DeepLearningExamples

Why we included this project

NVIDIA maintains this collection as its reference set of deep learning examples, and it is better treated as a learning and benchmarking resource than as a single deployable product. Each subdirectory is a self-contained recipe for one model, EfficientNet, Mask R-CNN, BERT, Tacotron, and others, with scripts, Dockerfiles, and Jupyter notebooks that show how to hit published accuracy and throughput on Volta, Turing, and Ampere GPUs. Teams looking for a solid starting point for mixed-precision training, multi-GPU scaling, or TensorRT and Triton inference can adapt these working examples instead of building from scratch. Because the models span computer vision, NLP, speech, and recommender systems across PyTorch, TensorFlow, and MXNet, the repo also works as a survey of how NVIDIA lays out production-grade training pipelines. What makes it worth a look is the curated, reproducible reference implementations and the notebooks that walk through them, not a turnkey application you can run out of the box.