415 lines
16 KiB
YAML
415 lines
16 KiB
YAML
apiVersion: v1
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kind: ConfigMap
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metadata:
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name: nccl-scripts
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data:
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run.sh: |
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#!/usr/bin/env bash
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set -ex
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# 현재 실행 시작 시 타임스탬프 저장
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START_TIME=$(date +%s)
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# ----- RDMA/IB 강제 -----
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export NCCL_DEBUG=INFO
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export NCCL_DEBUG_SUBSYS=INIT,NET,IB
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#export NCCL_NET=IB
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#export NCCL_IB_DISABLE=0
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export NCCL_SOCKET_IFNAME="net"
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#export NCCL_IB_HCA="=mlx5_6,mlx5_7,mlx5_8,mlx5_9,mlx5_0"
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export NCCL_IB_HCA="=error"
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#export NCCL_IB_HCA="mlx5_0,mlx5_1,mlx5_2,mlx5_8,mlx5_9,mlx5_5,mlx5_6,mlx5_7"
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#export NCCL_NET_GDR_LEVEL=2
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# (RoCE 환경에 따라 필요 시) export NCCL_IB_GID_INDEX=3
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# 프로세스별 로그 파일 경로 (노드/프로세스마다 별도 파일)
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export NCCL_DEBUG_FILE="/tmp/nccl-%h-%p.log"
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#export CUDA_VISIBLE_DEVICES="1,2,3,4,5,6,7"
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# ----- 학습 실행 (각 파드가 1개 프로세스씩 구동) -----
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#echo "[RUN] RANK=$RANK on $(hostname)"
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torchrun \
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--nnodes=2 \
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--nproc_per_node=8 \
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--rdzv_backend=c10d \
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--node_rank=0 \
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--rdzv_endpoint=nccl-0.nccl:23456 \
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/workspace/train_nccl.py 2>&1 | tee /tmp/nccl-aggregate.log
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# ----- RDMA 사용 여부 집계 -----
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# 새로 생성된 로그 목록 가져오기: START_TIME 이후 생성된 파일만
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#new_logs=$(find /tmp -name "nccl-nccl-*.log" -type f -newermt "@${START_TIME}")
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#
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#total=0; ok=0
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#for f in $new_logs; do
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# [[ -f "$f" ]] || continue
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# total=$((total+1))
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# if rdma_line=$(grep -E "Channel .*GDRDMA" "$f"); then
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# echo "[OK] RDMA: $rdma_line"
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# ok=$((ok+1))
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# fi
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#done
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#echo "[RDMA-CHECK] used IB on $ok / $total local ranks (processes) in $(hostname)"
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#
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## 모든 랭크가 IB를 썼는지(파드 단위) 판단: 로컬 프로세스 수와 비교
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#if [[ "$ok" -eq 8 ]]; then
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# echo "[RDMA-CHECK] ✅ RDMA path OK for all local GPUs"
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# exit 0
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#else
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# echo "[RDMA-CHECK] ❌ RDMA path NOT used by all local GPUs"
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# echo "---- NCCL NET/IB related lines ----"
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# grep -h "NET/" /tmp/nccl-*.log || true
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# exit 1
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#fi
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train_nccl.py: |
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import os
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import time
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import torch
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import argparse
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import torch.distributed as dist
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import torch.multiprocessing as mp
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from torch.optim.lr_scheduler import StepLR
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# for dataset
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from torchvision.datasets.cifar import CIFAR10
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import torchvision.transforms as tfs
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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# for model
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from torchvision.models import vgg11
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from torch.nn.parallel import DistributedDataParallel as DDP
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import numpy as np
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import random
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import datetime
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def set_random_seeds(random_seed=0):
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torch.manual_seed(random_seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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np.random.seed(random_seed)
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random.seed(random_seed)
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def get_args_parser():
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument('--lr', type=float, default=0.01)
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parser.add_argument('--epoch', type=int, default=90)
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parser.add_argument('--batch_size', type=int, default=1200)
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parser.add_argument('--global_rank', type=int, default=0)
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parser.add_argument('--vis_step', type=int, default=10)
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parser.add_argument('--num_workers', type=int, default=24)
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parser.add_argument("--local_rank", type=int,
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help="Local rank. Necessary for using the torch.distributed.launch utility.")
