我已经在我的ubuntu 16.04中安装了tensorflow,使用的是ubuntu内置的apt cuda安装。

现在我的问题是,我如何测试tensorflow是否真的使用gpu?我有一个gtx 960m gpu。当我导入tensorflow时,这是输出

I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally

这个输出是否足够检查tensorflow是否使用gpu ?


当前回答

下面还将返回GPU设备的名称。

import tensorflow as tf
tf.test.gpu_device_name()

其他回答

下面还将返回GPU设备的名称。

import tensorflow as tf
tf.test.gpu_device_name()

在Jupyter或你的IDE中运行这个命令,检查Tensorflow是否使用GPU:

我找到了最简单、最全面的方法。只要设置tf.debug .set_log_device_placement(True),你就可以看到ops是否真的在GPU上运行,例如在device /job:localhost/replica:0/task:0/device:GPU:0中执行op _EagerConst

更多文档:https://www.tensorflow.org/guide/gpu#logging_device_placement

除了其他答案之外,以下内容应该有助于确保你的tensorflow版本包含GPU支持。

import tensorflow as tf
print(tf.test.is_built_with_cuda())

这是我用来列出tf可用设备的行。会话直接从bash:

python -c "import os; os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'; import tensorflow as tf; sess = tf.Session(); [print(x) for x in sess.list_devices()]; print(tf.__version__);"

它将打印可用的设备和tensorflow版本,例如:

_DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 268435456, 10588614393916958794)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 12320120782636586575)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 13378821206986992411)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:GPU:0, GPU, 32039954023, 12481654498215526877)
1.14.0