--- title: "DeepSpeed vs DeepLearningExamples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-nvidia-deeplearningexamples" tools: ["deepspeedai-deepspeed", "nvidia-deeplearningexamples"] --- # DeepSpeed vs DeepLearningExamples *GraphCanon updated Aug 17, 2026* ## Verdict Pick DeepSpeed if decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression; pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Aug 6, 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 [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | State-of-the-Art Deep Learning scripts for various applications | | Stars | 42,870 | 14,844 | | Forks | 4,920 | 3,408 | | Open issues | 1,308 | 321 | | Language | Python | Jupyter Notebook | | Adopt for | Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression. | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 734d | | Open issues (now) | 1.3k | 321 | | Stars delta | Unknown | +14 (30d) | | Open issues delta | Unknown | -1 (30d) | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | ## Decision facts: DeepSpeed - **Adopt for:** Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression. ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Choose when ### Choose DeepSpeed if… - DeepSpeed is primarily Python; DeepLearningExamples is Jupyter Notebook. - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; DeepSpeed is Python. - 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 NOT to use DeepSpeed - - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively ## 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 DeepSpeed and DeepLearningExamples? DeepSpeed: Deep learning optimization library for efficient distributed training and inference. 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 DeepSpeed over DeepLearningExamples? Choose DeepSpeed over DeepLearningExamples when DeepSpeed is primarily Python; DeepLearningExamples is Jupyter Notebook; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters). ### When should I choose DeepLearningExamples over DeepSpeed? Choose DeepLearningExamples over DeepSpeed when DeepLearningExamples is primarily Jupyter Notebook; DeepSpeed is Python; 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 avoid DeepSpeed? - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively ### 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 DeepSpeed or DeepLearningExamples more popular on GitHub? DeepSpeed has more GitHub stars (42,870 vs 14,844). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSpeed and DeepLearningExamples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DeepSpeed or DeepLearningExamples? GraphCanon lists graph-backed alternatives at [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) and [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/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/deepspeedai-deepspeed-vs-nvidia-deeplearningexamples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSpeed or DeepLearningExamples? DeepSpeed: 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 DeepSpeed and DeepLearningExamples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust); [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepspeedai-deepspeed`](/api/graphcanon/graph?tool=deepspeedai-deepspeed) - 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/_