如何判断 tensorflow 是否从 python shell 内部使用 gpu 加速? [英] How to tell if tensorflow is using gpu acceleration from inside python shell?

问题描述

我已经使用第二个答案在我的 ubuntu 16.04 中安装了 tensorflow

I have installed tensorflow in my ubuntu 16.04 using the second answer here with ubuntu's builtin apt cuda installation.

Now my question is how can I test if tensorflow is really using gpu? I have a gtx 960m gpu. When I import tensorflow this is the output

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

Is this output enough to check if tensorflow is using gpu ?

解决方案

No, I don't think "open CUDA library" is enough to tell, because different nodes of the graph may be on different devices.

When using tensorflow2:

print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

For tensorflow1, to find out which device is used, you can enable log device placement like this:

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

Check your console for this type of output.

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