CUDA out of memory. Tried to allocate 384.00 MiB (GPU 0; 15.90 GiB total capacity; 14.69 GiB already allocated; 291.75 MiB free; 14.76 GiB reserved in total by PyTorch)

[abrh119]

any fixes for this?

[Alegzandra]

Try reducing the batch size.

[Siddharth-Latthe-07]

here are some possible solution for the above issue:-

1. Reduce Batch Size:
   The most straightforward way is to reduce the batch size. This decreases the amount of memory needed for each forward and backward pass.
   sample snippet:-

batch_size = 16  # Try reducing this value
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

2. clear cache
3. Use Mixed Precision Training:
   Mixed precision training can significantly reduce memory usage by using half-precision (float16) instead of full-precision (float32).
   Sample snippet:-

from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()
for epoch in range(num_epochs):
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        with autocast():
            outputs = model(inputs.float())
            loss = criterion(outputs, labels.long())
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()

3. Gradient Accumulation:
   Accumulate gradients over several mini-batches to simulate a larger batch size

accumulation_steps = 4
for epoch in range(num_epochs):
    optimizer.zero_grad()
    for i, (inputs, labels) in enumerate(train_loader):
        outputs = model(inputs.float())
        loss = criterion(outputs, labels.long())
        loss = loss / accumulation_steps
        loss.backward()
        if (i + 1) % accumulation_steps == 0:
            optimizer.step()
            optimizer.zero_grad()

4. Optimize Data Loading:
   Ensure that data loading is efficient and not causing memory overheads.

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)

5. Example with Reduced Batch Size and Mixed Precision
   Here is how you can combine reducing batch size and using mixed precision training:

import torch
from torch.cuda.amp import autocast, GradScaler
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# Assuming you have your dataset and model defined
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)  # Reduced batch size
model = CNN_LSTM(input_dim, hidden_dim, num_classes).cuda()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
scaler = GradScaler()

for epoch in range(num_epochs):
    model.train()
    for inputs, labels in train_loader:
        inputs, labels = inputs.cuda(), labels.cuda()  # Ensure data is on GPU
        optimizer.zero_grad()
        with autocast():
            outputs = model(inputs.float())
            loss = criterion(outputs, labels.long())
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()

    # Evaluate on validation set if needed
    # Calculate accuracy, etc.

# Example evaluation loop
model.eval()
total_correct = 0
total_samples = 0

for inputs, labels in test_loader:
    inputs, labels = inputs.cuda(), labels.cuda()
    with torch.no_grad():
        outputs = model(inputs.float())
        _, predicted = torch.max(outputs, 1)
        total_correct += (predicted == labels).sum().item()
        total_samples += labels.size(0)

accuracy = total_correct / total_samples
print(f'Accuracy on test set: {accuracy}')

Hope, this helps
Thanks