TensorFlow使用GPU测试

查看当前TensorFlow版本查看当前主机上运行的设备查看GPU是否可用指定在CPU/GPU上运行比较在CPU和GPU上的运行时间import tensorflow as tfprint(tf.__version__)查看当前主机上运行的设备import tensorflow as tfgpus = tf.config.experimental.list_physical_devices(dev

热衷做分母

13355人浏览 · 2022-01-27 10:57:02

热衷做分母 · 2022-01-27 10:57:02 发布

查看当前TensorFlow版本

import tensorflow as tf
print(tf.__version__)

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查看当前主机上运行的设备

import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
cpus = tf.config.experimental.list_physical_devices(device_type='CPU')
print(gpus)
print(cpus)

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查看GPU是否可用，指定在CPU/GPU上运行

import tensorflow as tf

# 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)

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比较在CPU和GPU上的运行时间

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)