How to check for memory leak in python

For memory-intensive tasks, you can use Python’s memory_profiler package to find out the memory usage of your code line by line. This can help you identify memory leaks or spots where memory usage is unexpectedly high. We will also show the mixed precision training in pytorch.

Here’s how you can use it:

!pip install memory_profiler

• Diagnose memory leaks by tracking unexplained increases in memory usage over time.
• Reduce memory costs by pinpointing and minimizing excessive memory consumption in critical sections.

from memory_profiler import profile

@profile
defmy_func():
 a = [1] * (10 ** 6)
 b = [2] * (2 * 10 ** 7)
 del b
return a

then run:

python -m memory_profiler your_script.py

What is mixed precision training:

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()