如何检查keras是否使用gpu版本的tensorflow？

When I run a keras script, I get the following output:

当我运行keras脚本时,我得到以下输出:

Using TensorFlow backend.
2017-06-14 17:40:44.621761: W 
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.
2017-06-14 17:40:44.621783: W 
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.
2017-06-14 17:40:44.621788: W 
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.
2017-06-14 17:40:44.621791: W 
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.
2017-06-14 17:40:44.621795: W 
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.
2017-06-14 17:40:44.721911: I 
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful 
NUMA node read from SysFS had negative value (-1), but there must be 
at least one NUMA node, so returning NUMA node zero
2017-06-14 17:40:44.722288: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 
with properties: 
name: GeForce GTX 850M
major: 5 minor: 0 memoryClockRate (GHz) 0.9015
pciBusID 0000:0a:00.0
Total memory: 3.95GiB
Free memory: 3.69GiB
2017-06-14 17:40:44.722302: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
2017-06-14 17:40:44.722307: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y 
2017-06-14 17:40:44.722312: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating 
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, 
pci bus id: 0000:0a:00.0)

What does this mean? Am I using GPU or CPU version of tensorflow?

这是什么意思?我使用GPU或CPU版本的张量流?

Before installing keras, I was working with the GPU version of tensorflow.

在安装keras之前,我正在使用GPU版本的tensorflow。

Also sudo pip3 list shows tensorflow-gpu(1.1.0) and nothing like tensorflow-cpu.

另外sudo pip3列表显示tensorflow-gpu(1.1.0),没有像tensorflow-cpu。

Running the command mentioned on [this * question], gives the following:

运行[此*问题]中提到的命令,提供以下内容:

The TensorFlow library wasn't compiled to use SSE4.1 instructions, 
but these are available on your machine and could speed up CPU 
computations.
2017-06-14 17:53:31.424793: W 
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.
2017-06-14 17:53:31.424803: W 
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.
2017-06-14 17:53:31.424812: W 
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.
2017-06-14 17:53:31.424820: W 
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.
2017-06-14 17:53:31.540959: I 
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful 
NUMA node read from SysFS had negative value (-1), but there must be 
at least one NUMA node, so returning NUMA node zero
2017-06-14 17:53:31.541359: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 
with properties: 
name: GeForce GTX 850M
major: 5 minor: 0 memoryClockRate (GHz) 0.9015
pciBusID 0000:0a:00.0
Total memory: 3.95GiB
Free memory: 128.12MiB
2017-06-14 17:53:31.541407: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
2017-06-14 17:53:31.541420: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y 
2017-06-14 17:53:31.541441: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating 
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, 
pci bus id: 0000:0a:00.0)
2017-06-14 17:53:31.547902: E 
tensorflow/stream_executor/cuda/cuda_driver.cc:893] failed to 
allocate 128.12M (134348800 bytes) from device: 
CUDA_ERROR_OUT_OF_MEMORY
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce 
GTX 850M, pci bus id: 0000:0a:00.0
2017-06-14 17:53:31.549482: I 
tensorflow/core/common_runtime/direct_session.cc:257] Device 
mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce 
GTX 850M, pci bus id: 0000:0a:00.0

1 个解决方案

#1

You are using the GPU version. You can list the available tensorflow devices with (also check this question):

您正在使用GPU版本。您可以列出可用的tensorflow设备(也请查看此问题):

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

In your case both the cpu and gpu are available, if you use the cpu version of tensorflow the gpu will not be listed. In your case, without setting your tensorflow device (with tf.device("..")), tensorflow will automatically pick your gpu!

在你的情况下,cpu和gpu都可用,如果使用exporflow的cpu版本,则不会列出gpu。在你的情况下,没有设置你的tensorflow设备(使用tf.device(“..”)),tensorflow会自动选择你的gpu!

In addition, your sudo pip3 list clearly shows you are using tensorflow-gpu. If you would have the tensoflow cpu version the name would be something like tensorflow(1.1.0).

另外,你的sudo pip3列表清楚地显示你正在使用tensorflow-gpu。如果你有tenoflow cpu版本,名称就像tensorflow(1.1.0)。

Check this issue for information about the warnings.

有关警告的信息,请查看此问题。

#1