Distributed data parallel training in Pytorchyangkky.github.io 后续等我把这些并行计算的内容捋清楚了,会再自己写一份更详细的tutorial~ 注意:需要在每一个进程设置相同的随机种子,以便所有模型权重都初始化为相同的值。 1. 动机 加速神经网络训练最简单的办法就是上GPU,如果一块GPU还是不够,
本系列开始整理Distributed Training的相关知识,契机其实主要是在23年初ChatGPT刚开始火热起来,深刻觉得对于CVer而言,相比于NLP的领域技术,NLP的工程技术反而是更大的鸿沟,NLPer简直是降维打击CVer,故开始学习相关的Nvidia和MicroSoft等论文,以及Megatron, DeepSpeed等代码。本文是Data Parallel的第一篇,主要介绍DP, DDP,...
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models ZeRO-Offload: Democratizing Billion-Scale Model Training ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters Fully Sharded Data Parallel: faster AI training with fewer GPUs 图解大模型训练之:...
与DataParallel不同的是,Distributed Data Parallel会开设多个进程而非线程,进程数 =GPU数,每个进程都可以独立进行训练,也就是说代码的所有部分都会被每个进程同步调用,如果你某个地方print张量,你会发现device的差异 sampler会将数据按照进程数切分, 「确保不同进程的数据不同」 每个进程独立进行前向训练 每个进程利用R...
import moxing as mox mox.file.copy_parallel(src_path, dst_path) logging.info(f"end copy data from {src_path} to {dst_path}") def main(): seed = datetime.datetime.now().year setup_seed(seed) parser = argparse.ArgumentParser(description='Pytorch distribute training', formatter_class=argpar...
# import torch.distributed as distif rank == 0:downloading_dataset() downloading_model_weights()dist.barrier()print( f"Rank {rank + 1}/{world_size} training process passed data download barrier.\n")此代码示例中的dist.barrier将阻塞调用，直到完成主进程（rank == 0）downloadingdataset和do...
#importtorch.distributedasdistifrank==0:downloading_dataset()downloading_model_weights()dist.barrier()print(f"Rank {rank + 1}/{world_size} training process passed data download barrier.\n") 此代码示例中的dist.barrier将阻塞调用,直到完成主进程(rank == 0)downloading_dataset和downloading_model_weight...
Building the training function# Notice the only change is in therun(). Instead of usingxmp.spawn, we directly call thetrain_fn, as the process spawning is taken care of bytorchrun. deftrain_fn():device=xm.xla_device()rank=xm.get_ordinal()# Create the model and ...
) optimizer.step() if rank == 0: stop_training = time() if (i + 1) % 10 == 0 and rank == 0: print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Time data load: {:.3f}ms, Time training: {:.3f}ms'.format(epoch + 1, args.epochs, i ...
本文连接(要梯子):https://towardsdatascience.com/distributed-model-training-in-pytorch-using-distributeddataparallel-d3d3864dc2a7 作者:Aleksey Bilogur 先进的深度学习模型参数正以指数级速度增长:去年的GPT-2有大约7.5亿个参数,今年的GPT-3有1750亿个参数。虽然GPT是一个比较极端的例子但是各种SOTA模型正在推动...