tensorflow-gpu 已安装但未用于计算 [英] tensorflow-gpu installed but not used for calculation

问题描述

当我运行示例代码时，我全新安装了 Windows 10 并安装了 tensorflow-gpu(我认为我应该已经成功完成了)，我看到 gpu0 的用法如下:

<预><代码>>>>将张量流导入为 tf>>># 创建一个图形.... 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)>>># 创建一个 log_device_placement 设置为 True 的会话.... sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))2017-05-08 02:10:35.354149: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] TensorFlow 库是't 编译为使用 SSE 指令，但这些指令在您的机器上可用并且可以加速 CPU 计算.2017-05-08 02:10:35.354283: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] TensorFlow 库是't 编译为使用 SSE2 指令，但这些在您的机器上可用并且可以加速 CPU 计算.2017-05-08 02:10:35.355376:W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] TensorFlow 库是't 编译为使用 SSE3 指令，但这些在您的机器上可用并且可以加速 CPU 计算.2017-05-08 02:10:35.355835: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] TensorFlow 库是't 编译为使用 SSE4.1 指令，但这些在您的机器上可用并且可以加速 CPU 计算.2017-05-08 02:10:35.356245:W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] TensorFlow 库是't 编译为使用 SSE4.2 指令，但这些在您的机器上可用并且可以加速 CPU 计算.2017-05-08 02:10:35.356629: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] TensorFlow 库是't 编译为使用 AVX 指令，但这些在您的机器上可用并且可以加速 CPU 计算.2017-05-08 02:10:35.356977:W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] TensorFlow 库是't 编译为使用 AVX2 指令，但这些指令在您的机器上可用并且可以加快 CPU 计算.2017-05-08 02:10:35.357376: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] TensorFlow 库是't 编译为使用 FMA 指令，但这些在您的机器上可用并且可以加速 CPU 计算.2017-05-08 02:10:35.765058:我 c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:887] 找到设备 0有属性:名称:GeForce GTX 1080 Ti主要:6 次要:1 memoryClockRate (GHz) 1.607pciBusID 0000:01:00.0总内存:11.00GiB可用内存:9.12GiB2017-05-08 02:10:35.765151:我 c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:908] DMA:02017-05-08 02:10:35.765851:我 c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:918] 0:Y2017-05-08 02:10:35.780335:我 c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:977] 创建 TensorFlow 设备(/gpu:0) ->(设备:0，名称:GeForce GTX 1080 Ti，pci 总线 ID:0000:01:00.0)设备映射:/job:localhost/replica:0/task:0/gpu:0 ->设备:0，名称:GeForce GTX 1080 Ti，pci 总线 ID:0000:01:00.02017-05-08 02:10:36.157808:我 c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\direct_session.cc:257] 设备映射:/job:localhost/replica:0/task:0/gpu:0 ->设备:0，名称:GeForce GTX 1080 Ti，pci 总线 ID:0000:01:00.0>>># 运行操作....打印(sess.run(c))MatMul: (MatMul):/job:localhost/replica:0/task:0/gpu:02017-05-08 02:10:46.297244:我 c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:841] MatMul: (MatMul)/job:localhost/replica:0/task:0/gpu:0b: (Const):/job:localhost/replica:0/task:0/gpu:02017-05-08 02:10:46.299024: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:841] b: (Const)/job:localhost/replica:0/task:0/gpu:0a: (Const):/job:localhost/replica:0/task:0/gpu:02017-05-08 02:10:46.302386:我 c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:841] a:(常量)/job:localhost/replica:0/task:0/gpu:0[[ 22. 28.][49. 64.]]

但是当我运行deep-learning代码时，gpu-memory全部使用，但gpu-loading几乎为0.cpu-loading在20%左右(在安装tensorflow-gpu之前，cpu-loading是100%)，学习时间比用cpu快一点.

可能是什么原因?请提前给我一些，非常感谢

解决方案

线路 gpu:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0表明计算是在 GPU 上完成的.使用率不太高的原因可能是因为相对于 GPU 的能力，没有太多的处理要做.

I have a fresh install of windows 10 and installed tensorflow-gpu (i think i should have done successfully) as i run the sample code, i see the gpu0 is used as follow:

>>> import tensorflow as tf
>>> # Creates a graph.
... 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)
>>> # Creates a session with log_device_placement set to True.
... sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
2017-05-08 02:10:35.354149: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-05-08 02:10:35.354283: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-08 02:10:35.355376: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-08 02:10:35.355835: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-08 02:10:35.356245: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-08 02:10:35.356629: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-05-08 02:10:35.356977: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-08 02:10:35.357376: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-05-08 02:10:35.765058: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.607
pciBusID 0000:01:00.0
Total memory: 11.00GiB
Free memory: 9.12GiB
2017-05-08 02:10:35.765151: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:908] DMA: 0
2017-05-08 02:10:35.765851: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:918] 0:   Y
2017-05-08 02:10:35.780335: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0
2017-05-08 02:10:36.157808: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\direct_session.cc:257] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0

>>> # Runs the op.
... print(sess.run(c))
MatMul: (MatMul): /job:localhost/replica:0/task:0/gpu:0
2017-05-08 02:10:46.297244: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:841] MatMul: (MatMul)/job:localhost/replica:0/task:0/gpu:0
b: (Const): /job:localhost/replica:0/task:0/gpu:0
2017-05-08 02:10:46.299024: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:841] b: (Const)/job:localhost/replica:0/task:0/gpu:0
a: (Const): /job:localhost/replica:0/task:0/gpu:0
2017-05-08 02:10:46.302386: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:841] a: (Const)/job:localhost/replica:0/task:0/gpu:0
[[ 22.  28.]
 [ 49.  64.]]

But when i run the code for deep-learning, the gpu-memory is all used but the gpu-loading is almost 0. the cpu-loading is around 20% (before install tensorflow-gpu, the cpu-loading is 100%), the time for learning is a bit faster than using cpu.

what may be the cause? Please give me some advance, thank you so much

解决方案

The line gpu:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0 shows that the computation is being done on the gpu. The reason the useage isn't too high is probably because there isn't too much processing to do relative to the power of your GPU.

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