open-r1 vs DeepLearningExamples

Common questions

What is the difference between open-r1 and DeepLearningExamples?

open-r1: Fully open reproduction of DeepSeek-R1. 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 open-r1 over DeepLearningExamples?

Choose open-r1 over DeepLearningExamples when open-r1 is primarily Python; DeepLearningExamples is Jupyter Notebook; Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.; Tags unique to open-r1: deepseek-r1, rl pipeline, vllm, python; Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.

When should I choose DeepLearningExamples over open-r1?

Choose DeepLearningExamples over open-r1 when DeepLearningExamples is primarily Jupyter Notebook; open-r1 is Python; Tags unique to DeepLearningExamples: mxnet, deep-learning, nlp, 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 open-r1?

Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch v2.6.0, as this may lead to errors. Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming.

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原

Is open-r1 or DeepLearningExamples more popular on GitHub?

open-r1 has more GitHub stars (26,401 vs 14,830). Stars measure visibility, not whether either tool fits your constraints.

Are open-r1 and DeepLearningExamples open source?

Yes - both are open-source projects on GitHub.

Where can I find alternatives to open-r1 or DeepLearningExamples?

GraphCanon lists graph-backed alternatives at open-r1 alternatives and DeepLearningExamples alternatives (open-r1 markdown twin, DeepLearningExamples markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, open-r1 or DeepLearningExamples?

open-r1: Slowing. 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 open-r1 and DeepLearningExamples?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; DeepLearningExamples trust report.