关键词

运行tensorflow python程序,限制对GPU和CPU的占用操作

要限制TensorFlow Python程序对GPU和CPU的占用操作,可以使用TensorFlow的tf.config模块中的experimental API。以下是详细步骤:

步骤一:导入依赖库

首先需要导入TensorFlow和其他依赖库:

import tensorflow as tf
import os

步骤二:设置GPU的内存增长

可以使用以下代码设置GPU的内存增长,以便能够更好地控制TensorFlow程序对GPU的占用:

gpus = tf.config.list_physical_devices('GPU')
if gpus:
  try:
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
  except RuntimeError as e:
    print(e)

步骤三:设置CPU和GPU的可见性

可以使用以下代码设置CPU和GPU的可见性,以便能够更好地控制TensorFlow程序对CPU和GPU的占用:

os.environ['CUDA_VISIBLE_DEVICES'] = '0'  # 只使用第一张GPU

步骤四:限制CPU和GPU占用

可以使用以下代码限制TensorFlow程序对CPU和GPU的占用(本例中,CPU和GPU的占用被限制在20%以下):

# 设置CPU占用限制
config = tf.compat.v1.ConfigProto()
config_proto.InterOpParallelismThreads = 1
config_proto.IntraOpParallelismThreads = 1
os.environ["OMP_NUM_THREADS"] = str(1)

# 设置GPU占用限制
gpu_nums = str(len(tf.config.experimental.list_physical_devices('GPU')))
if gpu_nums == 1:
    config.gpu_options.per_process_gpu_memory_fraction = 0.2
    config.gpu_options.allow_growth = True
else:
    # 多卡
    config.gpu_options.allow_growth = True
    config.gpu_options.per_process_gpu_memory_fraction = 1. / gpu_nums * 0.2

session = tf.compat.v1.Session(config=config)

至此,限制TensorFlow Python程序对GPU和CPU的占用操作的攻略已经完成。

例如,下面是一个使用以上攻略的示例:

import tensorflow as tf
import os

# 步骤二:设置GPU的内存增长
gpus = tf.config.list_physical_devices('GPU')
if gpus:
  try:
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
  except RuntimeError as e:
    print(e)

# 步骤三:设置CPU和GPU的可见性
os.environ['CUDA_VISIBLE_DEVICES'] = '0'  # 只使用第一张GPU

# 步骤四:限制CPU和GPU占用
config = tf.compat.v1.ConfigProto()
config_proto.InterOpParallelismThreads = 1
config_proto.IntraOpParallelismThreads = 1
os.environ["OMP_NUM_THREADS"] = str(1)

gpu_nums = str(len(tf.config.experimental.list_physical_devices('GPU')))
if gpu_nums == 1:
    config.gpu_options.per_process_gpu_memory_fraction = 0.2
    config.gpu_options.allow_growth = True
else:
    config.gpu_options.allow_growth = True
    config.gpu_options.per_process_gpu_memory_fraction = 1. / gpu_nums * 0.2

session = tf.compat.v1.Session(config=config)

# 示例程序
x = tf.constant([1.0, 2.0, 3.0, 4.0])
y = tf.constant([4.0, 3.0, 2.0, 1.0])
z = tf.multiply(x, y)

with tf.compat.v1.Session() as sess:
    output = sess.run(z)
    print(output)

另外一个使用以上攻略的示例:

import tensorflow as tf
import os

# 步骤二:设置GPU的内存增长
gpus = tf.config.list_physical_devices('GPU')
if gpus:
  try:
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
  except RuntimeError as e:
    print(e)

# 步骤三:设置CPU和GPU的可见性
os.environ['CUDA_VISIBLE_DEVICES'] = '0'  # 只使用第一张GPU

# 步骤四:限制CPU和GPU占用
config = tf.compat.v1.ConfigProto()
config_proto.InterOpParallelismThreads = 1
config_proto.IntraOpParallelismThreads = 1
os.environ["OMP_NUM_THREADS"] = str(1)

gpu_nums = str(len(tf.config.experimental.list_physical_devices('GPU')))
if gpu_nums == 1:
    config.gpu_options.per_process_gpu_memory_fraction = 0.2
    config.gpu_options.allow_growth = True
else:
    config.gpu_options.allow_growth = True
    config.gpu_options.per_process_gpu_memory_fraction = 1. / gpu_nums * 0.2

session = tf.compat.v1.Session(config=config)

# 示例程序
a = tf.Variable(tf.constant(5.0, dtype=tf.float32), dtype=tf.float32, trainable=True)
b = tf.Variable(tf.constant(3.0, dtype=tf.float32), dtype=tf.float32, trainable=True)
c = tf.add(a, b)

init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:
    sess.run(init)
    output = sess.run(c)
    print(output)

本文链接:http://task.lmcjl.com/news/14262.html

展开阅读全文