tf打开gpu

import tensorflow as tf
# 显示几块gpu可用
print ("Num GPUs Available:", len(tf.config.list_physical_devices("GPU")))

Num GPUs Available: 1

# 查看是否有GPU
gpu_device_name = tf.test.gpu_device_name()
print(gpu_device_name)

/device:GPU:0

# GPU是否可用
tf.test.is_gpu_available()

True

tf.config.list_physical_devices('GPU')

[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

# 为找出将运算和张量分配到的目标设备
tf.debugging.set_log_device_placement(True)

打开gpu

import os
# 一块gpu参数为0
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

 查看

cfig=tf.compat.v1.ConfigProto(log_device_placement=True)
sess = tf.compat.v1.Session()
sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))

>>> Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: NVIDIA GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1