Question

在我的Ubuntu上安装TensorFlow时，我想将GPU与CUDA一起使用。

的这一步停了下来

这到底./configure到底在哪里？或者我的源树根源在哪里。

我的TensorFlow位于/usr/local/lib/python2.7/dist-packages/tensorflow。但我仍然没有找到./configure。

修改

我根据Salvador Dali's answer找到了./configure。但是在执行示例代码时，我收到以下错误：

>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 8
E tensorflow/stream_executor/cuda/cuda_driver.cc:466] failed call to cuInit: CUDA_ERROR_NO_DEVICE
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:86] kernel driver does not appear to be running on this host (cliu-ubuntu): /proc/driver/nvidia/version does not exist
I tensorflow/core/common_runtime/gpu/gpu_init.cc:112] DMA: 
I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 8

无法找到cuda设备。

答案

请参阅有关如何启用GPU支持的答案here。

Answer 1

这是一个bash脚本，假设在

中

源树的根

当你cloned the repo时。这是https://github.com/tensorflow/tensorflow/blob/master/configure

Answer 2

回答第一个问题：./configure已根据答案here找到。它位于tensorflow的源文件夹下，如here所示。
回答第二个问题：

实际上，我有GPU NVIDIA Corporation GK208GLM [Quadro K610M]。我也安装了CUDA + cuDNN。（因此，以下答案基于您已使用正确版本正确安装CUDA 7.0+ + cuDNN。）但问题是：我安装了驱动程序，但GPU无法正常工作。我让它按以下步骤工作：

起初，我做了lspci并得到了：

01:00.0 VGA compatible controller: NVIDIA Corporation GK208GLM [Quadro K610M] (rev ff)

此处的状态为 rev ff 。然后，我做了sudo update-pciids，再次检查lspci，然后得到：

01:00.0 VGA compatible controller: NVIDIA Corporation GK208GLM [Quadro K610M] (rev a1)

现在，Nvidia GPU的状态正确为 rev a1 。但现在，tensorflow还没有支持GPU。接下来的步骤是（我安装的Nvidia驱动程序是版本nvidia-352）：

sudo modprobe nvidia_352
sudo modprobe nvidia_352_uvm

以便将驱动程序添加到正确的模式。再次检查：

cliu@cliu-ubuntu:~$ lspci -vnn | grep -i VGA -A 12
01:00.0 VGA compatible controller [0300]: NVIDIA Corporation GK208GLM [Quadro K610M] [10de:12b9] (rev a1) (prog-if 00 [VGA controller])
    Subsystem: Hewlett-Packard Company Device [103c:1909]
    Flags: bus master, fast devsel, latency 0, IRQ 16
    Memory at cb000000 (32-bit, non-prefetchable) [size=16M]
    Memory at 50000000 (64-bit, prefetchable) [size=256M]
    Memory at 60000000 (64-bit, prefetchable) [size=32M]
    I/O ports at 5000 [size=128]
    Expansion ROM at cc000000 [disabled] [size=512K]
    Capabilities: <access denied>
    Kernel driver in use: nvidia
cliu@cliu-ubuntu:~$ lsmod | grep nvidia
nvidia_uvm             77824  0 
nvidia               8646656  1 nvidia_uvm
drm                   348160  7 i915,drm_kms_helper,nvidia

我们可以发现显示Kernel driver in use: nvidia且nvidia处于正确模式。

现在，使用示例here来测试GPU：

cliu@cliu-ubuntu:~$ python
Python 2.7.9 (default, Apr  2 2015, 15:33:21) 
[GCC 4.9.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
>>> b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
>>> c = tf.matmul(a, b)
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 8
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:888] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:88] Found device 0 with properties: 
name: Quadro K610M
major: 3 minor: 5 memoryClockRate (GHz) 0.954
pciBusID 0000:01:00.0
Total memory: 1023.81MiB
Free memory: 1007.66MiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:112] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:122] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:643] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro K610M, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_region_allocator.cc:47] Setting region size to 846897152
I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 8
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Quadro K610M, pci bus id: 0000:01:00.0
I tensorflow/core/common_runtime/local_session.cc:107] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Quadro K610M, pci bus id: 0000:01:00.0

>>> print sess.run(c)
b: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:289] b: /job:localhost/replica:0/task:0/gpu:0
a: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:289] a: /job:localhost/replica:0/task:0/gpu:0
MatMul: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:289] MatMul: /job:localhost/replica:0/task:0/gpu:0
[[ 22.  28.]
 [ 49.  64.]]

如您所见，GPU已被使用。

Answer 3

关于第二个问题：您是否安装了兼容的GPU（NVIDIA计算能力3.5或更高版本），是否按照说明安装了CUDA 7.0 + cuDNN？这是您看到失败的最可能原因。如果答案是肯定的，那可能是一个cuda安装问题。当你运行nvidia-smi时，你看到你的GPU列出了吗？如果没有，你需要先修复它。这可能需要获得更新的驱动程序和/或重新运行nvidia-xconfig等。

Answer 4

只有拥有7.0 cuda库和6.5 cudnn库，才能从源代码重建GPU版本。这需要由谷歌更新，我认为

TensorFlow的./configure在哪里以及如何启用GPU支持？

4 个答案: