# nvidia/deeplearningexamples **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/nvidia-deeplearningexamples).** 14,819 stars · 3,406 forks · Jupyter Notebook ## Links - GitHub: https://github.com/NVIDIA/DeepLearningExamples - awesome-repositories: https://awesome-repositories.com/repository/nvidia-deeplearningexamples.md ## Topics `computer-vision` `deep-learning` `drug-discovery` `forecasting` `large-language-models` `mxnet` `nlp` `paddlepaddle` `pytorch` `recommender-systems` `speech-recognition` `speech-synthesis` `tensorflow` `tensorflow2` `translation` ## Description This project is a collection of optimized scripts, deployment patterns, and reference implementations designed for scaling and accelerating state-of-the-art AI models. It serves as a multi-domain model zoo and a distributed training framework, providing PyTorch reference implementations for training and deploying models on GPU-accelerated infrastructure. The repository distinguishes itself through an optimization suite focused on NVIDIA GPU hardware, utilizing automatic mixed precision and specialized math modes to increase training speed and throughput. It provides enterprise deployment patterns using pre-configured containers to ensure reproducible performance and accuracy when moving trained models into production environments. The implementation surface covers a wide range of machine learning architectures, including computer vision, natural language processing, graph neural networks, audio, recommendation systems, and time-series forecasting. These are supported by capabilities for multi-GPU data parallelism, distributed cluster training, and domain-specific compiler optimizations to handle large-scale workloads. ## Tags ### Artificial Intelligence & ML - [Distributed Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks.md) — Ships a distributed training framework for scaling deep learning workloads across multi-node GPU clusters. - [Computer Vision Models](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-models.md) — Provides reference implementations for image classification, object detection, and segmentation models optimized for GPU hardware. ([source](https://github.com/nvidia/deeplearningexamples#readme)) - [Deep Learning Reference Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-reference-implementations.md) — Ships reference scripts for training state-of-the-art deep learning models with consistent accuracy across environments. ([source](https://github.com/nvidia/deeplearningexamples#readme)) - [Distributed GPU Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-gpu-training.md) — Distributes computational loads of neural network training across multiple GPU nodes using synchronized data parallelism. - [Enterprise AI Deployments](https://awesome-repositories.com/f/artificial-intelligence-ml/enterprise-ai-deployments.md) — Provides enterprise-grade deployment patterns and pre-configured containers to ensure reproducible performance in production. - [Computer Vision](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/computer-vision.md) — Provides toolkits and optimized scripts for training and deploying deep learning models for image processing. - [Mixed Precision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training.md) — Utilizes 16-bit and 32-bit floating point formats to increase training throughput and reduce GPU memory usage. - [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-training.md) — Enables scaling of machine learning model training across multiple compute nodes and GPU clusters. ([source](https://github.com/nvidia/deeplearningexamples#readme)) - [Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/training-algorithms/deep-learning-optimization/model-deployment.md) — Implements processes for preparing and moving optimized models into production execution on target hardware. - [Natural Language Processing Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing-implementations.md) — Implements reference models for natural language understanding, translation, and sequence generation. ([source](https://github.com/nvidia/deeplearningexamples#readme)) - [Optimization Patterns](https://awesome-repositories.com/f/artificial-intelligence-ml/nvidia-gpu-kernels/optimization-patterns.md) — Provides reference patterns for accelerating training speed through mixed precision and specialized math modes on NVIDIA hardware. - [Graph Neural Network Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/graph-neural-network-implementations.md) — Provides reference implementations for processing non-Euclidean data and geometric deep learning tasks. ([source](https://github.com/nvidia/deeplearningexamples#readme)) - [Pre-trained Model Zoos](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/pre-trained-model-zoos.md) — Offers a multi-domain collection of reference model implementations for computer vision, NLP, and graph neural networks. - [Model Compilation Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-compilation-optimizers.md) — Transforms high-level model definitions into optimized representations to increase execution speed on target hardware. - [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Provides reference implementations for language understanding and translation tasks. - [Recommendation Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/recommendation-architectures.md) — Implements deep learning architectures specifically designed for personalized recommendation and ranking tasks. ([source](https://github.com/nvidia/deeplearningexamples#readme)) - [Reproducible Training Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/reproducible-training-workflows.md) — Provides scripts to achieve reproducible accuracy and performance when training models on high-capacity hardware. ([source](https://github.com/nvidia/deeplearningexamples#readme)) - [Time Series Forecasting](https://awesome-repositories.com/f/artificial-intelligence-ml/time-series-forecasting.md) — Provides models and architectures for predicting future values in temporal data sequences. ([source](https://github.com/nvidia/deeplearningexamples#readme)) ### Part of an Awesome List - [Data Parallelism](https://awesome-repositories.com/f/awesome-lists/devtools/gpu-acceleration/deep-learning-acceleration/data-parallelism.md) — Splits training batches across multiple GPUs to accelerate the convergence of large deep learning models. - [Speech and Audio Models](https://awesome-repositories.com/f/awesome-lists/ai/speech-and-audio-models.md) — Provides reference models for speech recognition and synthesis optimized for GPU acceleration. ([source](https://github.com/nvidia/deeplearningexamples#readme)) ### DevOps & Infrastructure - [AI Deployment Containers](https://awesome-repositories.com/f/devops-infrastructure/ai-deployment-containers.md) — Provides pre-configured container environments optimized for machine learning workflows and model deployment. ([source](https://github.com/nvidia/deeplearningexamples#readme)) - [AI Model Production Deployment](https://awesome-repositories.com/f/devops-infrastructure/ai-model-production-deployment.md) — Provides reference implementation patterns for moving trained deep learning models into production environments. ([source](https://github.com/nvidia/deeplearningexamples#readme)) ### Hardware & IoT - [Training Throughput Optimizations](https://awesome-repositories.com/f/hardware-iot/integration-performance/gpu-performance/training-throughput-optimizations.md) — Implements automatic mixed precision and specialized math modes to increase training speed and throughput on NVIDIA GPUs. ### Education & Learning Resources - [Deep Learning Reference Implementations](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education/deep-learning-reference-implementations.md) — Supplies optimized PyTorch reference implementations for training and deploying state-of-the-art models. ### Software Engineering & Architecture - [Architecture Reference Implementations](https://awesome-repositories.com/f/software-engineering-architecture/open-source-projects/architecture-reference-implementations.md) — Provides standardized Python scripts as blueprints for training and deploying specific model architectures. - [Container-Based Isolation](https://awesome-repositories.com/f/software-engineering-architecture/software-architecture/architectural-patterns/modular-decoupled-design/decoupled-architectures/container-based-isolation.md) — Packages models with specific library versions and drivers into containers to ensure reproducible performance.