"Adding visible gpu devices: 0.." has been constantly outputted in nohup.out - tensorflow

Question

I'm running a pso program and I use tensorflow to calculate the mean square error as fitness, but every minute there's an output in nohup.out beginning with "Adding visible gpu devices: 0". I use both gpu and cpu to run my code, but 5 days later they have almost the same rate. Why dose gpu run so slow? How can I stop the constant output?
I use the No.2 device, and the gpu seems to work.
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.130 Driver Version: 384.130 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 TITAN Xp Off | 00000000:02:00.0 Off | N/A |
| 23% 39C P0 61W / 250W | 0MiB / 12189MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 TITAN Xp Off | 00000000:04:00.0 Off | N/A |
| 23% 40C P0 60W / 250W | 0MiB / 12189MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 TITAN Xp Off | 00000000:83:00.0 Off | N/A |
| 23% 39C P8 17W / 250W | 7261MiB / 12189MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 TITAN Xp Off | 00000000:84:00.0 Off | N/A |
| 23% 37C P0 58W / 250W | 0MiB / 12189MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 2 26737 C python 7251MiB
nohup.out is printing the imformation every minute:
2019-11-08 11:49:18.032239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2019-11-08 11:49:18.032316: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-08 11:49:18.032326: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
2019-11-08 11:49:18.032332: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
2019-11-08 11:49:18.032511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6991 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:83:00.0, compute capability: 6.1)
2019-11-08 11:50:42.409142: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2019-11-08 11:50:42.409214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-08 11:50:42.409223: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
2019-11-08 11:50:42.409229: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
2019-11-08 11:50:42.409407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6991 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:83:00.0, compute capability: 6.1)
The objective function is like this:
def function(self, M, w_h, w_o):
def model(X, w_h, w_o):
h = tf.matmul(X, w_h)
return tf.matmul(h, w_o)
X = tf.placeholder(tf.float64, [None, 4])
Y = tf.placeholder(tf.float64, [None, 2])
w_h = tf.Variable(w_h)
w_o = tf.Variable(w_o)
py_x = model(X, w_h, w_o)
loss = tf.reduce_mean((py_x-Y)**2)
with tf.Session() as sess:
tf.initializers.global_variables().run()
sum = 0
length = len(trY)
for start, end in zip(range(0, length, batchsize), range(batchsize, length + 1, batchsize)):
sum += sess.run(loss, feed_dict={X: trX[start:end], Y: trY[start:end]})
if not length%batchsize:
E = sum/(length/batchsize)
else:
E = sum/(1+floor(length/batchsize))
return E