--- title: "lightly-train vs DeepLearningExamples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lightly-ai-lightly-train-vs-nvidia-deeplearningexamples" tools: ["lightly-ai-lightly-train", "nvidia-deeplearningexamples"] --- # lightly-train vs DeepLearningExamples *GraphCanon updated Sep 20, 2026* ## Verdict Pick lightly-train if lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation; 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. [lightly-train](https://docs.lightly.ai/train) reports 1.7k GitHub stars, 116 forks, and 82 open issues, last pushed Sep 14, 2026. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) has 15k stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. Figures are from public GitHub metadata via [lightly-train's repository](https://github.com/lightly-ai/lightly-train) and [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples). | | [lightly-train](/tools/lightly-ai-lightly-train.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Tagline | All-in-one training for vision models: pretraining, fine-tuning, distillation. | State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure | | Stars | 1,680 | 14,847 | | Forks | 116 | 3,406 | | Open issues | 82 | 321 | | Language | Python | Jupyter Notebook | | Adopt for | Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. | 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 | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | The license information for DeepLearningExamples is unknown. | | Categories | Computer Vision, Model Training | Computer Vision, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lightly-train](/tools/lightly-ai-lightly-train.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 766d | | Open issues (now) | 82 | 321 | | Stars delta | +58 (30d) | +3 (30d) | | Open issues delta | +19 (30d) | 0 (30d) | | Full report | [trust report](/tools/lightly-ai-lightly-train/trust.md) | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | ## Decision facts: lightly-train - **Requirements:** Min 8 GB RAM - **Adopt for:** Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. ## 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. ## Choose when ### Choose lightly-train if… - lightly-train is primarily Python; DeepLearningExamples is Jupyter Notebook. - Requirements: Min 8 GB RAM. - Tags unique to lightly-train: contrastive-learning, depth-estimation, dinov2, dinov3. - Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; lightly-train 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: drug-discovery, forecasting, large-language-models, mxnet. - Also covers Inference & Serving. - 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 NOT to use lightly-train - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## 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. ## Common questions ### What is the difference between lightly-train and DeepLearningExamples? lightly-train: All-in-one training for vision models: pretraining, fine-tuning, distillation.. DeepLearningExamples: State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure. See the comparison table for live GitHub stats and shared categories. ### When should I choose lightly-train over DeepLearningExamples? Choose lightly-train over DeepLearningExamples when lightly-train is primarily Python; DeepLearningExamples is Jupyter Notebook; Requirements: Min 8 GB RAM; Tags unique to lightly-train: contrastive-learning, depth-estimation, dinov2, dinov3; Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. ### When should I choose DeepLearningExamples over lightly-train? Choose DeepLearningExamples over lightly-train when DeepLearningExamples is primarily Jupyter Notebook; lightly-train 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: drug-discovery, forecasting, large-language-models, mxnet; Also covers Inference & Serving; 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 avoid lightly-train? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### 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. ### Is lightly-train or DeepLearningExamples more popular on GitHub? DeepLearningExamples has more GitHub stars (14,847 vs 1,680). Stars measure visibility, not whether either tool fits your constraints. ### Are lightly-train and DeepLearningExamples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to lightly-train or DeepLearningExamples? GraphCanon lists graph-backed alternatives at [lightly-train alternatives](/tools/lightly-ai-lightly-train/alternatives) and [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) ([lightly-train markdown twin](/tools/lightly-ai-lightly-train/alternatives.md), [DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/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/lightly-ai-lightly-train-vs-nvidia-deeplearningexamples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lightly-train or DeepLearningExamples? lightly-train: Very active. DeepLearningExamples: Dormant. 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 lightly-train and DeepLearningExamples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lightly-train trust report](/tools/lightly-ai-lightly-train/trust); [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lightly-ai-lightly-train`](/api/graphcanon/graph?tool=lightly-ai-lightly-train) - 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/_