TensorFlow的./configure在哪里，以及如何启用GPU支持? [英] where is the ./configure of TensorFlow and how to enable the GPU support?

问题描述

在Ubuntu上安装TensorFlow时，我想将GPU与CUDA一起使用.

When installing TensorFlow on my Ubuntu, I would like to use GPU with CUDA.

但是我在官方教程中停了下来:

这./configure到底在哪里?或者我的源代码树的根在哪里?

Where exactly is this ./configure ? Or where is my root of source tree.

我的TensorFlow位于此处/usr/local/lib/python2.7/dist-packages/tensorflow.但是我仍然没有找到./configure.

My TensorFlow is located here /usr/local/lib/python2.7/dist-packages/tensorflow. But I still did not find ./configure.

编辑

我已经根据 Salvador Dali的答案找到了./configure.但是在执行示例代码时，出现以下错误:

I have found the ./configure according to Salvador Dali's answer. But when doing the example code, I got the following error:

>>> 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

找不到cuda设备.

The cuda device cannot be found.

答案

在此处中查看有关如何启用GPU支持的答案.

See the answer about how did I enable GPU support here.

推荐答案

• 第一个问题的答案:已根据此处找到答案./configure.它位于tensorflow的源文件夹下，如此处所示.

  • Answer to first question: ./configure has already been found according to the answer here. It is under the source folder of tensorflow as shown here.

    第二个问题的答案:

    实际上，我有GPU NVIDIA Corporation GK208GLM [Quadro K610M].我还安装了CUDA + cuDNN. (因此，以下答案是基于您已经使用正确的版本正确安装了CUDA 7.0+ + cuDNN的.)问题是:我已经安装了驱动程序，但是GPU无法正常工作.我通过以下步骤使其工作:

    Actually, I have the GPU NVIDIA Corporation GK208GLM [Quadro K610M]. I also have CUDA + cuDNN installed. (Therefore, the following answer is based on you have already installed CUDA 7.0+ + cuDNN correctly with the correct versions.) However the problem is: I have driver installed but the GPU is just not working. I made it working in the following steps:

    起初，我这样做lspci并得到:

    At first, I did this lspci and got:

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

    此处的状态为修订版.然后，我做了sudo update-pciids，然后再次检查lspci，并得到了:

    The status here is rev ff. Then, I did sudo update-pciids, and check lspci again, and got:

    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 .但是现在，tensorflow尚不支持GPU.下一步是(我安装的Nvidia驱动程序是版本nvidia-352):

    Now, the status of Nvidia GPU is correct as rev a1. But now, the tensorflow is not supporting GPU yet. The next steps are (the Nvidia driver I installed is version 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:

    in order to add the driver into correct mode. Check again:

    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
    

    我们发现显示了Kernel driver in use: nvidia并且nvidia处于正确模式.

    We can find that the Kernel driver in use: nvidia is shown and nvidia is in correct mode.

    现在，使用示例 用于测试GPU:

    Now, use the example here for testing the 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被利用.

    As you can see, the GPU is utilized.

    这篇关于TensorFlow的./configure在哪里，以及如何启用GPU支持?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！