TensorFlow无法在GPU上运行（带有TF后端的Keras）

Question

I have followed the steps on installing tensorflow with GPU support and have made sure that the machine I'm using has A GPU thats compatible but it still seems that TensorFlow isn't running properly on my machine. I have a program that trains a keras sequential model(with python 2.7) on a large amount of data using a TensorFlow back end and the output while training is the following:

 2018-04-17 00:35:13.837040: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2018-04-17 00:35:14.042784: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2018-04-17 00:35:14.043143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1344] Found device 0 with properties: name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235 pciBusID: 0000:00:1e.0 totalMemory: 11.17GiB freeMemory: 11.10GiB 2018-04-17 00:35:14.043186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1423] Adding visible gpu devices: 0 2018-04-17 00:35:16.374355: I tensorflow/core/common_runtime/gpu/gpu_device.cc:911] Device interconnect StreamExecutor with strength 1 edge matrix: 2018-04-17 00:35:16.374397: I tensorflow/core/common_runtime/gpu/gpu_device.cc:917] 0 2018-04-17 00:35:16.374405: I tensorflow/core/common_runtime/gpu/gpu_device.cc:930] 0: N 2018-04-17 00:35:16.380956: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1041] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10764 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0, compute capability: 3.7) 

I don't really understand what these logs mean, however, I ran this job simultaneously on a device that just has a CPU and the time it took to complete the training jobs were identical. Can anyone help tell me how to make my training job run on a GPU? Thanks in advance!

Answer 1

You might consider trying to specify a GPU to run your program, which is a simple piece of code.

with tf.device('/gpu:1'):
  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)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print sess.run(c)

If not, I recommend using anaconda3 to create a TensorFlow-GPU virtual environment, which generally defaults to the GPU version.