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Opis
PyTorch je python knjižnica namijenjena razvoju aplikacija temeljenih na dubokom učenju koja se oslanja na ubrzanje grafičkim procesorima. Glavne prednosti koje PyTorch knjižnica pruža su imperativni pristup programiranju na "python" način, kroz jednostavno sučelje koje omogućuje lakše otkrivanje grešaka pri razvoju.
Verzije
verzija | modul | red |
---|
1.14.0 | scientific/pytorch/1.14.0-ngc | gpu |
Dokumentacija
Primjeri
Ispod se nalaze primjeri aplikacija umjetnog benchmarka koji testira performanse na modelu Resnet50:
- singlegpu.py - python skripta umjetnog benchmarka
- singlegpu.sh - skripta sustava PBS koja koristi jedan grafički procesor
# source
# - https://github.com/horovod/horovod/blob/master/examples/pytorch/pytorch_synthetic_benchmark.py
import argparse
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data.distributed
from torchvision import models
import sys
import time
import numpy as np
# Benchmark settings
parser = argparse.ArgumentParser(description='PyTorch Synthetic Benchmark',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-i",
"--images",
type=int,
help="image number",
default=1024)
parser.add_argument('--batch_size',
type=int,
default=32,
help='input batch size')
parser.add_argument("-e",
"--epochs",
type=int,
help="epochs",
default=10)
parser.add_argument('--model',
type=str,
default='resnet50',
help='model to benchmark')
args = parser.parse_args()
# model
model = getattr(models, args.model)()
model.cuda()
lr_scaler = 1
optimizer = optim.SGD(model.parameters(), lr=0.01 * lr_scaler)
cudnn.benchmark = True
# data
data = torch.randn(args.batch_size, 3, 224, 224)
target = torch.LongTensor(args.batch_size).random_() % 1000
data, target = data.cuda(), target.cuda()
# fit
def benchmark_step():
optimizer.zero_grad()
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
for epoch in range(args.epochs):
begin = time.time()
for batches in range(args.images//args.batch_size):
benchmark_step()
end = time.time()
imgsec = args.images//(end-begin)
print('--- Epoch %i: %0.2f img/sec ---' % (epoch, imgsec))
#!/bin/bash
#PBS -q gpu
#PBS -l select=1:ncpus=32:ngpus=1
#PBS -o output/
#PBS -e output/
# pozovi modul
module load scientific/pytorch/1.14.0-ngc
# pomakni se u direktorij gdje se nalazi skripta
cd ${PBS_O_WORKDIR:-""}
# potjeraj skriptu
run-singlenode.sh singlegpu.py \
--images 10240 \
--batch_size 256 \
--epochs 5
Napomene