我正在运行Keras的示例kaggle_otto_nn.py，后端为theano。当我设置cnmem=1时，会出现以下错误：
cliu@cliu-ubuntu:keras-examples$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32,lib.cnmem=1 python kaggle_otto_nn.py
Using Theano backend.
ERROR (theano.sandbox.cuda): ERROR: Not using GPU. Initialisation of device gpu failed:
initCnmem: cnmemInit call failed! Reason=CNMEM_STATUS_OUT_OF_MEMORY. numdev=1
/usr/local/lib/python2.7/dist-packages/Theano-0.8.0rc1-py2.7.egg/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
"downsample module has been moved to the theano.tensor.signal.pool module.")
Traceback (most recent call last):
File "kaggle_otto_nn.py", line 28, in <module>
from keras.models import Sequential
File "build/bdist.linux-x86_64/egg/keras/models.py", line 15, in <module>
File "build/bdist.linux-x86_64/egg/keras/backend/__init__.py", line 46, in <module>
File "build/bdist.linux-x86_64/egg/keras/backend/theano_backend.py", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/Theano-0.8.0rc1-py2.7.egg/theano/__init__.py", line 111, in <module>
theano.sandbox.cuda.tests.test_driver.test_nvidia_driver1()
File "/usr/local/lib/python2.7/dist-packages/Theano-0.8.0rc1-py2.7.egg/theano/sandbox/cuda/tests/test_driver.py", line 38, in test_nvidia_driver1
if not numpy.allclose(f(), a.sum()):
File "/usr/local/lib/python2.7/dist-packages/Theano-0.8.0rc1-py2.7.egg/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-0.8.0rc1-py2.7.egg/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-0.8.0rc1-py2.7.egg/theano/compile/function_module.py", line 859, in __call__
outputs = self.fn()
RuntimeError: Cuda error: kernel_reduce_ccontig_node_97496c4d3cf9a06dc4082cc141f918d2_0: out of memory. (grid: 1 x 1; block: 256 x 1 x 1)
Apply node that caused the error: GpuCAReduce{add}{1}(<CudaNdarrayType(float32, vector)>)
Toposort index: 0
Inputs types: [CudaNdarrayType(float32, vector)]
Inputs shapes: [(10000,)]
Inputs strides: [(1,)]
Inputs values: ['not shown']
Outputs clients: [[HostFromGpu(GpuCAReduce{add}{1}.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.
似乎我无法将cnmem设置为非常大的值（大约> 0.9），因为它可能会导致 GPU 的内存溢出。当我设置cnmem=0.9时，它正在正常工作。 据此，它
表示内存池的起始大小（以 MB 或总 GPU 内存的百分比为单位）。
和
这可能会导致内存碎片。因此，如果您在使用 cnmem 时遇到内存错误，请尝试在开始时分配更多内存或禁用它。如果您尝试此操作，请在 ：ref
theano-dev上报告您的结果。
但是，如果我遇到内存错误，为什么要在开始时分配更多内存？ 就我而言，在开始时分配更多内存似乎会导致错误。
这个问题就是基于此解决的。
如此处所示，cnmem实际上只允许分配为float。
0：未启用。
0
<= 1：使用总 GPU 内存的这一部分（驱动程序内存裁剪为 .95）。 > 1：以兆字节 （MB） 内存为单位使用此数字。
因此，如果cnmem=1.0而不是cnmem=1，它将起作用.