--- title: "comet-examples vs awesome-automl-papers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/comet-ml-comet-examples-vs-hibayesian-awesome-automl-papers" tools: ["comet-ml-comet-examples", "hibayesian-awesome-automl-papers"] --- # comet-examples vs awesome-automl-papers *GraphCanon updated Aug 4, 2026* ## Verdict Pick comet-examples if comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format; pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. [comet-examples](https://github.com/comet-ml/comet-examples) reports 176 GitHub stars, 67 forks, and 26 open issues, last pushed Jul 28, 2026. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) has 4.2k stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. Figures are from public GitHub metadata via [comet-examples's repository](https://github.com/comet-ml/comet-examples) and [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers). | | [comet-examples](/tools/comet-ml-comet-examples.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Tagline | Examples of Machine Learning code using Comet.ml | A curated list of automated machine learning papers and resources. | | Stars | 176 | 4,155 | | Forks | 67 | 678 | | Open issues | 26 | 2 | | Language | Jupyter Notebook | - | | Adopt for | Comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [comet-examples](/tools/comet-ml-comet-examples.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 784d | | Open issues (now) | 26 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/comet-ml-comet-examples/trust.md) | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | ## Decision facts: comet-examples - **Adopt for:** Comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. ## Decision facts: awesome-automl-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## Choose when ### Choose comet-examples if… - Tags unique to comet-examples: comet-ml, deep-learning-algorithms, machine-learning-platform, python. - When you are working with deep learning frameworks such as PyTorch or TensorFlow and want to integrate Comet.ml for experiment tracking and model management. - More recently updated (last pushed Jul 28, 2026). ### Choose awesome-automl-papers if… - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - When you need a curated list of academic materials to research or learn about AutoML technologies - More GitHub stars (4.2k vs 176) - visibility, not fit. ## When NOT to use comet-examples - If you are looking for a tool without third-party dependencies, as Comet-examples necessitates the use of Comet.ml which requires registration. - Avoid using this repository if your project does not need advanced experiment management features and simple code examples would suffice. ## When NOT to use awesome-automl-papers - If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources - When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ## Common questions ### What is the difference between comet-examples and awesome-automl-papers? comet-examples: Examples of Machine Learning code using Comet.ml. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose comet-examples over awesome-automl-papers? Choose comet-examples over awesome-automl-papers when Tags unique to comet-examples: comet-ml, deep-learning-algorithms, machine-learning-platform, python; When you are working with deep learning frameworks such as PyTorch or TensorFlow and want to integrate Comet.ml for experiment tracking and model management; More recently updated (last pushed Jul 28, 2026). ### When should I choose awesome-automl-papers over comet-examples? Choose awesome-automl-papers over comet-examples when Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies; More GitHub stars (4.2k vs 176) - visibility, not fit. ### When should I avoid comet-examples? If you are looking for a tool without third-party dependencies, as Comet-examples necessitates the use of Comet.ml which requires registration. Avoid using this repository if your project does not need advanced experiment management features and simple code examples would suffice. ### When should I avoid awesome-automl-papers? If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ### Is comet-examples or awesome-automl-papers more popular on GitHub? awesome-automl-papers has more GitHub stars (4,155 vs 176). Stars measure visibility, not whether either tool fits your constraints. ### Are comet-examples and awesome-automl-papers open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to comet-examples or awesome-automl-papers? GraphCanon lists graph-backed alternatives at [comet-examples alternatives](/tools/comet-ml-comet-examples/alternatives) and [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) ([comet-examples markdown twin](/tools/comet-ml-comet-examples/alternatives.md), [awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/comet-ml-comet-examples-vs-hibayesian-awesome-automl-papers.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, comet-examples or awesome-automl-papers? comet-examples: Very active. awesome-automl-papers: 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 comet-examples and awesome-automl-papers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [comet-examples trust report](/tools/comet-ml-comet-examples/trust); [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=comet-ml-comet-examples`](/api/graphcanon/graph?tool=comet-ml-comet-examples) - 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/_