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Commit b5c13f46 authored by Piero Coronica's avatar Piero Coronica
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pytorch/example/main.py deleted 100644 → 0
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import argparse
import os
import random
import shutil
import time
import warnings
from enum import Enum
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.datasets as datasets
import torchvision.models as models
import torchvision.transforms as transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import Subset
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet',
help='path to dataset (default: imagenet)')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N',
help='mini-batch size (default: 256), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)',
dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
parser.add_argument('--world-size', default=-1, type=int,
help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
help='Use multi-processing distributed training to launch '
'N processes per node, which has N GPUs. This is the '
'fastest way to use PyTorch for either single node or '
'multi node data parallel training')
parser.add_argument('--dummy', action='store_true', help="use fake data to benchmark")
best_acc1 = 0
def main():
args = parser.parse_args()
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
cudnn.benchmark = False
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
if torch.cuda.is_available():
ngpus_per_node = torch.cuda.device_count()
if ngpus_per_node == 1 and args.dist_backend == "nccl":
warnings.warn("nccl backend >=2.5 requires GPU count>1, see https://github.com/NVIDIA/nccl/issues/103 perhaps use 'gloo'")
else:
ngpus_per_node = 1
if args.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
else:
# Simply call main_worker function
main_worker(args.gpu, ngpus_per_node, args)
def main_worker(gpu, ngpus_per_node, args):
global best_acc1
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if not torch.cuda.is_available() and not torch.backends.mps.is_available():
print('using CPU, this will be slow')
elif args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if torch.cuda.is_available():
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs of the current node.
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
print(f"Hello - Worker: {args.rank}\tSize: {args.world_size}\tdevice: {args.gpu}")
elif args.gpu is not None and torch.cuda.is_available():
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
elif torch.backends.mps.is_available():
device = torch.device("mps")
model = model.to(device)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
if torch.cuda.is_available():
if args.gpu:
device = torch.device('cuda:{}'.format(args.gpu))
else:
device = torch.device("cuda")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
# define loss function (criterion), optimizer, and learning rate scheduler
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
elif torch.cuda.is_available():
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
if args.gpu is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# Data loading code
if args.dummy:
print("=> Dummy data is used!")
train_dataset = datasets.FakeData(51200, (3, 224, 224), 1000, transforms.ToTensor())
val_dataset = datasets.FakeData(50000, (3, 224, 224), 1000, transforms.ToTensor())
else:
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
val_dataset = datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True)
else:
train_sampler = None
val_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True, sampler=val_sampler)
if args.evaluate:
validate(val_loader, model, criterion, args)
return
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, device, args)
# evaluate on validation set
def train(train_loader, model, criterion, optimizer, epoch, device, args):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
ips = AverageMeter('IPS', ':6.2f')
progress = ProgressMeter(
len(train_loader),
[batch_time, data_time, ips],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
for i, (images, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
# move data to the same device as model
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
# compute output
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
ips.update(len(images)/(time.time() - end))
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i + 1)
def validate(val_loader, model, criterion, args):
def run_validate(loader, base_progress=0):
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(loader):
i = base_progress + i
if args.gpu is not None and torch.cuda.is_available():
images = images.cuda(args.gpu, non_blocking=True)
if torch.backends.mps.is_available():
images = images.to('mps')
target = target.to('mps')
if torch.cuda.is_available():
