pytorch-multi-gpu-training data_parallel方法 代码链接 两处改动 改动一:py文件最开始导入模块处 os.environ["CUDA_VISIBLE_DEVICES"]="2,3"# 必须在`importtorch`语句之前设置才能生效 改动二:模型实例化处 model= Net()model= model.to(device)model= nn.DataParallel(model)# 就在这里wrap一下,模型就会使用所有的GPU 参考资料:[1]
对于单机多卡的情形,WORLD_SIZE表示总的GPU数量,RANK就是哪块GPU,此时LOCAL_RANK(当前机器的第几块GPU) 跟我们的RANK是一样的,因为是单机多卡。 多GPU启动指令:python -m torch.distributed.launch --nproc_per_node=8 --use_env train_multi_gpu_using_launch.py,指令,nproc_per_node参数为使用GPU数量 。我...
type=int,default=-1)args=parser.parse_args()# 每个进程根据自己的local_rank设置应该使用的GPUtorch.cuda.set_device(args.local_rank)device=torch.device('cuda',args.local_rank)# 初始化分布式环境,主要用来帮助进程间通信torch.distributed.init_process...
nccl backend is currently the fastest and highly recommended backend to be used with Multi-Process Single-GPU distributed training and this applies to both single-node and multi-node distributed training 好了,来说说具体的使用方法(下面展示一个node也就是一个主机的情况)为: 代码语言:javascript 代码运...
nccl backend is currently the fastest and highly recommended backend to be used with Multi-Process Single-GPU distributed training and this applies to both single-node and multi-node distributed training 好了,来说说具体的使用方法(下面展示一个node也就是一个主机的情况)为: ...
单机多卡：代表某一块GPU的编号 参考文献 GitHub - jia-zhuang/pytorch-multi-gpu-training: 整理 ...
This is the highly recommended way to useDistributedDataParallel, with multiple processes, each of which operates on a single GPU. This is currently the fastest approach to do data parallel training using PyTorch and applies to both single-node(multi-GPU) and multi-node data parallel training. ...
{}, "distributed_type": "MULTI_GPU", "downcast_bf16": false, "fsdp_config": {}, "machine_rank": 0, "main_process_ip": null, "main_process_port": null, "main_training_function": "main", "mixed_precision": "no", "num_machines": 1, "num_processes": 2, "use_cpu": false ...
yaml { "compute_environment": "LOCAL_MACHINE", "deepspeed_config": {}, "distributed_type": "MULTI_GPU", "downcast_bf16": false, "fsdp_config": {}, "machine_rank": 0, "main_process_ip": null, "main_process_port": null, "main_training_function": "main", "mixed_precision": "...
您需要处理的第一个也是最复杂的新事情是进程初始化。普通的PyTorch训练脚本在单个进程中执行其代码的单一副本。使用数据并行模型，情况就更加复杂了:现在训练脚本的同步副本与训练集群中的gpu数量一样多，每个gpu运行在不同的进程中。考虑以下最小的例子:# multi_init.pyimport torchimport torch.distributed as dist...