--- title: "torchtune vs DeepLearningExamples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/meta-pytorch-torchtune-vs-nvidia-deeplearningexamples" tools: ["meta-pytorch-torchtune", "nvidia-deeplearningexamples"] --- # torchtune vs DeepLearningExamples *GraphCanon updated Aug 17, 2026* ## Verdict Pick torchtune if a PyTorch-native post-training library focused on finetuning multimodal LLMs using state-of-the-art quantization techniques; pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings. [torchtune](https://pytorch.org/torchtune/main/) reports 5.8k GitHub stars, 743 forks, and 455 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 [torchtune's repository](https://github.com/meta-pytorch/torchtune) and [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples). | | [torchtune](/tools/meta-pytorch-torchtune.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Tagline | PyTorch native post-training library | State-of-the-Art Deep Learning scripts for various applications | | Stars | 5,793 | 14,844 | | Forks | 743 | 3,408 | | Open issues | 455 | 321 | | Language | Python | Jupyter Notebook | | Adopt for | A PyTorch-native post-training library focused on finetuning multimodal LLMs using state-of-the-art quantization techniques. | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | - | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [torchtune](/tools/meta-pytorch-torchtune.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 734d | | Open issues (now) | 455 | 321 | | Stars delta | Unknown | +14 (30d) | | Open issues delta | Unknown | -1 (30d) | | Full report | [trust report](/tools/meta-pytorch-torchtune/trust.md) | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | ## Decision facts: torchtune - **Adopt for:** A PyTorch-native post-training library focused on finetuning multimodal LLMs using state-of-the-art quantization techniques. ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Choose when ### Choose torchtune if… - torchtune is primarily Python; DeepLearningExamples is Jupyter Notebook. - Tags unique to torchtune: multimodal-llms, post-training, pytorch, quantization techniques. - - When you are working with the latest stable or preview nightly versions of PyTorch and need advanced finetuning for multimodal large language models (LLMs). ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; torchtune 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 torchtune - - If you rely on a fixed, older version of PyTorch as Torchtune only supports the latest stable and preview nightly versions. - - For scenarios where custom or non-PyTorch-native optimization methods are preferred over torchao’s quantization techniques. ## 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 torchtune and DeepLearningExamples? torchtune: PyTorch native post-training library. 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 torchtune over DeepLearningExamples? Choose torchtune over DeepLearningExamples when torchtune is primarily Python; DeepLearningExamples is Jupyter Notebook; Tags unique to torchtune: multimodal-llms, post-training, pytorch, quantization techniques; - When you are working with the latest stable or preview nightly versions of PyTorch and need advanced finetuning for multimodal large language models (LLMs). ### When should I choose DeepLearningExamples over torchtune? Choose DeepLearningExamples over torchtune when DeepLearningExamples is primarily Jupyter Notebook; torchtune 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 torchtune? - If you rely on a fixed, older version of PyTorch as Torchtune only supports the latest stable and preview nightly versions. - For scenarios where custom or non-PyTorch-native optimization methods are preferred over torchao’s quantization techniques. ### 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 torchtune or DeepLearningExamples more popular on GitHub? DeepLearningExamples has more GitHub stars (14,844 vs 5,793). Stars measure visibility, not whether either tool fits your constraints. ### Are torchtune and DeepLearningExamples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to torchtune or DeepLearningExamples? GraphCanon lists graph-backed alternatives at [torchtune alternatives](/tools/meta-pytorch-torchtune/alternatives) and [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) ([torchtune markdown twin](/tools/meta-pytorch-torchtune/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/meta-pytorch-torchtune-vs-nvidia-deeplearningexamples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, torchtune or DeepLearningExamples? torchtune: 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 torchtune and DeepLearningExamples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [torchtune trust report](/tools/meta-pytorch-torchtune/trust); [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=meta-pytorch-torchtune`](/api/graphcanon/graph?tool=meta-pytorch-torchtune) - 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/_