转到命令行并运行Python
以下示例列出了主机上可见GPU的数量。Docs

import tensorflow as tf
devices = tf.config.list_physical_devices('GPU')
print(len(devices))

适用于CUDA Docs

import tensorflow as tf
tf.test.is_built_with_cuda()

返回构建TensorFlow时是否支持CUDA（GPU）。Docs
您也可以使用以下函数进行检查，但已弃用**

import tensorflow as tf

tf.test.is_gpu_available()

如果GPU可用，两者均返回True

Tensorflow 2 GPU检查可以在这里找到，

已弃用，https://www.tensorflow.org/api_docs/python/tf/test/is_gpu_available

https://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices
检查Tensorflow构建时是否支持CUDA（GPU），
https://www.tensorflow.org/api_docs/python/tf/test/is_built_with_cuda
Tensorflow GPU指南，
https://www.tensorflow.org/guide/gpu

代码

import tensorflow as tf

print(tf.config.list_physical_devices('GPU'))
# [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

print(tf.test.is_built_with_cuda)
# <function is_built_with_cuda at 0x7f4f5730fbf8>

print(tf.test.gpu_device_name())
# /device:GPU:0

print(tf.config.get_visible_devices())
# [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

print(tf.config.list_physical_devices('GPU'))

[物理设备（名称=“/物理设备：GPU：0”，设备类型=“GPU”）]