+ world_size: Total number of processes + """+ os.environ["MASTER_ADDR"] ="localhost"+ os.environ["MASTER_PORT"] ="12355"+ init_process_group(backend="nccl", rank=rank, world_size=world_size) + torch.cuda.set_device(rank) 第三步,创建 DDP 模型,这里的gpu_id就是rank。 Copy - se...
world-size = 16 group default=1 rank = 0~15 local rank : node1-0~7 ; node2-0~7 nnodes : 有多少台机器 node-rank : 当前是哪一台机器 nproc_per_node:每台机器有多少进程 PipeDream abstract Gpipe流水线其存在两个问题:硬件利用率低,内存占用大.worker 之间只能同时处理一个 minibatch,系统中...
world_size):dist.init_process_group("nccl",rank=rank,world_size=world_size)model=nn.Linear(10,10).cuda(rank)model=nn.parallel.DistributedDataParallel(model,device_ids=[rank])optimizer=torch.optim.SGD(model.parameters(),lr=0.01)forepochinrange(10):inputs=torch.randn(32,10).cuda(...
ds_config[ 'train_micro_batch_size_per_gpu'] = args.per_device_train_batch_size ds_config[ 'train_batch_size'] = args.per_device_train_batch_size * torch.distributed.get_world_size( ) * args.gradient_accumulation_steps # If passed along, set the training seed now. # 设置随机种子以...
get_world_size self.global_rank = dist.get_rank() # 3. ZERO 最核心的部分:建立 optimizer # self.optimizer = self._configure_zero_optimizer(optimizer=None) if zero_stage <= ZeroStageEnum.gradients: # zero1 or zero2 optimizer = DeepSpeedZeroOptimizer( optimizer , dp_process_group=self.data...
get started 这里上来就是 world_size, local_rank 没有解释。文档里似乎急于说明用起来“简单”,所以都不肯正经写几句怎么安装,怎么跑一个多机小例子。 又比如这里有一个完整例子 DeepSpeed Integration ,里面注释行数不少,但 T0 什么的似乎是在聊闲天。 有一些句子需要译回中文才知道什么意思。 when there is...
world_size if not USE_TORCH_DDP: timers('allreduce').start() model.allreduce_params(reduce_after=False, fp32_allreduce=args.fp32_allreduce) timers('allreduce').stop() (B) 我们也跳过更新主节点梯度,因为DeepSpeed在内部解决了这个问题。 代码语言:javascript 复制 # Update master gradients. if...
默认等于world_size(GPU个数) self.rank = rank # 当前属于哪个进程/哪块GPU self.epoch = 0 self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) # 每个进程的样本个数 self.total_size = self.num_samples * self.num_replicas # 数据集总样本的个数 self.shuffle ...
Setting ds_accelerator to cuda (auto detect) using world size: 1 and model-parallel size: 1 > using dynamic loss scaling > initializing model parallel with size 1 Pretrain GPT2 model arguments: pretrained_bert ... False attention_dropout ... 0.1 num_attention_heads ... 16 hidden_size .....
Describe the bug When using DeepSpeed 0.10.0 (or version > 0.8.2) with Ray 2.5.1 I get the following error when trying to run a job on 3 Reay workers: AssertionError: Check batch related parameters. train_batch_size is not equal to micro...