--- title: "accelerate vs DeepLearningExamples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-accelerate-vs-nvidia-deeplearningexamples" tools: ["huggingface-accelerate", "nvidia-deeplearningexamples"] --- # accelerate vs DeepLearningExamples *GraphCanon updated Aug 17, 2026* ## Verdict Pick accelerate if tool: accelerate; pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings. [accelerate](https://huggingface.co/docs/accelerate) reports 9.8k GitHub stars, 1.4k forks, and 105 open issues, last pushed Jul 30, 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 [accelerate's repository](https://github.com/huggingface/accelerate) and [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples). | | [accelerate](/tools/huggingface-accelerate.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Tagline | A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support. | State-of-the-Art Deep Learning scripts for various applications | | Stars | 9,803 | 14,844 | | Forks | 1,425 | 3,408 | | Open issues | 105 | 321 | | Language | Python | Jupyter Notebook | | Adopt for | Tool: accelerate | 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._ | | [accelerate](/tools/huggingface-accelerate.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 3d | 734d | | Open issues (now) | 105 | 321 | | Stars delta | Unknown | +14 (30d) | | Open issues delta | Unknown | -1 (30d) | | Full report | [trust report](/tools/huggingface-accelerate/trust.md) | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | ## Decision facts: accelerate - **Adopt for:** Tool: accelerate ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Choose when ### Choose accelerate if… - accelerate is primarily Python; DeepLearningExamples is Jupyter Notebook. - Tags unique to accelerate: deepspeed, fsdp, mixed precision, pytorch. - Easy mixed-precision support for PyTorch models ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; accelerate is Python. - Tags unique to DeepLearningExamples: computer-vision, deep-learning, drug-discovery, forecasting. - 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 accelerate - Non-PyTorch projects do not benefit from this tool - Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow - Limited to Python environments compatible with PyTorch 1.10.0+ ## 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 accelerate and DeepLearningExamples? accelerate: A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support.. 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 accelerate over DeepLearningExamples? Choose accelerate over DeepLearningExamples when accelerate is primarily Python; DeepLearningExamples is Jupyter Notebook; Tags unique to accelerate: deepspeed, fsdp, mixed precision, pytorch; Easy mixed-precision support for PyTorch models. ### When should I choose DeepLearningExamples over accelerate? Choose DeepLearningExamples over accelerate when DeepLearningExamples is primarily Jupyter Notebook; accelerate is Python; Tags unique to DeepLearningExamples: computer-vision, deep-learning, drug-discovery, forecasting; 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 accelerate? Non-PyTorch projects do not benefit from this tool Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow Limited to Python environments compatible with PyTorch 1.10.0+ ### 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 accelerate or DeepLearningExamples more popular on GitHub? DeepLearningExamples has more GitHub stars (14,844 vs 9,803). Stars measure visibility, not whether either tool fits your constraints. ### Are accelerate and DeepLearningExamples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to accelerate or DeepLearningExamples? GraphCanon lists graph-backed alternatives at [accelerate alternatives](/tools/huggingface-accelerate/alternatives) and [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) ([accelerate markdown twin](/tools/huggingface-accelerate/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/huggingface-accelerate-vs-nvidia-deeplearningexamples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, accelerate or DeepLearningExamples? accelerate: 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 accelerate and DeepLearningExamples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [accelerate trust report](/tools/huggingface-accelerate/trust); [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-accelerate`](/api/graphcanon/graph?tool=huggingface-accelerate) - 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/_