LichtFeld-Studio Core Dump During Training: Troubleshooting

Dec 6, 2025 by Alex Johnson 60 views

Encountering a core dump during the training phase of a neural rendering project, such as with LichtFeld-Studio, can be a frustrating experience. This article delves into the potential causes and solutions for a core dump issue specifically within the context of LichtFeld-Studio, using a large dataset of 1700 images aligned with RealityScan and exported in COLMAP format. We'll examine the error logs, discuss potential hardware and software conflicts, and explore strategies to optimize your training process.

Understanding the Error Messages

The error log provides several clues about what might be going wrong. Let's break down the key messages and what they imply:

Parameter Mismatches: The log indicates discrepancies between the JSON configuration file and the default parameters of LichtFeld-Studio. Specifically, means_lr has different values in the JSON and the default settings. It also lists parameters present in the struct but missing from the JSON, and vice versa. These mismatches can lead to unexpected behavior during training.
Temporary Directory Issues: The repeated error message Project temporary directory exists. This should not happen /tmp/LichtFeldStudio/lfs_proj__187e8 suggests a problem with how LichtFeld-Studio is managing temporary files. This could be due to a previous crash leaving the directory behind, or a permission issue.
CUDA Error: Illegal Memory Access: This is a critical error that directly leads to the core dump. It indicates that the CUDA kernel, which is responsible for GPU computations, is trying to access a memory location that it is not authorized to access. This often points to issues with memory management, incorrect data types, or out-of-bounds access within the CUDA code.
Cache Image Loader: The message not all images fit to cpu memory required_GB 34.25. available 8.92 indicates that the system doesn't have enough CPU memory to cache all the images, potentially leading to performance issues or crashes during training.

Diagnosing the Core Dump

Given these error messages, here's a structured approach to diagnosing the core dump issue:

Address Parameter Mismatches:
- Configuration Review: Carefully examine the JSON configuration file you are using to ensure that all parameters are correctly defined and aligned with the expected values. Pay close attention to the learning rate parameters (means_lr, final_lr_fraction, etc.) as they can significantly impact training stability.
- Default Values: If certain parameters are missing from your JSON, consider whether you need to explicitly define them or if the default values are appropriate. If you need to define them, ensure you use the correct syntax and data types. The log displays default values for missing parameters which should be used as a reference.
- Unrecognized Parameters: Remove any unrecognized parameters (bg_modulation, gut) from the JSON file. These parameters are being ignored and could indicate an outdated or incorrect configuration.
Investigate Temporary Directory Issues:
- Manual Cleanup: Manually delete the temporary directory /tmp/LichtFeldStudio/lfs_proj__187e8 before starting LichtFeld-Studio. Ensure that you have the necessary permissions to delete this directory.
- Permissions: Verify that the user running LichtFeld-Studio has read and write permissions to the /tmp directory. Incorrect permissions can prevent the application from creating and managing temporary files.
Tackle the CUDA Error:
- CUDA Version: Ensure that you have a compatible version of CUDA installed for your GPU and the version of PyTorch (used by LichtFeld-Studio). Mismatched versions can lead to illegal memory access errors.
- Driver Version: Update your NVIDIA drivers to the latest stable version. Outdated or buggy drivers are a common cause of CUDA errors.
- Memory Usage: Monitor GPU memory usage during training. If you're running out of memory, reduce the batch size, image resolution, or the number of Gaussians to fit within the GPU's capacity. Tools like nvidia-smi can help monitor GPU usage.
- CUDA Launch Blocking: As the error message suggests, try running LichtFeld-Studio with the environment variable CUDA_LAUNCH_BLOCKING=1. This forces CUDA to execute kernels synchronously, which can make it easier to identify the source of the error. Note that this will significantly slow down training.
- Device-Side Assertions: Compile PyTorch with TORCH_USE_CUDA_DSA to enable device-side assertions. This can help catch errors that occur within the CUDA kernels themselves.
Optimize Image Loading:
- Reduce Image Size: If possible, reduce the size of your input images. This will decrease the memory footprint and potentially allow more images to be cached in CPU memory.
- Increase RAM: The logs clearly indicate that there is not enough memory to load all the images, consider increasing the system's RAM if possible.
- Preprocess Images: Although the logs say that the images are not preprocessed, preprocessing images can optimize loading times and reduce memory usage during training. Consider converting images to a more efficient format or resizing them.
- Increase num_workers: by increasing the number of image loader threads the image loading process should become faster. Be careful to not set this higher than the amount of virtual processors.

Hardware and Software Considerations

GPU: Ensure your GPU is powerful enough to handle the demands of neural rendering. Insufficient GPU memory or processing power can lead to crashes.
CPU: A strong CPU is also important, especially for data loading and preprocessing. The error log indicates that the CPU is struggling to load all the images into memory.
Operating System: Verify that your operating system is compatible with LichtFeld-Studio and all its dependencies (CUDA, PyTorch, etc.).
Dependencies: Double-check that all the required libraries and dependencies are installed and that their versions are compatible with each other.

Practical Steps and Code Examples

Updating NVIDIA Drivers (Ubuntu):

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt install nvidia-driver-535 # Replace 535 with the latest version
sudo reboot

Setting CUDA Launch Blocking:

export CUDA_LAUNCH_BLOCKING=1
./LichtFeld-Studio

Monitoring GPU Usage:
```
nvidia-smi
```

Adjusting Batch Size (Example in PyTorch-like syntax):

# Assuming you have a training loop like this:
for epoch in range(num_epochs):
    for i, data in enumerate(train_loader):
        inputs, labels = data
        inputs = inputs.to(device)
        labels = labels.to(device)

        # ... your training code here ...

# Reduce batch size in your data loader
train_loader = DataLoader(dataset, batch_size=16, shuffle=True) # Reduced from e.g. 32 to 16

Conclusion

Resolving a core dump issue in LichtFeld-Studio requires a systematic approach, starting with a careful examination of the error messages and followed by targeted troubleshooting steps. By addressing parameter mismatches, temporary directory issues, CUDA errors, and image loading problems, you can increase the stability of your training process and achieve successful results with your neural rendering projects. Remember to monitor your hardware resources and keep your software dependencies up to date for optimal performance. For additional information on debugging CUDA errors, visit the NVIDIA Developer Blog for in-depth articles and best practices.