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parser.add_argument('--world_size', type=int, default=0)
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parser.add_argument('--port', type=int, default=2022)
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parser.add_argument('--root', type=str, default='data')
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parser.add_argument('--start_epoch', type=int, default=0)
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parser.add_argument('--save_path', type=str, default='./save')
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parser.add_argument('--save_file_name', type=str, default='vgg_cifar')
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return parser
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def main(opts):
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# 1. set random seeds
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set_random_seeds(random_seed=0)
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# 2. initialization
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init_for_distributed(opts)
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# 3. visdom
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vis = None
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# 4. data set
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transform_train = tfs.Compose([
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tfs.Resize(256),
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tfs.RandomCrop(224),
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tfs.RandomHorizontalFlip(),
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tfs.ToTensor(),
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tfs.Normalize(mean=(0.4914, 0.4822, 0.4465),
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std=(0.2023, 0.1994, 0.2010)),
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])
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transform_test = tfs.Compose([
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tfs.Resize(256),
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tfs.CenterCrop(224),
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tfs.ToTensor(),
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tfs.Normalize(mean=(0.4914, 0.4822, 0.4465),
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std=(0.2023, 0.1994, 0.2010)),
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])
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train_set = CIFAR10(root=opts.root,
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train=True,
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transform=transform_train,
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download=True)
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test_set = CIFAR10(root=opts.root,
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train=False,
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transform=transform_test,
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download=True)
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train_sampler = DistributedSampler(dataset=train_set, shuffle=True)
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test_sampler = DistributedSampler(dataset=test_set, shuffle=False)
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train_loader = DataLoader(dataset=train_set,
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batch_size=int(opts.batch_size / opts.world_size),
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shuffle=False,
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num_workers=int(opts.num_workers / opts.world_size),
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sampler=train_sampler,
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pin_memory=True)
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test_loader = DataLoader(dataset=test_set,
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batch_size=int(opts.batch_size / opts.world_size),
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shuffle=False,
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num_workers=int(opts.num_workers / opts.world_size),
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sampler=test_sampler,
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pin_memory=True)
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# 5. model
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model = vgg11(pretrained=False)
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model = model.cuda(opts.local_rank)
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model = DDP(module=model,
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device_ids=[opts.local_rank])
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# 6. criterion
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criterion = torch.nn.CrossEntropyLoss().to(opts.local_rank)
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# 7. optimizer
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optimizer = torch.optim.SGD(params=model.parameters(),
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lr=0.01,
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weight_decay=0.0005,
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momentum=0.9)
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# 8. scheduler
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scheduler = StepLR(optimizer=optimizer,
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step_size=30,
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gamma=0.1)
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if opts.start_epoch != 0:
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checkpoint = torch.load(os.path.join(opts.save_path, opts.save_file_name) + '.{}.pth.tar'
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.format(opts.start_epoch - 1),
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map_location=torch.device('cuda:{}'.format(opts.local_rank)))
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model.load_state_dict(checkpoint['model_state_dict']) # load model state dict
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optimizer.load_state_dict(checkpoint['optimizer_state_dict']) # load optim state dict
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scheduler.load_state_dict(checkpoint['scheduler_state_dict']) # load sched state dict
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if opts.global_rank == 0:
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print('\nLoaded checkpoint from epoch %d.\n' % (int(opts.start_epoch) - 1))
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for epoch in range(opts.start_epoch, opts.epoch):
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# 9. train
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tic = time.time()
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model.train()
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train_sampler.set_epoch(epoch)
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for i, (images, labels) in enumerate(train_loader):
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images = images.to(opts.local_rank)
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labels = labels.to(opts.local_rank)
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outputs = model(images)
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# ----------- update -----------
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optimizer.zero_grad()
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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# get lr
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for param_group in optimizer.param_groups:
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lr = param_group['lr']
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# time
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toc = time.time()
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# visualization
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if (i % opts.vis_step == 0 or i == len(train_loader) - 1):
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print('GPU[{0}] Epoch [{1}/{2}], Iter [{3}/{4}], Loss: {5:.4f}, LR: {6:.5f}, Time: {7:.2f}'.format(opts.global_rank,
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epoch,
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opts.epoch,
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i,
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len(train_loader),
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loss.item(),
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lr,
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toc - tic))
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if vis is not None and opts.local_rank == 0:
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vis.line(X=torch.ones((1, 1)) * i + epoch * len(train_loader),
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Y=torch.Tensor([loss]).unsqueeze(0),
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update='append',
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win='loss',
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opts=dict(x_label='step',
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y_label='loss',
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title='loss',
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legend=['total_loss']))
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# save pth file
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if opts.local_rank == 0:
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if not os.path.exists(opts.save_path):
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os.mkdir(opts.save_path)
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checkpoint = {'epoch': epoch,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'scheduler_state_dict': scheduler.state_dict()}
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torch.save(checkpoint, os.path.join(opts.save_path, opts.save_file_name + '.{}.pth.tar'.format(epoch)))
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print("save pth.tar {} epoch!".format(epoch))
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# 10. test
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model.eval()
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val_avg_loss = 0
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correct_top1 = 0
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correct_top5 = 0
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total = 0
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with torch.no_grad():
