Pytorch设备选择,多GPU训练

To函数

功能：转换数据类型设备

1.tensor.to() 2.mouble.to()

例子：

torch.cuda

多GPU分发并行机制

把数据等分，给不同的GPU运行

torch.nn.DataParallel

# ============================ 手动选择gpu
gpu_list = [2,3]                                                     
gpu_list_str = ,.join(map(str, gpu_list))                          
os.environ.setdefault("CUDA_VISIBLE_DEVICES", gpu_list_str)          
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# ============================ 依内存情况自动选择主gpu
def get_gpu_memory():                                                             
    import platform                                                               
    if Windows != platform.system():                                            
        import os                                                                 
        os.system(nvidia-smi -q -d Memory | grep -A4 GPU | grep Free > tmp.txt) 
        memory_gpu = [int(x.split()[2]) for x in open(tmp.txt, r).readlines()]
        os.system(rm tmp.txt)                                                   
    else:                                                                         
        memory_gpu = False                                                        
        print("显存计算功能暂不支持windows操作系统")                                            
    return memory_gpu                                                             
                                                                                  
                                                                                  
gpu_memory = get_gpu_memory()                                                     
if not gpu_memory:                                                                
    print("
gpu free memory: {}".format(gpu_memory))                             
    gpu_list = np.argsort(gpu_memory)[::-1]                                       
                                                                                  
    gpu_list_str = ,.join(map(str, gpu_list))                                   
    os.environ.setdefault("CUDA_VISIBLE_DEVICES", gpu_list_str)                   
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

常见报错

1.原因：gpu训练出来的model，用map_location映射到cpu上

path_state_dict = "./model_in_gpu_0.pkl" 
state_dict_load = torch.load(path_state_dict, map_location="cpu")
print("state_dict_load:
{}".format(state_dict_load))

2.多GPU（DataParallel）训练出来的模型，需要更改一下层的名字，因为在前面多了一个moudle字段

from collections import OrderedDict                   
new_state_dict = OrderedDict()                        
for k, v in state_dict_load.items():                  
    namekey = k[7:] if k.startswith(module.) else k 
    new_state_dict[namekey] = v                       
print("new_state_dict:
{}".format(new_state_dict))   
                                                      
net.load_state_dict(new_state_dict)

让网络加载new_state_dict即可