Question

我有一台ASUS n552vw笔记本电脑，它具有4GB专用的Geforce GTX 960M图形卡。我将这些代码行放在我的代码的开头，以比较使用GPU或CPU的训练速度，发现使用CPU获胜似乎很成功！

对于GPU：

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

对于CPU：

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

我已经安装了CUDA，cuDNN，tensorflow-gpu等以提高我的训练速度，但似乎发生了相反的事情！

当我尝试第一个代码时，它说（在执行开始之前）：

Train on 2128 samples, validate on 22 samples
Epoch 1/1
2019-08-02 18:49:41.828287: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-08-02 18:49:42.457662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce GTX 960M major: 5 minor: 0 memoryClockRate(GHz): 1.176
pciBusID: 0000:01:00.0
totalMemory: 4.00GiB freeMemory: 3.34GiB
2019-08-02 18:49:42.458819: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-08-02 18:49:43.776498: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-08-02 18:49:43.777007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-08-02 18:49:43.777385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-08-02 18:49:43.777855: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3050 MB memory) -> physical GPU (device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0, compute capability: 5.0)
2019-08-02 18:49:51.834610: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library cublas64_100.dll locally

这真的很慢[Finished in 263.2s]，但是当我尝试第二个代码时，它说：

Train on 2128 samples, validate on 22 samples
Epoch 1/1
2019-08-02 18:51:43.021867: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-08-02 18:51:43.641123: E tensorflow/stream_executor/cuda/cuda_driver.cc:300] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2019-08-02 18:51:43.645072: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:161] retrieving CUDA diagnostic information for host: DESKTOP-UQ8B9FK
2019-08-02 18:51:43.645818: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:168] hostname: DESKTOP-UQ8B9FK

它比第一个代码[Finished in 104.7s]快得多！怎么可能？？

编辑：这是与Tensorflow相关的代码的一部分：

model = Sequential()
model.add((LSTM(un , return_sequences = True)))
model.add(Dropout(dp)) 

model.add((LSTM(un , return_sequences = True)))
model.add(Dropout(dp)) 

model.add((LSTM(un , return_sequences = True)))
model.add(Dropout(dp)) 

model.add((LSTM(un , return_sequences = True)))
model.add(Dropout(dp)) 

model.add((LSTM(un , return_sequences = False)))
model.add(Dropout(dp)) 

model.add(RepeatVector(rp))

model.add((LSTM(un , return_sequences= True))) 
model.add(Dropout(dp))   

model.add((LSTM(un , return_sequences= True))) 
model.add(Dropout(dp))

model.add((LSTM(un , return_sequences= True))) 
model.add(Dropout(dp))

model.add((LSTM(un , return_sequences= True))) 
model.add(Dropout(dp))

model.add((LSTM(un , return_sequences= True))) 
model.add(Dropout(dp))

model.add(TimeDistributed(Dense(ds)))

Answer 1

这里有两个相关的问题：

模型需要“足够大”才能从GPU加速中获利，因为需要将训练数据传输到GPU，并且需要从GPU下载新权重，而这种开销降低了效率，事情变慢了。
对于循环层，将它们并行化并不容易，因为它们在时间步长上具有大量顺序计算。您可能会考虑使用CuDNNLSTM层而不是普通的LSTM，因为它已针对GPU使用进行了优化。

通常对于小型模型，在GPU上进行训练可能不会比在CPU上进行训练更快。

在CPU上运行Tensorflow比在GPU上运行它更快

1 个答案: