--- title: "DeepLearningExamples vs openpi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-deeplearningexamples-vs-physical-intelligence-openpi" tools: ["nvidia-deeplearningexamples", "physical-intelligence-openpi"] --- # DeepLearningExamples vs openpi *GraphCanon updated Sep 20, 2026* ## Verdict Pick DeepLearningExamples if deepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N; pick openpi if openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) reports 15k GitHub stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. [openpi](https://github.com/Physical-Intelligence/openpi) has 14k stars, 2.5k forks, and 345 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples) and [openpi's repository](https://github.com/Physical-Intelligence/openpi). | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Tagline | State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure | Repository for running AI models with GPU requirements specified for inference and fine-tuning. | | Stars | 14,847 | 13,874 | | Forks | 3,406 | 2,467 | | Open issues | 321 | 345 | | Language | Jupyter Notebook | Python | | Adopt for | DeepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N | openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. | | Persona | - | - | | Runtime | - | - | | License | The license information for DeepLearningExamples is unknown. | Apache-2.0 | | Categories | Computer Vision, Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 766d | 24d | | Open issues (now) | 321 | 345 | | Stars delta | +3 (30d) | +776 (30d) | | Open issues delta | 0 (30d) | +29 (30d) | | Full report | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | [trust report](/tools/physical-intelligence-openpi/trust.md) | ## Decision facts: DeepLearningExamples - **Requirements:** Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS. - **Adopt for:** DeepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N - **License detail:** The license information for DeepLearningExamples is unknown. ## Decision facts: openpi - **Requirements:** The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning. - **Adopt for:** openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. ## Choose when ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; openpi is Python. - Requirements: Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS.. - Tags unique to DeepLearningExamples: computer-vision, deep-learning, drug-discovery, forecasting. - Also covers Computer Vision. - When you need deep learning scripts optimized for NVIDIA GPUs, including Volta, Turing, and Ampere architectures, for tasks like computer vision, NLP, and speech recognition. ### Choose openpi if… - openpi is primarily Python; DeepLearningExamples is Jupyter Notebook. - Requirements: The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning.. - Tags unique to openpi: fine-tuning, gpu, inference, lora. - Use openpi when you need to run AI models with precise GPU memory requirements for inference and fine-tuning, particularly with LoRA and full fine-tuning modes. ## When NOT to use DeepLearningExamples - If your infrastructure does not include NVIDIA GPUs, as the scripts are specifically optimized for NVIDIA hardware. - If you are looking for a tool that supports a wider range of hardware or non-NVIDIA GPU environments. - When you need a solution that is not tied to specific frameworks like PyTorch, TensorFlow, or PaddlePaddle, as DeepLearningExamples focuses on these frameworks. ## When NOT to use openpi - Avoid using openpi if your project requires multi-node training, as the current training script does not support this feature. - Do not use openpi if you are working on an operating system other than Ubuntu 22.04, as the repository has not been tested with other systems. ## Common questions ### What is the difference between DeepLearningExamples and openpi? DeepLearningExamples: State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure. openpi: Repository for running AI models with GPU requirements specified for inference and fine-tuning.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepLearningExamples over openpi? Choose DeepLearningExamples over openpi when DeepLearningExamples is primarily Jupyter Notebook; openpi is Python; Requirements: Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS.; Tags unique to DeepLearningExamples: computer-vision, deep-learning, drug-discovery, forecasting; Also covers Computer Vision; When you need deep learning scripts optimized for NVIDIA GPUs, including Volta, Turing, and Ampere architectures, for tasks like computer vision, NLP, and speech recognition. ### When should I choose openpi over DeepLearningExamples? Choose openpi over DeepLearningExamples when openpi is primarily Python; DeepLearningExamples is Jupyter Notebook; Requirements: The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning.; Tags unique to openpi: fine-tuning, gpu, inference, lora; Use openpi when you need to run AI models with precise GPU memory requirements for inference and fine-tuning, particularly with LoRA and full fine-tuning modes. ### When should I avoid DeepLearningExamples? If your infrastructure does not include NVIDIA GPUs, as the scripts are specifically optimized for NVIDIA hardware. If you are looking for a tool that supports a wider range of hardware or non-NVIDIA GPU environments. When you need a solution that is not tied to specific frameworks like PyTorch, TensorFlow, or PaddlePaddle, as DeepLearningExamples focuses on these frameworks. ### When should I avoid openpi? Avoid using openpi if your project requires multi-node training, as the current training script does not support this feature. Do not use openpi if you are working on an operating system other than Ubuntu 22.04, as the repository has not been tested with other systems. ### Is DeepLearningExamples or openpi more popular on GitHub? DeepLearningExamples has more GitHub stars (14,847 vs 13,874). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepLearningExamples and openpi open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DeepLearningExamples or openpi? GraphCanon lists graph-backed alternatives at [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) and [openpi alternatives](/tools/physical-intelligence-openpi/alternatives) ([DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/alternatives.md), [openpi markdown twin](/tools/physical-intelligence-openpi/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/nvidia-deeplearningexamples-vs-physical-intelligence-openpi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepLearningExamples or openpi? DeepLearningExamples: Dormant. openpi: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for DeepLearningExamples and openpi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust); [openpi trust report](/tools/physical-intelligence-openpi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-deeplearningexamples`](/api/graphcanon/graph?tool=nvidia-deeplearningexamples) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_