PYTORCH TIP: mixed precision training

gistfile1.txt

"""
Use torch.cuda.amp in PyTorch for mixed precision training. 
This method mixes 32-bit and 16-bit data to reduce memory use and speed up model training, 
without much loss in accuracy. 

It takes advantage of the quick computing of 16-bit data and controls precision by handling specific operations in 32-bit. 
This approach offers a balance between speed and accuracy in training models.
"""
import torch
from torch.cuda.amp import autocast, GradScaler
from torch import nn, optim

model = nn.Linear(10, 2).cuda()
optimizer = optim.SGD(model.parameters(), lr=0.01)
scaler = GradScaler()

data, target = torch.randn(5, 10).cuda(), 
 torch.randint(0, 2, (5,)).cuda()

optimizer.zero_grad()

with autocast():
 loss = nn.functional.cross_entropy(model(data), target)

scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()