Learn how to use NVIDIA Performance Primitives (NPP) to leverage the GPU's power and accelerate training processes significantly. NPP is composed of over 5,000 signal and image processing functions, including GPU-based JPEG encoder and decoder functions, resize, warp affine transform, rotation, color conversion, etc. These functions can be used seamlessly in many areas of deep learning, such as image loading, data augmentation, etc. Not only these GPU functions are faster than their CPU counterparts, but also by using them the end users save the overhead from exchanging the memory between host and device. We'll introduce NPP and its performance against the state-of-the-art CPU implementations. Then we'll focus on showing how easy and natural it is to integrate NPP in deep training projects, with only minor changes to code bases. Finally, we'll take an example and present the performance comparison with and without NPP acceleration.