Keras-GPUID和显存占用设定步骤

#! -- coding: utf-8 --*--
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

#! -- coding: utf-8 --*--
import tensorflow as tf
import keras.backend.tensorflow_backend as ktf

# GPU 显存自动调用
cOnfig= tf.ConfigProto()
config.gpu_options.allow_growth=True
session = tf.Session(cOnfig=config)
ktf.set_session(session)

#! -- coding: utf-8 --*--
import tensorflow as tf
import keras.backend.tensorflow_backend as ktf

# 设定 GPU 显存占用比例为 0.3
cOnfig= tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
session = tf.Session(cOnfig=config)
ktf.set_session(session )

#! -- coding: utf-8 --*--
import os
import tensorflow as tf
import keras.backend.tensorflow_backend as ktf

# GPU 显存自动分配
cOnfig= tf.ConfigProto()
config.gpu_options.allow_growth=True
#config.gpu_options.per_process_gpu_memory_fraction = 0.3
session = tf.Session(cOnfig=config)
ktf.set_session(session)

# 指定GPUID, 第一块GPU可用
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

#! -- coding: utf-8 --*--

# 将内存中的数据分批(batch_size)送到显存中进行运算
def generate_arrays_from_memory(data_train, labels_train, batch_size):
  x = data_train
  y=labels_train
  ylen=len(y)
  loopcount=ylen // batch_size
  while True:
    i = np.random.randint(0,loopcount)
    yield x[i*batch_size:(i+1)*batch_size],y[i*batch_size:(i+1)*batch_size]

# load数据到内存
data_train=np.loadtxt("./data_train.txt")
labels_train=np.loadtxt('./labels_train.txt')
data_val=np.loadtxt('./data_val.txt')
labels_val=np.loadtxt('./labels_val.txt')

hist=model.fit_generator(generate_arrays_from_memory(data_train,
                           labels_train,
                           batch_size),
             steps_per_epoch=int(train_size/bs),
             epochs=ne,
             validation_data=(data_val,labels_val),
             callbacks=callbacks )

#! -- coding: utf-8 --*--

def process_line(line): 
  tmp = [int(val) for val in line.strip().split(',')] 
  x = np.array(tmp[:-1]) 
  y = np.array(tmp[-1:]) 
  return x,y 

def generate_arrays_from_file(path,batch_size): 
  while 1: 
    f = open(path) 
    cnt = 0 
    X =[] 
    Y =[] 
    for line in f: 
      # create Numpy arrays of input data 
      # and labels, from each line in the file 
      x, y = process_line(line) 
      X.append(x) 
      Y.append(y) 
      cnt += 1 
      if cnt==batch_size: 
        cnt = 0 
        yield (np.array(X), np.array(Y)) 
        X = [] 
        Y = [] 
  f.close() 

import os
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
from numba import cuda

cOnfig= tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.1 # 设定显存的利用率
set_session(tf.Session(cOnfig=config))

K.clear_session() # 方法一：如果不关闭，则会一直占用显存

cuda.select_device(1) # 方法二：选择GPU1
cuda.close() #关闭选择的GPU