--- title: "DeepLearningExamples vs pytorch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-deeplearningexamples-vs-pytorch-pytorch" tools: ["nvidia-deeplearningexamples", "pytorch-pytorch"] --- # DeepLearningExamples vs pytorch *GraphCanon updated Aug 17, 2026* ## Verdict Pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings; pick pytorch if dynamic computation graphs with GPU acceleration. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) reports 15k GitHub stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. [pytorch](https://pytorch.org) has 102k stars, 29k forks, and 18k open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples) and [pytorch's repository](https://github.com/pytorch/pytorch). | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [pytorch](/tools/pytorch-pytorch.md) | | --- | --- | --- | | Tagline | State-of-the-Art Deep Learning scripts for various applications | Tensors and Dynamic neural networks in Python with strong GPU acceleration | | Stars | 14,844 | 102,144 | | Forks | 3,408 | 28,650 | | Open issues | 321 | 18,389 | | Language | Jupyter Notebook | Python | | Adopt for | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | Dynamic computation graphs with GPU acceleration. | | Persona | - | - | | Runtime | - | - | | License | - | Other | | Categories | 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) | [pytorch](/tools/pytorch-pytorch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 734d | 0d | | Open issues (now) | 321 | 18k | | Stars delta | +14 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Full report | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | [trust report](/tools/pytorch-pytorch/trust.md) | **Typed relationship:** DeepLearningExamples _(integrates with)_ pytorch The DeepLearningExamples by NVIDIA may make use of or build upon PyTorch to provide training scripts for state-of-the-art deep learning models. ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Decision facts: pytorch - **Adopt for:** Dynamic computation graphs with GPU acceleration. ## Choose when ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; pytorch is Python. - The DeepLearningExamples by NVIDIA may make use of or build upon PyTorch to provide training scripts for state-of-the-art deep learning models. - Tags unique to DeepLearningExamples: computer-vision, drug-discovery, forecasting, large language models. - The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance. ### Choose pytorch if… - pytorch is primarily Python; DeepLearningExamples is Jupyter Notebook. - The DeepLearningExamples by NVIDIA may make use of or build upon PyTorch to provide training scripts for state-of-the-art deep learning models. - Tags unique to pytorch: autograd, gpu, machine-learning, neural-network. - pytorch ships Docker support for self-hosted deployment. - Required dynamic computation graph functionality for flexible model architectures ## When NOT to use DeepLearningExamples - Avoid using DeepLearningExamples if you do not have access to NVIDIA GPUs, as it is heavily optimized for these specific hardware configurations to provide maximum utilization of Tensor Cores. - If your project requires frameworks that are less common (e.g., MXNet or PaddlePaddle) without the same level of support as PyTorch and TensorFlow on this platform, consider other repositories that n ## When NOT to use pytorch - Static graph frameworks like TensorFlow are preferred for simpler, less variable models - Environments with limited GPU support or requiring multi-language compatibility ## Common questions ### What is the difference between DeepLearningExamples and pytorch? DeepLearningExamples: State-of-the-Art Deep Learning scripts for various applications. pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepLearningExamples over pytorch? Choose DeepLearningExamples over pytorch when DeepLearningExamples is primarily Jupyter Notebook; pytorch is Python; The DeepLearningExamples by NVIDIA may make use of or build upon PyTorch to provide training scripts for state-of-the-art deep learning models; Tags unique to DeepLearningExamples: computer-vision, drug-discovery, forecasting, large language models; The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance. ### When should I choose pytorch over DeepLearningExamples? Choose pytorch over DeepLearningExamples when pytorch is primarily Python; DeepLearningExamples is Jupyter Notebook; The DeepLearningExamples by NVIDIA may make use of or build upon PyTorch to provide training scripts for state-of-the-art deep learning models; Tags unique to pytorch: autograd, gpu, machine-learning, neural-network; pytorch ships Docker support for self-hosted deployment; Required dynamic computation graph functionality for flexible model architectures. ### When should I avoid DeepLearningExamples? Avoid using DeepLearningExamples if you do not have access to NVIDIA GPUs, as it is heavily optimized for these specific hardware configurations to provide maximum utilization of Tensor Cores. If your project requires frameworks that are less common (e.g., MXNet or PaddlePaddle) without the same level of support as PyTorch and TensorFlow on this platform, consider other repositories that n ### When should I avoid pytorch? Static graph frameworks like TensorFlow are preferred for simpler, less variable models Environments with limited GPU support or requiring multi-language compatibility ### Is DeepLearningExamples or pytorch more popular on GitHub? pytorch has more GitHub stars (102,144 vs 14,844). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepLearningExamples and pytorch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DeepLearningExamples or pytorch? GraphCanon lists graph-backed alternatives at [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) and [pytorch alternatives](/tools/pytorch-pytorch/alternatives) ([DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/alternatives.md), [pytorch markdown twin](/tools/pytorch-pytorch/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-pytorch-pytorch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepLearningExamples or pytorch? DeepLearningExamples: Dormant. pytorch: Very 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 pytorch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust); [pytorch trust report](/tools/pytorch-pytorch/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/_