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Siddha Ganju - 30 Golden Rules of Deep Learning Performance
Discover the 30 golden rules for optimizing deep learning performance, including batch size determination, model pruning, caching, and more, to improve training efficiency and reduce processing time.
- Progressive augmentation and image resizing are ways to optimize deep learning models.
- The ideal batch size depends on the GPU, e.g., 64, multiples of 64 or 256 on TPUs.
- Using TensorFlow data sets can speed up training, making it easier for researchers to work on novel directions.
- Nvidia’s automatic mixed precision library can optimize modeling processes.
- Pruning and compression can improve model performance by increasing sparsity.
- Minimizing storage requirements by compressing files can improve data processing speed.
- Caching can make data processing faster by reducing the need for repetitive file reads.
- Automatic mixed precision libraries can optimize model processing for faster training times.
- Transfer learning can be used to modify pre-trained models for new tasks.
- Prefetching data can help reduce idle time for the GPU and CPU.
- Using a larger batch size can improve model training efficiency.
- Anaconda, Dalai, and Horovod are some tools that can help optimize pipeline processing.
- colaboratory (colab) offers pre-processing tools for data preparation.
- Nvidia’s DALI library can perform augmentation operations.
- Efficient models like MobileNet can be optimized for better hardware utilization.
- Using a community-led effort like TensorFlow and PyTorch can provide better results.
- Applying the 30 golden rules of deep learning performance can lead to better model optimization and training times.