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for i, (images, labels) in enumerate(test_loader):
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images = images.to(opts.local_rank) # [100, 3, 224, 224]
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labels = labels.to(opts.local_rank) # [100]
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outputs = model(images)
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loss = criterion(outputs, labels)
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val_avg_loss += loss.item()
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# ------------------------------------------------------------------------------
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# rank 1
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_, pred = torch.max(outputs, 1)
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total += labels.size(0)
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correct_top1 += (pred == labels).sum().item()
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# ------------------------------------------------------------------------------
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# rank 5
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_, rank5 = outputs.topk(5, 1, True, True)
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rank5 = rank5.t()
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correct5 = rank5.eq(labels.view(1, -1).expand_as(rank5))
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# ------------------------------------------------------------------------------
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for k in range(5): # 0, 1, 2, 3, 4, 5
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correct_k = correct5[:k+1].reshape(-1).float().sum(0, keepdim=True)
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correct_top5 += correct_k.item()
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accuracy_top1 = correct_top1 / total
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accuracy_top5 = correct_top5 / total
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val_avg_loss = val_avg_loss / len(test_loader) # make mean loss
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if vis is not None:
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vis.line(X=torch.ones((1, 3)) * epoch,
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Y=torch.Tensor([accuracy_top1, accuracy_top5, val_avg_loss]).unsqueeze(0),
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update='append',
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win='test_loss_acc',
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opts=dict(x_label='epoch',
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y_label='test_loss and acc',
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title='test_loss and accuracy',
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legend=['accuracy_top1', 'accuracy_top5', 'avg_loss']))
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print("top-1 percentage : {0:0.3f}%".format(correct_top1 / total * 100))
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print("top-5 percentage : {0:0.3f}%".format(correct_top5 / total * 100))
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scheduler.step()
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return 0
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def init_for_distributed(opts):
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# 1. setting for distributed training
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opts.global_rank = int(os.environ['RANK'])
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opts.local_rank = int(os.environ['LOCAL_RANK'])
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opts.world_size = int(os.environ['WORLD_SIZE'])
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torch.cuda.set_device(opts.local_rank)
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if opts.global_rank is not None and opts.local_rank is not None:
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print("Use GPU: [{}/{}] for training".format(opts.global_rank, opts.local_rank))
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# 2. init_process_group
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dist.init_process_group(
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backend="nccl",
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rank=opts.global_rank,
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world_size=opts.world_size,
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device_id=torch.device(f"cuda:{opts.local_rank}"),
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timeout=datetime.timedelta(seconds=60)
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)
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# if put this function, the all processes block at all.
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#torch.distributed.barrier()
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return
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if __name__ == '__main__':
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parser = argparse.ArgumentParser('vgg11 cifar training', parents=[get_args_parser()])
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opts = parser.parse_args()
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main(opts)
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---
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apiVersion: v1
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kind: Service
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metadata:
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name: nccl
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labels: { app: nccl }
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spec:
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clusterIP: None # Headless
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selector: { app: nccl }
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ports:
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- name: rdv
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port: 23456
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---
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apiVersion: apps/v1
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kind: StatefulSet
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metadata:
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name: nccl
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spec:
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serviceName: nccl
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replicas: 2 # >=2 노드에서 스케줄되도록 노드 리소스 준비 필요
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selector:
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matchLabels: { app: nccl }
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template:
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metadata:
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labels: { app: nccl }
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annotations:
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k8s.v1.cni.cncf.io/networks: hostdevice-net # HostDeviceNetwork (RDMA)
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spec:
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# hostNetwork: true
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# dnsPolicy: ClusterFirstWithHostNet
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# RDMA 장치나 /dev/infiniband 노출이 안 보이면 네트워크 오퍼레이터의 RDMA 플러그인 구성을 확인하세요.
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# (환경에 따라 RDMA Shared Device Plugin이 필요할 수 있음)
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containers:
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- name: worker
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image: nvcr.io/nvidia/pytorch:24.10-py3
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imagePullPolicy: IfNotPresent
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securityContext:
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privileged: true
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capabilities:
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add:
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- IPC_LOCK
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- NET_ADMIN
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# capabilities: { add: ["IPC_LOCK"] }
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command: [ "bash", "-lc", "cp /config_scripts/* /workspace/ && chmod +x /workspace/run.sh && sleep infinity" ]
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env:
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# --- (선택) 특정 GPU만 사용하려면 아래 값을 물리 인덱스로 설정 (예: "2") ---
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# - name: CUDA_VISIBLE_DEVICES_OVERRIDE
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# value: "2"
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# RDMA 관련 기본값은 run.sh에서 설정
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resources:
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limits:
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nvidia.com/gpu: "8"
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nvidia.com/hostdev: "8" # SR-IOV Device Plugin이 광고한 RDMA NIC 리소스
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requests:
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nvidia.com/gpu: "8"
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nvidia.com/hostdev: "8"
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volumeMounts:
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- name: scripts
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mountPath: /config_scripts
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- name: shared-memory
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mountPath: /dev/shm
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volumes:
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- name: scripts
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configMap:
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name: nccl-scripts
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defaultMode: 0755
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- name: shared-memory
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emptyDir:
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medium: Memory
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sizeLimit: 118541097369600m
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