--- title: "contrastors vs DeepLearningExamples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nomic-ai-contrastors-vs-nvidia-deeplearningexamples" tools: ["nomic-ai-contrastors", "nvidia-deeplearningexamples"] --- # contrastors vs DeepLearningExamples *GraphCanon updated Aug 22, 2026* ## Verdict Pick contrastors if contrastors is a Python library that leverages PyTorch for training contrastive learning models, ideal for tasks requiring dense retrieval or embeddings creation from text and images; pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings. [contrastors](https://github.com/nomic-ai/contrastors) reports 801 GitHub stars, 65 forks, and 16 open issues, last pushed Mar 26, 2025. [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 [contrastors's repository](https://github.com/nomic-ai/contrastors) and [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples). | | [contrastors](/tools/nomic-ai-contrastors.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Tagline | Train Models Contrastively in Pytorch | State-of-the-Art Deep Learning scripts for various applications | | Stars | 801 | 14,844 | | Forks | 65 | 3,408 | | Open issues | 16 | 321 | | Language | Python | Jupyter Notebook | | Adopt for | Contrastors is a Python library that leverages PyTorch for training contrastive learning models, ideal for tasks requiring dense retrieval or embeddings creation from text and images. | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [contrastors](/tools/nomic-ai-contrastors.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Days since push | 513d | 734d | | Open issues (now) | 16 | 321 | | Stars delta | +3 (30d) | +14 (30d) | | Open issues delta | 0 (30d) | -1 (30d) | | Full report | [trust report](/tools/nomic-ai-contrastors/trust.md) | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | ## Decision facts: contrastors - **Adopt for:** Contrastors is a Python library that leverages PyTorch for training contrastive learning models, ideal for tasks requiring dense retrieval or embeddings creation from text and images. ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Choose when ### Choose contrastors if… - contrastors is primarily Python; DeepLearningExamples is Jupyter Notebook. - Tags unique to contrastors: contrastive-learning, dense-retrieval, embeddings, image-embeddings. - * Use Contrastors when you are working with multimodal data (text and image) and require generating effective embeddings for them. ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; contrastors is Python. - Tags unique to DeepLearningExamples: computer-vision, drug-discovery, forecasting, large language models. - Also covers Inference & Serving. - The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance. ## When NOT to use contrastors - * Do not use Contrastors if your preferred framework is TensorFlow or another non-PyTorch-based deep learning solution. - * Avoid Contrastors if you are working with data modalities that are not text or image, as its strengths are in these domains. ## 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 ## Common questions ### What is the difference between contrastors and DeepLearningExamples? contrastors: Train Models Contrastively in Pytorch. DeepLearningExamples: State-of-the-Art Deep Learning scripts for various applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose contrastors over DeepLearningExamples? Choose contrastors over DeepLearningExamples when contrastors is primarily Python; DeepLearningExamples is Jupyter Notebook; Tags unique to contrastors: contrastive-learning, dense-retrieval, embeddings, image-embeddings; * Use Contrastors when you are working with multimodal data (text and image) and require generating effective embeddings for them. ### When should I choose DeepLearningExamples over contrastors? Choose DeepLearningExamples over contrastors when DeepLearningExamples is primarily Jupyter Notebook; contrastors is Python; Tags unique to DeepLearningExamples: computer-vision, drug-discovery, forecasting, large language models; Also covers Inference & Serving; 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 avoid contrastors? * Do not use Contrastors if your preferred framework is TensorFlow or another non-PyTorch-based deep learning solution. * Avoid Contrastors if you are working with data modalities that are not text or image, as its strengths are in these domains. ### 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 ### Is contrastors or DeepLearningExamples more popular on GitHub? DeepLearningExamples has more GitHub stars (14,844 vs 801). Stars measure visibility, not whether either tool fits your constraints. ### Are contrastors and DeepLearningExamples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to contrastors or DeepLearningExamples? GraphCanon lists graph-backed alternatives at [contrastors alternatives](/tools/nomic-ai-contrastors/alternatives) and [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) ([contrastors markdown twin](/tools/nomic-ai-contrastors/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/nomic-ai-contrastors-vs-nvidia-deeplearningexamples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, contrastors or DeepLearningExamples? contrastors: Dormant. 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 contrastors and DeepLearningExamples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [contrastors trust report](/tools/nomic-ai-contrastors/trust); [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nomic-ai-contrastors`](/api/graphcanon/graph?tool=nomic-ai-contrastors) - 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/_