target = target.cuda(args.gpu, non_blocking=True)
# compute output
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i + 1)
batch_time = AverageMeter('Time', ':6.3f', Summary.NONE)
losses = AverageMeter('Loss', ':.4e', Summary.NONE)
top1 = AverageMeter('Acc@1', ':6.2f', Summary.AVERAGE)
top5 = AverageMeter('Acc@5', ':6.2f', Summary.AVERAGE)
progress = ProgressMeter(
len(val_loader) + (args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset))),
[batch_time, losses, top1, top5],
prefix='Test: ')
# switch to evaluate mode
model.eval()
run_validate(val_loader)
if args.distributed:
top1.all_reduce()
top5.all_reduce()
if args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset)):
aux_val_dataset = Subset(val_loader.dataset,
range(len(val_loader.sampler) * args.world_size, len(val_loader.dataset)))
aux_val_loader = torch.utils.data.DataLoader(
aux_val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
run_validate(aux_val_loader, len(val_loader))
progress.display_summary()
return top1.avg
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
class Summary(Enum):
NONE = 0
AVERAGE = 1
SUM = 2
COUNT = 3
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE):
self.name = name
self.fmt = fmt
self.summary_type = summary_type
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def all_reduce(self):
if torch.cuda.is_available():
device = torch.device("cuda")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
total = torch.tensor([self.sum, self.count], dtype=torch.float32, device=device)
dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False)
self.sum, self.count = total.tolist()
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
def summary(self):
fmtstr = ''
if self.summary_type is Summary.NONE:
fmtstr = ''
elif self.summary_type is Summary.AVERAGE:
fmtstr = '{name} {avg:.3f}'
elif self.summary_type is Summary.SUM:
fmtstr = '{name} {sum:.3f}'
elif self.summary_type is Summary.COUNT:
fmtstr = '{name} {count:.3f}'
else:
raise ValueError('invalid summary type %r' % self.summary_type)
return fmtstr.format(**self.__dict__)
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
print('\t'.join(entries))
def display_summary(self):
entries = [" *"]
entries += [meter.summary() for meter in self.meters]
print(' '.join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = '{:' + str(num_digits) + 'd}'
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()
pytorch/get-training-script.sh deleted 100755 → 0
+ 0
− 7
View file @ af347909
#! /bin/bash
# Download pyTorch example script
curl -o main.py https://raw.githubusercontent.com/pytorch/examples/8c246ba77dfd8dc738038591a74116605ba8a4c2/imagenet/main.py
# Modify size dummy train dataset
sed -i 's/1281167/51200/' main.py
# Remove validate and checkpoint
sed -i '284,301d' main.py
\ No newline at end of file
pytorch/raven/build.slurm deleted 100644 → 0
+ 0
− 49
View file @ af347909
#!/bin/bash -l
#SBATCH -D ./
#SBATCH -o _build_%j.log
#SBATCH -e _build_%j.log
#SBATCH -J build-pt
#SBATCH --time=0-00:30:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem=0
#SBATCH --cpus-per-task=72
#SBATCH --threads-per-core=1
module purge
module load apptainer/1.3.2
module list
## APPTAINER TMP DIR IN
# export APPTAINER_TMPDIR=/ptmp/$USER/.apptainer_example/tmp
export APPTAINER_CACHEDIR=/ptmp/$USER/.apptainer_example/cache
export APPTAINER_TMPDIR=${JOB_SHMTMPDIR}/.apptainer_example/tmp
# export APPTAINER_CACHEDIR=${JOB_SHMTMPDIR}/.apptainer_example/cache
mkdir -p $APPTAINER_TMPDIR $APPTAINER_CACHEDIR
echo APPTAINER_TMPDIR: $APPTAINER_TMPDIR
echo APPTAINER_CACHEDIR: $APPTAINER_CACHEDIR
IMAGE_DEF=pytorch-imagenet.def
# IMAGE_TMP=${JOB_SHMTMPDIR}/tmp.sif
commit_hash=$(git rev-parse --short HEAD)
date_stamp=$(date +"%Y-%m-%d_%H-%M-%S")
IMAGE_NAME=/ptmp/pierocor/containers/pytorch-ai_${commit_hash}_${date_stamp}.sif
# time apptainer build ${IMAGE_TMP} ${IMAGE_DEF} && mv ${IMAGE_TMP} ${IMAGE_NAME} &
time apptainer build ${IMAGE_NAME} ${IMAGE_DEF} &
PID=$!
# MONITOR TODO: REMOVE IT!
build_info=_build_${SLURM_JOBID}_info.csv
_print=$(top -u pierocor -b -n 1 | grep PID)
echo $_print > $build_info
while [ -n "$_print" ] ; do
# Get the RSS, virtual memory, and CPU usage of the process
mapfile -t _print < <(top -u pierocor -b -n 1 | egrep "apptainer|mksquashfs")
printf '%s\n' "${_print[@]}"
# Wait 10 seconds before taking the next measurement
sleep 10
done >> $build_info
pytorch/raven/pytorch-imagenet.def → pytorch/raven/nv-pytorch.def
+ 0
− 0
View file @ b5c13f46
File moved
pytorch/viper/amd-pytorch.def 0 → 100644
+ 9
− 0
View file @ b5c13f46
BootStrap: docker
From: rocm/pytorch:rocm6.3.4_ubuntu24.04_py3.12_pytorch_release_2.4.0
%labels
author Piero Coronica
version 1.0
%help
The converted Docker image from ROCm PyTorch (rocm 6.3.4, pytorch 2.4.0).
\ No newline at end of file
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