TensorFlow和keras中GPU使用的设置操作

CUDA_VISIBLE_DEVICES=1 python train.py

import os
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
 
# 指定第一块GPU可用 
os.environ["CUDA_VISIBLE_DEVICES"] = "0" #指定GPU的第二种方法
 
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC' #A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc.
config.gpu_options.per_process_gpu_memory_fraction = 0.3 #定量
config.gpu_options.allow_growth = True  #按需
set_session(tf.Session(config=config)) 

#指定GPU
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
#设置GPU定量分配
config = tf.ConfigProto() 
config.gpu_options.per_process_gpu_memory_fraction = 0.9 # 占用GPU90%的显存 
session = tf.Session(config=config)
 
#设置GPU按需分配
config = tf.ConfigProto() 
config.gpu_options.allow_growth = True 
session = tf.Session(config=config)

import tensorflow as tf  
sess = tf.Session(config=tf.ConfigProto(device_count={'gpu':0}))

import tensorflow as tf
import keras.backend.tensorflow_backend as KTF 
KTF.set_session(tf.Session(config=tf.ConfigProto(device_count={'gpu':0})))

CUDA_VISIBLE_DEVICES="0" python3 train.py