0

例如，如果我运行这段代码：

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)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(c))

然后我会得到这个：

    2017-06-24 10:20:57.441289: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-06-24 10:20:57.442069: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-06-24 10:20:57.443010: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-06-24 10:20:57.444615: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-06-24 10:20:57.445662: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-06-24 10:20:57.446273: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-06-24 10:20:57.447475: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-06-24 10:20:57.448190: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-06-24 10:21:01.548276: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:940] Found device 0 with properties: 
name: GeForce GTX 1060
major: 6 minor: 1 memoryClockRate (GHz) 1.6705
pciBusID 0000:01:00.0
Total memory: 6.00GiB
Free memory: 5.01GiB
2017-06-24 10:21:01.549636: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:961] DMA: 0 
2017-06-24 10:21:01.550040: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0:   Y 
2017-06-24 10:21:01.550479: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0
2017-06-24 10:21:01.910806: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\direct_session.cc:265] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0

MatMul: (MatMul): /job:localhost/replica:0/task:0/gpu:0
b: (Const): /job:localhost/replica:0/task:0/gpu:0
a: (Const): /job:localhost/replica:0/task:0/gpu:0
2017-06-24 10:21:01.918147: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] MatMul: (MatMul)/job:localhost/replica:0/task:0/gpu:0
2017-06-24 10:21:01.918794: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] b: (Const)/job:localhost/replica:0/task:0/gpu:0
2017-06-24 10:21:01.919403: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] a: (Const)/job:localhost/replica:0/task:0/gpu:0
[[ 22.  28.]
 [ 49.  64.]]

警告总是出现，非常烦人。我尝试过使用其他解决方案，例如：

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

但它仍然给了我同样的警告。两件事，我怎样才能摆脱警告？tensorflow 只使用 GPU 吗？

4

2 回答 2

1

您正在获取此信息转储，因为您正在Session明确地开始您的指示以显示它们：

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

该log_device_placement=True位专门用于演示在哪些设备上执行了哪些操作（例如，证明 GPU 确实被正确使用等）

如果您不想看到此信息，只需在没有此类选项的情况下启动会话：

sess = tf.Session()

但是，拥有这一点仍然很有帮助：

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

因为 TF 可能会向您显示您可能会觉得烦人/不必要的其他警告，所以我建议保留这条线（但根据我的经验，这对 Windows 没有任何影响，仅在 Mac 和 Linux 上）

于 2017-06-24T10:38:00.547 回答
1

有建筑参数引起的警告。使用参数重新编译您的 tensorflowbazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-msse --copt=-msse2 --copt=-msse3 --copt=-msse4.1 --copt=-msse4.2 --copt=-mfpmath=both --config=cuda -k //tensorflow/tools/pip_package:build_pip_package将解决问题

于 2017-06-24T03:27:01.127 回答