--- title: "HPOBench vs Awesome-AI-Data-Guided-Projects" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-hpobench-vs-youssefhosni-awesome-ai-data-guided-projects" tools: ["automl-hpobench", "youssefhosni-awesome-ai-data-guided-projects"] --- # HPOBench vs Awesome-AI-Data-Guided-Projects *GraphCanon updated Oct 2, 2026* ## Verdict Pick HPOBench if hPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios; pick Awesome-AI-Data-Guided-Projects if awesome-AI-Data-Guided-Projects is a curated list featuring projects for building conversational chatbots using large language models and fine-tuning LLMs with LoRA, suitable for portfolio-building in AI. [HPOBench](https://github.com/automl/HPOBench) reports 173 GitHub stars, 36 forks, and 34 open issues, last pushed May 21, 2025. [Awesome-AI-Data-Guided-Projects](https://github.com/youssefHosni/Awesome-AI-Data-Guided-Projects) has 724 stars, 152 forks, and 3 open issues, last pushed May 5, 2024. Figures are from public GitHub metadata via [HPOBench's repository](https://github.com/automl/HPOBench) and [Awesome-AI-Data-Guided-Projects's repository](https://github.com/youssefHosni/Awesome-AI-Data-Guided-Projects). | | [HPOBench](/tools/automl-hpobench.md) | [Awesome-AI-Data-Guided-Projects](/tools/youssefhosni-awesome-ai-data-guided-projects.md) | | --- | --- | --- | | Tagline | A collection of hyperparameter optimization benchmark problems | A curated list of data science & AI guided projects for portfolio-building | | Stars | 173 | 724 | | Forks | 36 | 152 | | Open issues | 34 | 3 | | Language | Python | - | | Adopt for | HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. | Awesome-AI-Data-Guided-Projects is a curated list featuring projects for building conversational chatbots using large language models and fine-tuning LLMs with LoRA, suitable for portfolio-building in AI. | | Persona | - | - | | Runtime | - | - | | License | HPOBench is open source under the Apache-2.0 license. | GPL-3.0 License allows free use for personal and commercial purposes but requires users to make their modifications available under the same license terms. | | Categories | Model Training | Developer Tools, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HPOBench](/tools/automl-hpobench.md) | [Awesome-AI-Data-Guided-Projects](/tools/youssefhosni-awesome-ai-data-guided-projects.md) | | --- | --- | --- | | Days since push | 498d | 870d | | Open issues (now) | 34 | 3 | | Stars delta | +3 (30d) | +1 (30d) | | Open issues delta | 0 (30d) | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-hpobench/trust.md) | [trust report](/tools/youssefhosni-awesome-ai-data-guided-projects/trust.md) | ## Decision facts: HPOBench - **Pricing:** freemium - **Requirements:** The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step. - **Adopt for:** HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. - **License detail:** HPOBench is open source under the Apache-2.0 license. ## Decision facts: Awesome-AI-Data-Guided-Projects - **Adopt for:** Awesome-AI-Data-Guided-Projects is a curated list featuring projects for building conversational chatbots using large language models and fine-tuning LLMs with LoRA, suitable for portfolio-building in AI. - **License detail:** GPL-3.0 License allows free use for personal and commercial purposes but requires users to make their modifications available under the same license terms. ## Choose when ### Choose HPOBench if… - License: HPOBench is Apache-2.0, Awesome-AI-Data-Guided-Projects is GPL-3.0. - Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step.. - Tags unique to HPOBench: automl, bayesian-optimization, benchmark, hyperparameter-optimization. - When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ### Choose Awesome-AI-Data-Guided-Projects if… - License: Awesome-AI-Data-Guided-Projects is GPL-3.0, HPOBench is Apache-2.0. - Tags unique to Awesome-AI-Data-Guided-Projects: ai, computer-vision, datascience, deep-learning. - Also covers Developer Tools, LLM Frameworks. - You need guided projects to build conversational chatbot applications. ## When NOT to use HPOBench - Avoid HPOBench if your project does not require Python or you are looking for a platform that exclusively focuses on the automation of model selection without hyperparameter optimization. - If you prefer tools with built-in support for multiple programming languages, rather than focusing solely on Python as is the case with HPOBench. ## When NOT to use Awesome-AI-Data-Guided-Projects - Looking for end-to-end LLM training from scratch; this tool focuses more on fine-tuning and guided projects. - In search of proprietary AI tools or custom enterprise solutions, as Awesome-AI-Data-Guided-Projects offers open-source project guides. ## Common questions ### What is the difference between HPOBench and Awesome-AI-Data-Guided-Projects? HPOBench: A collection of hyperparameter optimization benchmark problems. Awesome-AI-Data-Guided-Projects: A curated list of data science & AI guided projects for portfolio-building. See the comparison table for live GitHub stats and shared categories. ### When should I choose HPOBench over Awesome-AI-Data-Guided-Projects? Choose HPOBench over Awesome-AI-Data-Guided-Projects when License: HPOBench is Apache-2.0, Awesome-AI-Data-Guided-Projects is GPL-3.0; Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step.; Tags unique to HPOBench: automl, bayesian-optimization, benchmark, hyperparameter-optimization; When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ### When should I choose Awesome-AI-Data-Guided-Projects over HPOBench? Choose Awesome-AI-Data-Guided-Projects over HPOBench when License: Awesome-AI-Data-Guided-Projects is GPL-3.0, HPOBench is Apache-2.0; Tags unique to Awesome-AI-Data-Guided-Projects: ai, computer-vision, datascience, deep-learning; Also covers Developer Tools, LLM Frameworks; You need guided projects to build conversational chatbot applications. ### When should I avoid HPOBench? Avoid HPOBench if your project does not require Python or you are looking for a platform that exclusively focuses on the automation of model selection without hyperparameter optimization. If you prefer tools with built-in support for multiple programming languages, rather than focusing solely on Python as is the case with HPOBench. ### When should I avoid Awesome-AI-Data-Guided-Projects? Looking for end-to-end LLM training from scratch; this tool focuses more on fine-tuning and guided projects. In search of proprietary AI tools or custom enterprise solutions, as Awesome-AI-Data-Guided-Projects offers open-source project guides. ### Is HPOBench or Awesome-AI-Data-Guided-Projects more popular on GitHub? Awesome-AI-Data-Guided-Projects has more GitHub stars (724 vs 173). Stars measure visibility, not whether either tool fits your constraints. ### Are HPOBench and Awesome-AI-Data-Guided-Projects open source? Yes - both are open-source projects on GitHub (HPOBench: Apache-2.0, Awesome-AI-Data-Guided-Projects: GPL-3.0). ### Where can I find alternatives to HPOBench or Awesome-AI-Data-Guided-Projects? GraphCanon lists graph-backed alternatives at [HPOBench alternatives](/tools/automl-hpobench/alternatives) and [Awesome-AI-Data-Guided-Projects alternatives](/tools/youssefhosni-awesome-ai-data-guided-projects/alternatives) ([HPOBench markdown twin](/tools/automl-hpobench/alternatives.md), [Awesome-AI-Data-Guided-Projects markdown twin](/tools/youssefhosni-awesome-ai-data-guided-projects/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/automl-hpobench-vs-youssefhosni-awesome-ai-data-guided-projects.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, HPOBench or Awesome-AI-Data-Guided-Projects? HPOBench: Dormant. Awesome-AI-Data-Guided-Projects: 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 HPOBench and Awesome-AI-Data-Guided-Projects? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HPOBench trust report](/tools/automl-hpobench/trust); [Awesome-AI-Data-Guided-Projects trust report](/tools/youssefhosni-awesome-ai-data-guided-projects/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-hpobench`](/api/graphcanon/graph?tool=automl-hpobench) - 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/_