我一直在搞乱 Keras，到目前为止我很喜欢它。在使用相当深的网络时，我一直遇到一个大问题：当调用 model.train_on_batch 或 model.fit 等时，Keras 分配的 GPU 内存明显多于模型本身所需的内存。这并不是由于尝试在一些非常大的图像上进行训练而导致的，而是网络模型本身似乎需要大量 GPU 内存。我创建了这个玩具示例来展示我的意思。基本上是这样的：
我首先创建一个相当深的网络，并使用 model.summary() 获取网络所需的参数总数（在本例中为 206538153，相当于大约 826 MB）。然后，我使用 nvidia-smi 查看 Keras 分配了多少 GPU 内存，我可以看到它非常有意义（849 MB）。
然后我编译网络，并可以确认这不会增加 GPU 内存使用量。正如我们在本例中所看到的，此时我有近 1 GB 的可用 VRAM。
然后，我尝试向网络提供一个简单的 16x16 图像和一个 1x1 真实数据，然后一切都崩溃了，因为 Keras 再次开始分配大量内存，而原因对我来说并不明显。训练网络似乎比仅仅拥有模型需要更多的内存，这对我来说没有意义。我已经在其他框架中在此 GPU 上训练了更深层次的网络，因此这让我认为我使用 Keras 是错误的（或者我的设置或 Keras 中存在问题，但当然这很难确定）。
这是代码：
from scipy import misc
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Reshape, Flatten, ZeroPadding2D, Dropout
import os
model = Sequential()
model.add(Convolution2D(256, 3, 3, border_mode='same', input_shape=(16,16,1)))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Convolution2D(512, 3, 3, border_mode='same'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(Convolution2D(1024, 3, 3, border_mode='same'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Convolution2D(256, 3, 3, border_mode='same'))
model.add(Convolution2D(32, 3, 3, border_mode='same'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(4))
model.add(Dense(1))
model.summary()
os.system("nvidia-smi")
raw_input("Press Enter to continue...")
model.compile(optimizer='sgd',
loss='mse',
metrics=['accuracy'])
os.system("nvidia-smi")
raw_input("Compiled model. Press Enter to continue...")
n_batches = 1
batch_size = 1
for ibatch in range(n_batches):
x = np.random.rand(batch_size, 16,16,1)
y = np.random.rand(batch_size, 1)
os.system("nvidia-smi")
raw_input("About to train one iteration. Press Enter to continue...")
model.train_on_batch(x, y)
print("Trained one iteration")
这为我提供了以下输出：
Using Theano backend.
Using gpu device 0: GeForce GTX 960 (CNMeM is disabled, cuDNN 5103)
/usr/local/lib/python2.7/dist-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.
warnings.warn(warn)
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
convolution2d_1 (Convolution2D) (None, 16, 16, 256) 2560 convolution2d_input_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 8, 8, 256) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 8, 8, 512) 1180160 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D) (None, 4, 4, 512) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, 4, 4, 1024) 4719616 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_3[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_4[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_6[0][0]
____________________________________________________________________________________________________
convolution2d_8 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_7[0][0]
____________________________________________________________________________________________________
convolution2d_9 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_8[0][0]
____________________________________________________________________________________________________
convolution2d_10 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_9[0][0]
____________________________________________________________________________________________________
convolution2d_11 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_10[0][0]
____________________________________________________________________________________________________
convolution2d_12 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_11[0][0]
____________________________________________________________________________________________________
convolution2d_13 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_12[0][0]
____________________________________________________________________________________________________
convolution2d_14 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_13[0][0]
____________________________________________________________________________________________________
convolution2d_15 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_14[0][0]
____________________________________________________________________________________________________
convolution2d_16 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_15[0][0]
____________________________________________________________________________________________________
convolution2d_17 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_16[0][0]
____________________________________________________________________________________________________
convolution2d_18 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_17[0][0]
____________________________________________________________________________________________________
convolution2d_19 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_18[0][0]
____________________________________________________________________________________________________
convolution2d_20 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_19[0][0]
____________________________________________________________________________________________________
convolution2d_21 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_20[0][0]
____________________________________________________________________________________________________
convolution2d_22 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_21[0][0]
____________________________________________________________________________________________________
convolution2d_23 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_22[0][0]
____________________________________________________________________________________________________
convolution2d_24 (Convolution2D) (None, 4, 4, 1024) 9438208 convolution2d_23[0][0]
____________________________________________________________________________________________________
maxpooling2d_3 (MaxPooling2D) (None, 2, 2, 1024) 0 convolution2d_24[0][0]
____________________________________________________________________________________________________
convolution2d_25 (Convolution2D) (None, 2, 2, 256) 2359552 maxpooling2d_3[0][0]
____________________________________________________________________________________________________
convolution2d_26 (Convolution2D) (None, 2, 2, 32) 73760 convolution2d_25[0][0]
____________________________________________________________________________________________________
maxpooling2d_4 (MaxPooling2D) (None, 1, 1, 32) 0 convolution2d_26[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 32) 0 maxpooling2d_4[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 4) 132 flatten_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 1) 5 dense_1[0][0]
====================================================================================================
Total params: 206538153
____________________________________________________________________________________________________
None
Thu Oct 6 09:05:42 2016
+------------------------------------------------------+
| NVIDIA-SMI 352.63 Driver Version: 352.63 |
|-------------------------------+----------------------+----------------------+
| 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 GTX 960 Off | 0000:01:00.0 On | N/A |
| 30% 37C P2 28W / 120W | 1082MiB / 2044MiB | 9% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1796 G /usr/bin/X 155MiB |
| 0 2597 G compiz 65MiB |
| 0 5966 C python 849MiB |
+-----------------------------------------------------------------------------+
Press Enter to continue...
