print(f"Process {rank}, Epoch {epoch}, Loss: {loss.item()}") dist.destroy_process_group() 修改main函数增加world_size参数并调整进程初始化以传递world_size。 def main(): num_processes = 4 world_size = num_processes data = torch.randn(100, 10) target = torch.randn(100, 1) mp.spawn(...
print(f"Process {rank}, Epoch {epoch}, Loss: {loss.item()}") dist.destroy_process_group() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 修改main函数增加world_size参数并调整进程初始化以传递world_size。 def main(): num_processes = 4 world_size =...
DDP使得在分布式环境下训练模型变得更加容易,开发者无需手动编写大部分分布式训练的代码。 4. `from torch.distributed import init_process_group, destroy_process_group`: 这些函数用于初始化和销毁分布式进程组。在使用分布式训练时,需要使用`init_process_group`函数来初始化分布式环境,包括指定通信后端(如NCCL、Gloo...
最后,我再来总结一下单卡训练转换成并行训练的修改处: 程序开始时执行dist.init_process_group('nccl'),结束时执行dist.destroy_process_group()。 用torchrun --nproc_per_node=GPU_COUNT main.py运行脚本。 进程初始化后用rank = dist.get_rank()获取当前的GPU ID,把模型和数据都放到这个GPU上。 封装一下...
🐛 Describe the bug I seem to have found an issue that can occur when destroying the default process group and attempting to reinitialize it immediately after. This can lead to a race condition where not all workers have finished destroyi...
.parameters(), lr=0.01)criterion = nn.MSELoss()forepochinrange(epochs):optimizer.zero_grad()output = ddp_model(data.to(rank))loss = criterion(output, target.to(rank))loss.backward()optimizer.step()print(f"Process{rank}, Epoch{epoch}, Loss:{loss.item...
for epoch in range(10):for images, labels in train_loader:images = images.to(rank)labels = labels.to(rank)optimizer.zero_grad()output = ddp_model(images)loss = loss_fn(output, labels)loss.backward()optimizer.step() 清理和关闭进程组dist.destroyprocessgroup()if __name...
init_process_group(backend="nccl") train() dist.destroy_process_group() if __name__ == "__main__": run() 本例启动的是一个2机4卡的训练任务,逻辑视图如下所示 本例中使用torchrun来执行多机多卡的分布式训练任务(注:torch.distributed.launch 已经被pytorch淘汰了,尽量不要再使用)。torchrun在...
Create a new process group if group of TorchDistributedTrial is None. optuna/optuna#4268 wconstabadded a commit that references this issue on Mar 21, 2024 [C10D] Document destroy_process_group usage... 5aab4f3 wconstabmentioned this on Mar 21, 2024 [C10D] Document destroy_process_group...
dist.destroy_process_group() 请注意,上面的代码只是一个非常基础的示例,用于说明如何使用torch.distributed进行分布式训练。在实际应用中,您可能需要根据您的模型和数据集进行更复杂的模型拆分和数据加载。此外,您还需要处理多进程启动、错误处理和日志记录等问题。