nvidia/deeplearningexamples

This repository is a collection of deep learning example projects published by NVIDIA. The README frames it as a set of ready-made training and deployment scripts for well-known model architectures, tuned so that they reach the published accuracy numbers and good performance on NVIDIA's Volta, Turing, and Ampere generation GPUs. The phrase Tensor Cores in the title refers to special units inside those GPUs that accelerate the mixed-precision math used in modern training. The code is meant to be run inside Docker containers that NVIDIA publishes monthly on its NGC container registry. Those containers include the examples from this repository, NVIDIA's contributions to the upstream framework projects, and supporting libraries such as cuDNN, NCCL, and cuBLAS. The README points out that everything in the monthly image goes through a quality assurance pass first, and links to per-release notes for the different framework images. Most of the README is a large table that lists every model in the collection. The Computer Vision section alone covers image classification networks such as several variants of EfficientNet and GPUNet, segmentation models including two Mask R-CNN ports and nnUNet, and other vision tasks. Each row notes which framework the example is written in (PyTorch, TensorFlow2, MXNet, or PaddlePaddle), and whether the example supports automatic mixed precision, multi-GPU training, multi-node training, export to TensorRT for fast inference, export to ONNX, deployment with the Triton inference server, an entry on NVIDIA's Deep Learning Containers index, and a companion Jupyter notebook. Beyond computer vision, the wider repository follows the same table format for natural language processing models, recommendation models, forecasting models, drug discovery work, and large language model training. The topics tags on the GitHub page list those areas, which is why one repository covers such a broad range. Each row in the tables links to the subfolder where the actual training scripts, configuration files, and documentation for that single model live. For a non-technical reader, the practical takeaway is that the project is a reference library aimed at engineers who have NVIDIA GPU hardware and want a known-good starting point for training or running a particular model, rather than a single product you install and use directly.