Thu Oct 6 09:05:44 2016
+------------------------------------------------------+
| NVIDIA-SMI 352.63 Driver Version: 352.63 |
|-------------------------------+----------------------+----------------------+
| 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 GTX 960 Off | 0000:01:00.0 On | N/A |
| 30% 38C P2 28W / 120W | 1082MiB / 2044MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1796 G /usr/bin/X 155MiB |
| 0 2597 G compiz 65MiB |
| 0 5966 C python 849MiB |
+-----------------------------------------------------------------------------+
Compiled model. Press Enter to continue...
Thu Oct 6 09:05:44 2016
+------------------------------------------------------+
| NVIDIA-SMI 352.63 Driver Version: 352.63 |
|-------------------------------+----------------------+----------------------+
| 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 GTX 960 Off | 0000:01:00.0 On | N/A |
| 30% 38C P2 28W / 120W | 1082MiB / 2044MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1796 G /usr/bin/X 155MiB |
| 0 2597 G compiz 65MiB |
| 0 5966 C python 849MiB |
+-----------------------------------------------------------------------------+
About to train one iteration. Press Enter to continue...
Error allocating 37748736 bytes of device memory (out of memory). Driver report 34205696 bytes free and 2144010240 bytes total
Traceback (most recent call last):
File "memtest.py", line 65, in <module>
model.train_on_batch(x, y)
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 712, in train_on_batch
class_weight=class_weight)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1221, in train_on_batch
outputs = self.train_function(ins)
File "/usr/local/lib/python2.7/dist-packages/keras/backend/theano_backend.py", line 717, in __call__
return self.function(*inputs)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 871, in __call__
storage_map=getattr(self.fn, 'storage_map', None))
File "/usr/local/lib/python2.7/dist-packages/theano/gof/link.py", line 314, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 859, in __call__
outputs = self.fn()
MemoryError: Error allocating 37748736 bytes of device memory (out of memory).
Apply node that caused the error: GpuContiguous(GpuDimShuffle{3,2,0,1}.0)
Toposort index: 338
Inputs types: [CudaNdarrayType(float32, 4D)]
Inputs shapes: [(1024, 1024, 3, 3)]
Inputs strides: [(1, 1024, 3145728, 1048576)]
Inputs values: ['not shown']
Outputs clients: [[GpuDnnConv{algo='small', inplace=True}(GpuContiguous.0, GpuContiguous.0, GpuAllocEmpty.0, GpuDnnConvDesc{border_mode='half', subsample=(1, 1), conv_mode='conv', precision='float32'}.0, Constant{1.0}, Constant{0.0}), GpuDnnConvGradI{algo='none', inplace=True}(GpuContiguous.0, GpuContiguous.0, GpuAllocEmpty.0, GpuDnnConvDesc{border_mode='half', subsample=(1, 1), conv_mode='conv', precision='float32'}.0, Constant{1.0}, Constant{0.0})]]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
有几点需要注意：
- 我尝试过 Theano 和 TensorFlow 后端。两者都有同样的问题，并且在同一行内存不足。在 TensorFlow 中，Keras 似乎预分配了大量内存（约 1.5 GB），因此 nvidia-smi 无法帮助我们跟踪那里发生的情况，但我得到了相同的内存不足异常。同样，这指向 Keras（我的使用）中的错误（尽管很难确定这些事情，但可能与我的设置有关）。
- 我尝试在 Theano 中使用 CNMEM，其行为类似于 TensorFlow：它预先分配大量内存（约 1.5 GB），但在同一位置崩溃。
- 有一些关于 CudNN 版本的警告。我尝试使用 CUDA 而不是 CudNN 运行 Theano 后端，并且得到了相同的错误，所以这不是问题的根源。
- 如果您想在自己的 GPU 上进行测试，您可能需要使网络更深/更浅，具体取决于您需要多少 GPU 内存来测试。
- 我的配置如下：Ubuntu 14.04、GeForce GTX 960、CUDA 7.5.18、CudNN 5.1.3、Python 2.7、Keras 1.1.0（通过pip安装）
- 我尝试更改模型的编译以使用不同的优化器和损失，但这似乎没有改变任何东西。
- 我尝试更改 train_on_batch 函数以使用 fit 代替，但它有同样的问题。
- 我在 StackOverflow 上看到了一个类似的问题 -为什么这个 Keras 模型需要超过 6GB 的内存？ https://stackoverflow.com/questions/35757151/why-does-this-keras-model-require-over-6gb-of-memory- 但据我所知，我的配置中没有这些问题。我从未安装过多个版本的 CUDA，并且我已经多次检查 PATH、LD_LIBRARY_PATH 和 CUDA_ROOT 变量，次数多得我无法计算。
- Julius 建议激活参数本身会占用 GPU 内存。如果这是真的，有人可以更清楚地解释一下吗？据我所知，我尝试将卷积层的激活函数更改为明显硬编码的函数，没有可学习的参数，但这并没有改变任何东西。此外，这些参数似乎不太可能占用与网络本身其余部分几乎一样多的内存。
- 经过彻底的测试，我可以训练的最大网络大约有 453 MB 的参数，而我的 GPU RAM 大约为 2 GB。这是正常的吗？
- 在一些适合我的 GPU 的较小 CNN 上测试 Keras 后，我发现 GPU RAM 使用量突然出现峰值。如果我运行一个包含大约 100 MB 参数的网络，则在训练过程中 99% 的时间它将使用不到 200 MB 的 GPU RAM。但每隔一段时间，内存使用量就会飙升至约 1.3 GB。似乎可以肯定地认为正是这些尖峰导致了我的问题。我从未在其他框架中见过这些峰值，但它们的存在可能有充分的理由吗？如果有人知道导致它们的原因，并且有办法避免它们，请插话！
