Question

系统： Ubuntu 16.04.2 cudnn 5.1，CUDA 8.0

我从git（最新版本）安装了theano。

当我从https://github.com/yusuketomoto/chainer-fast-neuralstyle/tree/resize-conv运行生成样本时，它会报告内存是否已使用CPU或GPU。

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

-

import theano.sandbox.cuda.basic_ops as sbcuda
sbcuda.cuda_ndarray.cuda_ndarray.mem_info()
(500105216L, 11995578368L)

-

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

这些数字究竟是什么意思？ Theano / Chainer只能访问~500MB的VRAM？

Answer 1

我设法通过完全卸载theano解决了这个问题。我很困惑为什么导入chainer显示theano警告，但它是这样做的。卸载theano允许chainer脚本工作。

Theano / Chainer报告不报告在K80上使用12GB RAM正确的免费VRAM

1 个答案: