深度学习训练时GPU相关配置和问题

1. 查看电脑中可以使用的GPU

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

输出： 

 2. 选择使用GPU

import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"  ##表示使用GPU编号为0的GPU进行计算
from tensorflow.compat.v1 import ConfigProto# tf 2.x的写法
config =ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction=0.6    # 设置最大使用显存60%

3. 清理GPU内存

出现下面问题时：

2022-12-08 16:09:39.813281: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 128.0KiB (rounded to 131072)requested by op Fill
If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. 
Current allocation summary follows.
Current allocation summary follows.
...这里省略...
ResourceExhaustedError: {{function_node __wrapped__Fill_device_/job:localhost/replica:0/task:0/device:GPU:0}} OOM when allocating tensor with shape[256,128] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Fill]

当一个GPU内存满了，再去加载模型训练时会出错，这个时候可以先清理GPU内存后再去训练

可以在代码中直接清除（ 推荐使用的方式）：

from numba import cuda
device = cuda.get_current_device()
device.reset()

也可以将PID进程关掉：kill -9 1891022 

在命令行中输入：nvidia-smi  查看GPU Memory Usage