from torch.distributed importinit_process_group, destroy_process_group 1. 添加 init_process_group(backend='nccl') 2. 设置当前device ddp_local_rank = int(os.environ['LOCAL_RANK']) device = f'cuda:{ddp_local_rank}' t
to(device) model = DDP(model, device_ids=[local_rank], output_device=local_rank) #数据集操作与DDP一致 ###运行 ''' exmaple: 2 node, 8 GPUs per node (16GPUs) 需要在两台机器上分别运行脚本 注意细节:node_rank master 为 0 机器1 >>> python -m torch.distributed.launch \ --nproc_per...
🚀 The feature, motivation and pitch For a symmetry with torch.distributed.get_global_rank it would be useful to add torch.distributed.get_local_rank rather than have the user fish for it in the LOCAL_RANK env var. This feature is almost ...
我们需要在每一台机子(总共m台)上都运行一次torch.distributed.launch 每个torch.distributed.launch会启动n个进程,并给每个进程一个--local_rank=i的参数 这就是之前需要"新增:从外面得到local_rank参数"的原因 这样我们就得到n*m个进程,world_size=n*m 单机模式 多机模式 复习一下,master进程就是rank=0的进程。
os.environ['LOCAL_RANK'] # 得到在当前node中当前GPU进程的rank os.environ['WORLD_SIZE'] # 得到GPU的数量 1. 2. 3. torchrun 可以完成进程分配工作,不再需要使用mp.spawn手动分发进程,只需要设置一个通用的 main() 函数入口,然后用torchrun命令启动脚本即可 ...
type=int, help="node rank for distributed training" ) parser.add_argument( "--dist-url", default="tcp://224.66.41.62:23456", type=str, help="url used to set up distributed training", ) parser.add_argument( "--dist-backend", default="nccl", type=str, help="distributed backend" )然...
device_id = int(os.environ["LOCAL_RANK"]) 启动分布式训练:使用所需的参数实例化,并调用TorchDistributor启动训练。 下面是一个训练代码示例: Python复制 frompyspark.ml.torch.distributorimportTorchDistributordeftrain(learning_rate, use_gpu):importtorchimporttorch.distributedasdistimporttorch.nn....
local_rank` argument to be set, please change it to read from `os.environ['LOCAL_RANK']` instead. See https://pytorch.org/docs/stable/distributed.html#launch-utility for further instructions warnings.warn( WARNING:torch.distributed.run: *** Setting OMP_NUM_THREADS environment variable for eac...
device_id = int(os.environ["LOCAL_RANK"]) 啟動分散式訓練:使用所需的參數具現化TorchDistributor,並呼叫.run(*args)以啟動訓練。 以下是一個訓練程式碼範例: Python frompyspark.ml.torch.distributorimportTorchDistributordeftrain(learning_rate, use_gpu):importtorchimporttor...
torch.distributed.init_process_group( backend=backend, init_method=init_method, world_size=world_size, rank=rank, )exceptExceptionase:raisee torch.cuda.set_device(local_rank) func(cfg) 我们找到了这个函数的run()方法,但是这个方法需要传八个参数,我们从torch.multiprocessing.spawn方法传进来的只有七个...