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¿Cómo compruebo si keras está utilizando la versión gpu de tensorflow?

Question

Cuando ejecuto un script keras, obtengo el siguiente resultado:

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)

¿Qué significa esto? ¿Estoy usando GPU o CPU versión de tensorflow?

Antes de instalar keras, estaba trabajando con la versión GPU de tensorflow.

También la sudo pip3 list muestra tensorflow-gpu(1.1.0) y nada como tensorflow-cpu .

Al ejecutar el comando mencionado en [esta pregunta de stackoverflow], aparece lo siguiente:

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

Answer 1

Estás utilizando la versión de GPU. Puede enumerar los dispositivos de tensorflow disponibles con (también marque this pregunta):

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

En su caso, tanto el cpu como el gpu están disponibles, si utiliza la versión de censor de tensorflow, el gpu no aparecerá en la lista. En su caso, sin configurar su dispositivo tensorflow ( with tf.device("..") ), tensorflow elegirá automáticamente su gpu!

Además, su sudo pip3 list muestra claramente que está usando tensorflow-gpu. Si tuviera la versión de CPU de tensoflow, el nombre sería algo así como tensorflow(1.1.0) .

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