システム: Ubuntu 16.04.2 cudnn 5.1、CUDA 8.0
git（最新バージョン）からtheanoをインストールしました。
https://github.com/yusuketomoto/chainer-fast-neuralstyle/tree/resize-convから生成サンプルを実行すると、CPU または GPU が使用されているかどうかにかかわらず、メモリ不足が報告されます。
python generate.py sample_images/tubingen.jpg -m models/composition.model -o sample_images/output.jpg -g 0
WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL:
https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29
/home/ubuntu/Theano/theano/sandbox/cuda/__init__.py:558: UserWarning: Theano flag device=gpu* (old gpu back-end) only support floatX=float32. You have floatX=float64. Use the new gpu back-end with device=cuda* for that value of floatX.
warnings.warn(msg)
Using gpu device 0: Tesla K80 (CNMeM is enabled with initial size: 95.0% of memory, cuDNN 5105)
Traceback (most recent call last):
File "generate.py", line 45, in <module>
y = model(x)
File "/home/ubuntu/chainer-fast-neuralstyle/net.py", line 56, in __call__
h = F.relu(self.b2(self.c2(h), test=test))
File "/usr/local/lib/python2.7/dist-packages/chainer/links/connection/convolution_2d.py", line 108, in __call__
deterministic=self.deterministic)
File "/usr/local/lib/python2.7/dist-packages/chainer/functions/connection/convolution_2d.py", line 326, in convolution_2d
return func(x, W, b)
File "/usr/local/lib/python2.7/dist-packages/chainer/function.py", line 199, in __call__
outputs = self.forward(in_data)
File "/usr/local/lib/python2.7/dist-packages/chainer/function.py", line 310, in forward
return self.forward_gpu(inputs)
File "/usr/local/lib/python2.7/dist-packages/chainer/functions/connection/convolution_2d.py", line 90, in forward_gpu
y = cuda.cupy.empty((n, out_c, out_h, out_w), dtype=x.dtype)
File "/usr/local/lib/python2.7/dist-packages/cupy/creation/basic.py", line 19, in empty
return cupy.ndarray(shape, dtype=dtype, order=order)
File "cupy/core/core.pyx", line 88, in cupy.core.core.ndarray.__init__ (cupy/core/core.cpp:6333)
File "cupy/cuda/memory.pyx", line 280, in cupy.cuda.memory.alloc (cupy/cuda/memory.cpp:5988)
File "cupy/cuda/memory.pyx", line 431, in cupy.cuda.memory.MemoryPool.malloc (cupy/cuda/memory.cpp:9256)
File "cupy/cuda/memory.pyx", line 447, in cupy.cuda.memory.MemoryPool.malloc (cupy/cuda/memory.cpp:9162)
File "cupy/cuda/memory.pyx", line 342, in cupy.cuda.memory.SingleDeviceMemoryPool.malloc (cupy/cuda/memory.cpp:7817)
File "cupy/cuda/memory.pyx", line 368, in cupy.cuda.memory.SingleDeviceMemoryPool.malloc (cupy/cuda/memory.cpp:7592)
File "cupy/cuda/memory.pyx", line 260, in cupy.cuda.memory._malloc (cupy/cuda/memory.cpp:5930)
File "cupy/cuda/memory.pyx", line 261, in cupy.cuda.memory._malloc (cupy/cuda/memory.cpp:5851)
File "cupy/cuda/memory.pyx", line 35, in cupy.cuda.memory.Memory.__init__ (cupy/cuda/memory.cpp:1772)
File "cupy/cuda/runtime.pyx", line 207, in cupy.cuda.runtime.malloc (cupy/cuda/runtime.cpp:3429)
File "cupy/cuda/runtime.pyx", line 130, in cupy.cuda.runtime.check_status (cupy/cuda/runtime.cpp:2241)
cupy.cuda.runtime.CUDARuntimeError: cudaErrorMemoryAllocation: out of memory
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import theano.sandbox.cuda.basic_ops as sbcuda
sbcuda.cuda_ndarray.cuda_ndarray.mem_info()
(500105216L, 11995578368L)
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lspci -vvv |grep -i -A 20 nvidia
00:04.0 3D controller: NVIDIA Corporation GK210GL [Tesla K80] (rev a1)
Subsystem: NVIDIA Corporation GK210GL [Tesla K80]
Physical Slot: 4
Control: I/O+ Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx-
Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx-
Latency: 0
Interrupt: pin A routed to IRQ 11
Region 0: Memory at fd000000 (32-bit, non-prefetchable) [size=16M]
Region 1: Memory at 400000000 (64-bit, prefetchable) [size=16G]
Region 3: Memory at 800000000 (64-bit, prefetchable) [size=32M]
Region 5: I/O ports at c000 [size=128]
Capabilities: <access denied>
Kernel driver in use: nvidia
Kernel modules: nvidia_375_drm, nvidia_375
それらの数字は正確には何を意味するのでしょうか？Theano/Chainer は ~500MB の VRAM にしかアクセスできませんか?