This repository provides state-of-the-art deep learning scripts for various domains including Computer Vision, NLP, Recommender Systems, Speech, and GNNs. It targets researchers and engineers seeking to train and deploy models with reproducible accuracy and performance on NVIDIA enterprise-grade infrastructure, leveraging the NVIDIA CUDA-X software stack.

How It Works

The examples are organized by model and framework (PyTorch, TensorFlow, MXNet, PaddlePaddle), showcasing implementations optimized for NVIDIA GPUs, including Tensor Cores. Key features like Automatic Mixed Precision (AMP), TensorRT, ONNX, and Triton inference server integration are highlighted, enabling significant performance gains and easier deployment.

Quick Start & Requirements

• Installation: Primarily via NGC (NVIDIA GPU Cloud) Docker containers, which bundle the examples, NVIDIA libraries (cuDNN, NCCL, etc.), and framework versions.
• Prerequisites: NVIDIA GPUs (Volta, Turing, Ampere architectures recommended), CUDA-X software stack.
• Resources: NGC containers are monthly updated and include QA-tested libraries.
• Documentation: Links to NGC container registry: https://ngc.nvidia.com.

Highlighted Details

• Supports a wide range of models across multiple domains and frameworks.
• Optimized for NVIDIA hardware, emphasizing Tensor Core utilization and performance.
• Integrates with NVIDIA's deployment ecosystem: TensorRT, ONNX, Triton Inference Server.
• Provides support for multi-GPU and multi-node training for scalable workloads.

Maintenance & Community

NVIDIA actively maintains and updates the repository, encouraging community contributions via GitHub Issues and pull requests. Specific support levels for each network are detailed in individual READMEs.

Licensing & Compatibility

The repository itself appears to be under a permissive license allowing for broad use and contribution. Specific model implementations may have their own licensing terms.

Limitations & Caveats

The examples are heavily optimized for and dependent on NVIDIA hardware and software stack. While aiming for reproducibility, achieving identical performance may require specific hardware configurations and software versions. Support levels vary from ongoing updates to point-in-time releases.