在Ubuntu上优化PyTorch性能可以通过多种方法实现,以下是一些关键的优化技巧:
sudo apt update && sudo apt upgrade
sudo ubuntu-drivers autoinstall
sudo apt install libmkl-dev libopenblas-dev
torch.cuda.amp
模块进行混合精度训练,减少显存占用并加速训练过程。from torch.cuda.amp import GradScaler, autocast
scaler = GradScaler()
for data, target in dataloader:
optimizer.zero_grad()
with autocast():
output = model(data)
loss = criterion(output, target)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
accumulation_steps = 4
for i, (data, target) in enumerate(dataloader):
output = model(data)
loss = criterion(output, target)
loss = loss / accumulation_steps
loss.backward()
if (i + 1) % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
num_workers
参数增加数据加载的并行性。dataloader = DataLoader(dataset, batch_size=32, num_workers=4)
scripted_module = torch.jit.trace(model, example_inputs)
torch.inference_mode()
启用推理模式,以节省内存并加速计算。通过上述方法,可以显著提高在Ubuntu上使用PyTorch进行深度学习任务的效率。根据具体的硬件配置和模型需求,可以选择合适的优化策略。