--- title: "ColossalAI vs Keras-TextClassification" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-yongzhuo-keras-textclassification" tools: ["hpcaitech-colossalai", "yongzhuo-keras-textclassification"] --- # ColossalAI vs Keras-TextClassification *GraphCanon updated Aug 22, 2026* ## Verdict Pick ColossalAI if colossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models; pick Keras-TextClassification if chinese-focused text classification models using Keras. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 505 open issues, last pushed Jul 13, 2026. [Keras-TextClassification](https://blog.csdn.net/rensihui) has 1.8k stars, 397 forks, and 4 open issues, last pushed Jun 17, 2024. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [Keras-TextClassification's repository](https://github.com/yongzhuo/Keras-TextClassification). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [Keras-TextClassification](/tools/yongzhuo-keras-textclassification.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | Chinese text classification models based on Keras | | Stars | 41,432 | 1,809 | | Forks | 4,506 | 397 | | Open issues | 505 | 4 | | Language | Python | Python | | Adopt for | ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. | Chinese-focused text classification models using Keras. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License - allows free use, modification, distribution with attribution required but no guarantee or liability from contributors. | | Categories | Inference & Serving, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [Keras-TextClassification](/tools/yongzhuo-keras-textclassification.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 24d | 795d | | Open issues (now) | 505 | 4 | | Stars delta | Unknown | -3 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/yongzhuo-keras-textclassification/trust.md) | ## Shared compatibility - **Python**: [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime; [Keras-TextClassification](/tools/yongzhuo-keras-textclassification.md) - Python runtime ## Decision facts: ColossalAI - **Adopt for:** ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. ## Decision facts: Keras-TextClassification - **Requirements:** Min 4 GB RAM; Python environment must be prepared for running Keras.; Supports multiple model types requiring different levels of computation resources. - **Adopt for:** Chinese-focused text classification models using Keras. - **License detail:** MIT License - allows free use, modification, distribution with attribution required but no guarantee or liability from contributors. ## Choose when ### Choose ColossalAI if… - License: ColossalAI is Apache-2.0, Keras-TextClassification is MIT. - Tags unique to ColossalAI: ai, big model, data-parallelism, deep-learning. - Also covers Inference & Serving. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### Choose Keras-TextClassification if… - License: Keras-TextClassification is MIT, ColossalAI is Apache-2.0. - Requirements: Min 4 GB RAM; Python environment must be prepared for running Keras.; Supports multiple model types requiring different levels of computation resources.. - Tags unique to Keras-TextClassification: albert, bert, capsulenetwork, charcnn. - Requires Chinese text classification for tasks like multi-label, sentence similarity analysis. ## When NOT to use ColossalAI - You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. - Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). - You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ## When NOT to use Keras-TextClassification - Does not cater to non-Chinese language datasets effectively due to its Chinese-specific models. - Avoid if you seek a tool with extensive support beyond text classification like NER or POS tagging. ## Common questions ### What is the difference between ColossalAI and Keras-TextClassification? ColossalAI: Making large AI models cheaper, faster and more accessible. Keras-TextClassification: Chinese text classification models based on Keras. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over Keras-TextClassification? Choose ColossalAI over Keras-TextClassification when License: ColossalAI is Apache-2.0, Keras-TextClassification is MIT; Tags unique to ColossalAI: ai, big model, data-parallelism, deep-learning; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I choose Keras-TextClassification over ColossalAI? Choose Keras-TextClassification over ColossalAI when License: Keras-TextClassification is MIT, ColossalAI is Apache-2.0; Requirements: Min 4 GB RAM; Python environment must be prepared for running Keras.; Supports multiple model types requiring different levels of computation resources.; Tags unique to Keras-TextClassification: albert, bert, capsulenetwork, charcnn; Requires Chinese text classification for tasks like multi-label, sentence similarity analysis. ### When should I avoid ColossalAI? You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ### When should I avoid Keras-TextClassification? Does not cater to non-Chinese language datasets effectively due to its Chinese-specific models. Avoid if you seek a tool with extensive support beyond text classification like NER or POS tagging. ### Is ColossalAI or Keras-TextClassification more popular on GitHub? ColossalAI has more GitHub stars (41,432 vs 1,809). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and Keras-TextClassification open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, Keras-TextClassification: MIT). ### Where can I find alternatives to ColossalAI or Keras-TextClassification? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [Keras-TextClassification alternatives](/tools/yongzhuo-keras-textclassification/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [Keras-TextClassification markdown twin](/tools/yongzhuo-keras-textclassification/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/hpcaitech-colossalai-vs-yongzhuo-keras-textclassification.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or Keras-TextClassification? ColossalAI: Active. Keras-TextClassification: 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 ColossalAI and Keras-TextClassification? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [Keras-TextClassification trust report](/tools/yongzhuo-keras-textclassification/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hpcaitech-colossalai`](/api/graphcanon/graph?tool=hpcaitech-colossalai) - 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/_