--- title: "llm-course vs onepanel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-onepanelio-onepanel" tools: ["mlabonne-llm-course", "onepanelio-onepanel"] --- # llm-course vs onepanel *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-course when requirements: Course materials are available in Colab notebooks; access requires a Google account; pick onepanel when tags unique to onepanel: ai, aiops, annotation, deeplearning. [llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. [onepanel](https://docs.onepanel.ai/) has 730 stars, 73 forks, and 102 open issues, last pushed Feb 25, 2023. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [onepanel's repository](https://github.com/onepanelio/onepanel). | | [llm-course](/tools/mlabonne-llm-course.md) | [onepanel](/tools/onepanelio-onepanel.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises. | | Stars | 80,839 | 730 | | Forks | 9,421 | 73 | | Open issues | 84 | 102 | | Language | - | Go | | Adopt for | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | Model Training, Vector Databases, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [onepanel](/tools/onepanelio-onepanel.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 155d | 1232d | | Open issues (now) | 84 | 102 | | Owner type | User | Organization | | Security scan | No lockfile | 113 low (113 low) | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/onepanelio-onepanel/trust.md) | ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose llm-course if… - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models. - Also covers LLM Frameworks, Evaluation & Observability. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ### Choose onepanel if… - Tags unique to onepanel: ai, aiops, annotation, deeplearning. - Also covers Vector Databases. - onepanel ships Docker support for self-hosted deployment. ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## When NOT to use onepanel - Last GitHub push was 1233 days ago (dormant maintenance, Feb 25, 2023). Validate activity before betting a new project on onepanel. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between llm-course and onepanel? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. onepanel: The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over onepanel? Choose llm-course over onepanel when Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models; Also covers LLM Frameworks, Evaluation & Observability; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I choose onepanel over llm-course? Choose onepanel over llm-course when Tags unique to onepanel: ai, aiops, annotation, deeplearning; Also covers Vector Databases; onepanel ships Docker support for self-hosted deployment. ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### When should I avoid onepanel? Last GitHub push was 1233 days ago (dormant maintenance, Feb 25, 2023). Validate activity before betting a new project on onepanel. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is llm-course or onepanel more popular on GitHub? llm-course has more GitHub stars (80,839 vs 730). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and onepanel open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, onepanel: Apache-2.0). ### Where can I find alternatives to llm-course or onepanel? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [onepanel alternatives](/tools/onepanelio-onepanel/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [onepanel markdown twin](/tools/onepanelio-onepanel/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/mlabonne-llm-course-vs-onepanelio-onepanel.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or onepanel? llm-course: Slowing. onepanel: 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 llm-course and onepanel? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [onepanel trust report](/tools/onepanelio-onepanel/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course) - 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/_