，我都尝试过），它会正常运行，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")

改成

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，系统有大约 4GB 内存（尽管我怀疑这是问题所在，因为我使用的是生成器）。

【问题讨论】：

标签： tensorflow keras gpu lstm nvidia

【解决方案1】：

Keras 使用所有 4 个 GPU 计算，并且可以使用 CPU 进行代码编译。你可以试试下面的代码。更多信息请查看 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")

【讨论】：