PyTorch CUDA Out of Memory Error: Despite Free Memory

• Introduction
• Step-by-Step Guide
• Code Example
• Additional Notes
• Summary
• Conclusion
• References

Training deep learning models often requires significant GPU memory, and running out of CUDA memory is a common issue. This article provides a comprehensive guide with twelve practical solutions to troubleshoot and resolve "CUDA out of memory" errors during your training process.

1. Understand the Error: "CUDA out of memory" means your GPU doesn't have enough memory to store the data and model during training.

2. Check GPU Memory:

   import torch
   print(torch.cuda.memory_summary())

3. Reduce Batch Size:

   batch_size = 32  # Try 16, 8, 4, etc.

4. Use Smaller Data: Load only a portion of your dataset into memory.

5. Use a Smaller Model: Choose a model with fewer parameters.

6. Data Loading Optimization:

   torch.utils.data.DataLoader(..., num_workers=4, pin_memory=True)

7. Gradient Accumulation: Simulate larger batch sizes with limited memory.

8. Mixed Precision Training (fp16): Use half-precision floats.

   model.half()  # Convert model to fp16

9. Clear Unused Variables:

   del variable  
   torch.cuda.empty_cache()

10. Use Gradient Checkpointing: Trade computation time for memory.

11. Use a GPU with More Memory: If possible, upgrade your hardware.

12. Monitor Memory Usage: Use tools like nvidia-smi to track GPU memory.

This Python code defines and trains a simple convolutional neural network (CNN) to classify handwritten digits from the MNIST dataset using PyTorch. It includes data loading, model definition, training loop, and optional techniques for performance optimization like GPU utilization, gradient accumulation, and mixed precision training. The code also provides comments on monitoring memory usage and implementing gradient checkpointing for advanced users.

import torch
import torch.nn as nn
from torchvision import datasets, transforms

# --- Hyperparameters ---
batch_size = 32  # Reduce this if you run out of memory
learning_rate = 0.001
epochs = 10

# --- Data Loading ---
train_dataset = datasets.MNIST(
    root="./data",
    train=True,
    download=True,
    transform=transforms.ToTensor(),
)
train_loader = torch.utils.data.DataLoader(
    train_dataset,
    batch_size=batch_size,
    shuffle=True,
    num_workers=4,  # Use multiple workers for data loading
    pin_memory=True,  # Pin memory for faster transfer to GPU
)

# --- Model Definition ---
class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2)
        self.fc = nn.Linear(16 * 13 * 13, 10)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = x.view(-1, 16 * 13 * 13)
        x = self.fc(x)
        return x

# --- Device Selection ---
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# --- Model, Optimizer, Loss ---
model = SimpleCNN().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
loss_fn = nn.CrossEntropyLoss()

# --- Training Loop ---
for epoch in range(epochs):
    for batch_idx, (data, target) in enumerate(train_loader):
        # --- Data to Device ---
        data, target = data.to(device), target.to(device)

        # --- Forward Pass ---
        output = model(data)
        loss = loss_fn(output, target)

        # --- Backward Pass and Optimization ---
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # --- Print Progress (Optional) ---
        if batch_idx % 100 == 0:
            print(
                f"Epoch: {epoch+1}/{epochs}, Batch: {batch_idx}/{len(train_loader)}, Loss: {loss.item():.4f}"
            )

        # --- Clear Unused Variables (Optional) ---
        del data, target, output, loss

        # --- Gradient Accumulation (Example) ---
        # if (batch_idx + 1) % 2 == 0:  # Accumulate gradients over 2 batches
        #     optimizer.step()
        #     optimizer.zero_grad()

# --- Mixed Precision Training (fp16) ---
# model.half()  # Convert model to fp16
# ... (Modify optimizer and data types accordingly)

# --- Gradient Checkpointing (Advanced) ---
# Use libraries like torch.utils.checkpoint

# --- Monitor Memory Usage ---
# Use nvidia-smi command in the terminal

Explanation:

1. Hyperparameters: Start by reducing the batch_size.
2. Data Loading:
   • Use num_workers to load data in parallel.
   • pin_memory=True speeds up data transfer to the GPU.
3. Model Definition: If possible, choose a simpler model with fewer layers or parameters.
4. Training Loop:
   • Move data (data, target) to the selected device (CPU or GPU).
   • Optionally clear unused variables (del) and call torch.cuda.empty_cache() within the loop to free up GPU memory.
5. Gradient Accumulation: The commented-out code demonstrates how to simulate larger batch sizes by accumulating gradients over multiple mini-batches.
6. Mixed Precision Training (fp16): The commented-out code shows how to enable fp16 training, which can reduce memory usage. Remember to adjust your optimizer and data types accordingly.
7. Gradient Checkpointing: For very deep models, consider using gradient checkpointing (refer to PyTorch documentation for details).
8. Monitor Memory Usage: Regularly monitor your GPU memory usage using nvidia-smi in a separate terminal to track how your changes affect memory consumption.

Remember to experiment with these techniques one at a time and find the best combination for your specific model and dataset.

General Tips:

• Restart Kernel/Runtime: Sometimes, simply restarting your Jupyter Notebook kernel or your entire runtime environment (if using cloud services like Colab) can clear up memory issues.
• Close Other Applications: Close any unnecessary applications that might be using GPU memory.
• Experiment and Iterate: Finding the optimal balance between memory usage and training speed often requires experimentation. Try different combinations of the solutions mentioned above.

Understanding the Error Message:

The "CUDA out of memory" error message often provides additional details:

• "Tried to allocate ... MiB": This indicates the size of the memory allocation that failed.
• "GPU ...; ... GiB total capacity; ... GiB already allocated; ... bytes free": This breakdown shows the total, used, and free memory on your GPU.
• "If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation": This suggests potential memory fragmentation issues. Consider setting the PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:VALUE environment variable (experiment with different values).

Advanced Techniques:

• Distributed Training: For very large models and datasets, consider using distributed training frameworks like PyTorch's DistributedDataParallel to split the workload across multiple GPUs.
• Model Parallelism: If your model is too large to fit on a single GPU, explore model parallelism techniques to distribute it across multiple devices.
• Profiling: Use PyTorch's profiling tools (torch.profiler) or other GPU profiling tools (e.g., NVIDIA Nsight Systems) to identify memory bottlenecks in your code.

Code Example Notes:

• The provided code is a basic example. You'll need to adapt it to your specific model architecture, dataset, and training requirements.
• The comments in the code provide guidance on where to implement the different memory optimization techniques.
• Remember to uncomment and modify the code snippets for gradient accumulation, mixed precision training, and gradient checkpointing based on your needs.

This guide summarizes common solutions for the "CUDA out of memory" error, which occurs when your GPU lacks sufficient memory for training data and models.

| Solution | Description

By implementing these strategies, you can effectively manage GPU memory and overcome "CUDA out of memory" errors. Remember that the most effective approach often involves a combination of these solutions, tailored to your specific deep learning task. Experiment, monitor your memory usage, and iterate to find the optimal configuration for your training process.

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