--- title: "DeepLearningExamples vs geti_v2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-deeplearningexamples-vs-open-edge-platform-geti-v2" tools: ["nvidia-deeplearningexamples", "open-edge-platform-geti-v2"] --- # DeepLearningExamples vs geti_v2 *GraphCanon updated Aug 17, 2026* ## Verdict Pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings; pick geti_v2 if geti_v2 is designed for developers who need to build computer vision models quickly using limited datasets. It supports TypeScript and integrates with frameworks like OpenVINO. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) reports 15k GitHub stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. [geti_v2](https://docs.geti.intel.com/docs/2.0/user-guide/getting-started/introduction) has 484 stars, 51 forks, and 86 open issues, last pushed Jul 24, 2026. Figures are from public GitHub metadata via [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples) and [geti_v2's repository](https://github.com/open-edge-platform/geti_v2). | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [geti_v2](/tools/open-edge-platform-geti-v2.md) | | --- | --- | --- | | Tagline | State-of-the-Art Deep Learning scripts for various applications | Build computer vision models quickly with less data | | Stars | 14,844 | 484 | | Forks | 3,408 | 51 | | Open issues | 321 | 86 | | Language | Jupyter Notebook | TypeScript | | Adopt for | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | geti_v2 is designed for developers who need to build computer vision models quickly using limited datasets. It supports TypeScript and integrates with frameworks like OpenVINO. | | Persona | - | - | | Runtime | - | - | | License | - | The licensing type is listed as 'Other', implying that the license details should be closely reviewed for specific terms. | | Categories | Inference & Serving, Model Training | Computer Vision, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [geti_v2](/tools/open-edge-platform-geti-v2.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 734d | 0d | | Open issues (now) | 321 | 86 | | Stars delta | +14 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Full report | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | [trust report](/tools/open-edge-platform-geti-v2/trust.md) | ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Decision facts: geti_v2 - **Pricing:** unknown - Pricing information is not provided. - **Requirements:** Min 0 GB RAM - **Adopt for:** geti_v2 is designed for developers who need to build computer vision models quickly using limited datasets. It supports TypeScript and integrates with frameworks like OpenVINO. - **License detail:** The licensing type is listed as 'Other', implying that the license details should be closely reviewed for specific terms. ## Choose when ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; geti_v2 is TypeScript. - Tags unique to DeepLearningExamples: drug-discovery, forecasting, large language models, mxnet. - The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance. ### Choose geti_v2 if… - geti_v2 is primarily TypeScript; DeepLearningExamples is Jupyter Notebook. - Pricing: Pricing information is not provided.. - Requirements: Min 0 GB RAM. - Tags unique to geti_v2: fine-tuning, inference. - Also covers Computer Vision. - When you have a shortage of labeled data but still require high accuracy in your computer vision model. ## 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 geti_v2 - When you need to work with languages other than TypeScript, as geti_v2 is specifically designed for use with TypeScript environments. - In scenarios where you have abundant labeled data and can afford longer training times, which may not leverage the key advantage of geti_v2's efficiency in low-data conditions. ## Common questions ### What is the difference between DeepLearningExamples and geti_v2? DeepLearningExamples: State-of-the-Art Deep Learning scripts for various applications. geti_v2: Build computer vision models quickly with less data. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepLearningExamples over geti_v2? Choose DeepLearningExamples over geti_v2 when DeepLearningExamples is primarily Jupyter Notebook; geti_v2 is TypeScript; Tags unique to DeepLearningExamples: drug-discovery, forecasting, large language models, mxnet; 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 geti_v2 over DeepLearningExamples? Choose geti_v2 over DeepLearningExamples when geti_v2 is primarily TypeScript; DeepLearningExamples is Jupyter Notebook; Pricing: Pricing information is not provided.; Requirements: Min 0 GB RAM; Tags unique to geti_v2: fine-tuning, inference; Also covers Computer Vision; When you have a shortage of labeled data but still require high accuracy in your computer vision model. ### 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 geti_v2? When you need to work with languages other than TypeScript, as geti_v2 is specifically designed for use with TypeScript environments. In scenarios where you have abundant labeled data and can afford longer training times, which may not leverage the key advantage of geti_v2's efficiency in low-data conditions. ### Is DeepLearningExamples or geti_v2 more popular on GitHub? DeepLearningExamples has more GitHub stars (14,844 vs 484). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepLearningExamples and geti_v2 open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DeepLearningExamples or geti_v2? GraphCanon lists graph-backed alternatives at [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) and [geti_v2 alternatives](/tools/open-edge-platform-geti-v2/alternatives) ([DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/alternatives.md), [geti_v2 markdown twin](/tools/open-edge-platform-geti-v2/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-open-edge-platform-geti-v2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepLearningExamples or geti_v2? DeepLearningExamples: Dormant. geti_v2: 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 geti_v2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust); [geti_v2 trust report](/tools/open-edge-platform-geti-v2/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/_