import tensorflow as tf

tf.test.is_gpu_available()

# # Tensorflow 2.10是最后一个在本地windows上支持GPU的版本。从2.11版本开始，需要在windows WLS2（适用于 Linux 的 Windows 子系统）上安装才能使用GPU。所以要在native-windows上使用GPU，就只能安装2.10.0版本及以下的版本，或者安装老版的tensorflow-gpu.
# # 指定在cpu上运行
# with tf.device('/cpu:0'):
#     cpu_a = tf.random.normal([10000, 1000])
#     cpu_b = tf.random.normal([1000, 2000])
#     cpu_c = tf.matmul(cpu_a, cpu_b)
# print("cpu_a:", cpu_a.device)
# print("cpu_b:", cpu_b.device)
# print("cpu_c:", cpu_c.device)
# # 查看gpu是否可用
# print(tf.config.list_physical_devices('GPU'))
# # 指定在gpu上运行
# with tf.device('/gpu:0'):
#     gpu_a = tf.random.normal([10000, 1000])
#     gpu_b = tf.random.normal([1000, 2000])
#     gpu_c = tf.matmul(gpu_a, gpu_b)
# print("gpu_a:", gpu_a.device)
# print("gpu_b:", gpu_b.device)
# print("gpu_c:", gpu_c.device)
#
# import tensorflow as tf
# import timeit
#
#
# def cpu_run():
#     with tf.device('/cpu:0'):
#         cpu_a = tf.random.normal([10000, 1000])
#         cpu_b = tf.random.normal([1000, 2000])
#         c = tf.matmul(cpu_a, cpu_b)
#     return c
#
#
# def gpu_run():
#     with tf.device('/gpu:0'):
#         gpu_a = tf.random.normal([10000, 1000])
#         gpu_b = tf.random.normal([1000, 2000])
#         c = tf.matmul(gpu_a, gpu_b)
#     return c
#
#
# cpu_time = timeit.timeit(cpu_run, number=10)
# gpu_time = timeit.timeit(gpu_run, number=10)
# print("cpu:", cpu_time, "  gpu:", gpu_time)
# # 检测 tensorflow 能使用的设备情况
# from tensorflow.python.client import device_lib
#
# # os.environ["CUDA_VISIBLE_DEVICES"] = "1"  # 这个可以指定使用哪个设备
# print(device_lib.list_local_devices())