Hey Sure,
I understand that you are trying to configure MATLAB to make use of all the GPU memory for training the network.
To make full use of all the GPU memory when training a Convolutional Neural Network (CNN) in MATLAB's Deep Learning Toolbox, you can adjust several parameters and configurations. Here are some steps you can follow:
- Increase Mini-Batch Size: While you mentioned that you set the batch size to 4096, try increasing it even further. A larger batch size can help utilize more GPU memory effectively. However, keep in mind that extremely large batch sizes might lead to slower convergence or other issues, so experiment to find the right balance.
- Data Augmentation: If you're not already using data augmentation, consider adding it to your data preprocessing pipeline. Data augmentation can increase the effective size of your dataset and might allow you to use larger batch sizes.
- Check Network Architecture: Ensure that your network architecture is suitable for parallel training. Some network architectures or layer configurations might not be easily parallelizable across multiple GPUs. Make sure you're using an architecture that benefits from parallelization.
- Parallel Training Settings: Verify that you've correctly set up parallel training in MATLAB. You should use trainNetwork with the ExecutionEnvironment set to 'multi-gpu', and the MiniBatchSize property set to your desired batch size.
- GPU Memory Management: Check if there are any other processes or applications running that might be using GPU memory. Close unnecessary applications to free up more GPU memory for MATLAB.
- Batch Gradient Accumulation: If increasing the batch size still doesn't fully utilize the GPU memory, you can implement batch gradient accumulation. In this technique, you accumulate gradients over multiple mini-batches and update the weights once the accumulated gradients reach a certain threshold. This can effectively use more GPU memory while maintaining training stability.
I hope this helps!
