公开记录卓越
软件健康报告模式 0.27.0 · 指标 2.5.0 · 2026-07-27 00:43 UTC
ludwig-ai / ludwig
Low-code framework for building custom LLMs, neural networks, and other AI models
ludwig-ai/ludwig 的健康指数为 100 分中的 94 分，处于「卓越」区间。 其得分最高的类别是Engineering Quality（96/100），最低的是AI Readiness（52/100）。 最近一次更新在今天。 近期的大部分工作由 3 位贡献者完成。
94
总分 / 100软件健康指数
指标归入加权类别，统一采用 1–100 量表。总体分先取类别加权平均，再依据公开记录的分布进行校准，使各等级具有百分位含义；当公开证据触发高风险司法辖区政策时，评级会按政策调整，并设置 34（存在风险）的上限。
94
卓越93-100公开记录中的最高层级（约前 5%）；基本满足所有检验标准
优秀80-92各方面均表现强劲；仅有少量不足
良好65-79健康；不足之处有限且可控
中等50-64可接受，但存在明显不足；建议进行审查
薄弱35-49多个领域存在实质性薄弱环节
存在风险20-34存在重大薄弱环节；采用时应保持审慎
危急1-19问题严重（项目被弃置、仅有单一维护者、缺乏基本工程规范）
评分画像
每条轴代表一个类别。形状比平均值更重要——健康的对象会填满整个图形，而“一峰一谷”式画像意味着某一维度的优势正掩盖另一维度的风险。
加权总体分 81 经校准后在公布的指数量表上为 94（记录校准 2026-08-02）。
所有权
软件包生态系统
| 注册表 | 软件包 | 版本 | 月下载量 | 版本数 | 最近发布 | 标签 |
|---|---|---|---|---|---|---|
| PyPI | ludwig | 0.17.8 | 3,413 | 77 | 0 天前 | computer-visiondeep-learningludwigmachine-learningnatural-language-processing |
按类别列示的指标
活力
项目是否仍有生命——是否仍在编写代码，是否仍在发布版本？
83
开发活跃度
72良好
评分方式
| 36/36 | 推送新近度 — 最近一次推送于 0 天前 |
| 8.3/36 | 提交节奏 — 52 周中有 12 周有提交 |
| 18/18 | 提交量 — 最近一年 267 次提交 |
| 10/10 | OpenSSF Scorecard：Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10 |
所用输入
| commits_last_year | 267 |
| human_commit_share | 0.98 |
| days_since_last_push | 0 |
| active_weeks_last_year | 12 |
发布纪律
100卓越
评分方式
| 27/27 | 有发布版本 — 已发布 73 个发布版本 |
| 36/36 | 发布时效 — 最近一次发布版本于 0 天前 |
| 27/27 | 发布节奏 — 约每 9 天发布一次 |
| 0/10 | OpenSSF Scorecard：Signed-Releases — 无数据 |
所用输入
| releases_count | 73 |
| latest_release_tag | v0.17.8 |
| releases_from_tags | 否 |
| days_since_latest_release | 0 |
| mean_days_between_releases | 9 |
已排除计分（无数据或不适用）：OpenSSF Scorecard：Signed-Releases。 其余权重已重新归一化。
社区与采用
项目是否拥有用户、下载量与关注度，并具备欢迎贡献者参与的配置？
86
流行度与采用
98卓越
评分方式
| 60/60 | 星标 — 11,746 个星标 |
| 25/25 | 复刻 — 1,218 个复刻 |
| 12.6/15 | 关注者 — 183 位关注者 |
所用输入
| forks | 1,218 |
| stars | 11,746 |
| watchers | 183 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
社区健康
92优秀
评分方式
| 22.5/22.5 | README |
| 22.5/22.5 | 许可证 — 可识别的许可证（Apache-2.0） |
| 18/18 | CONTRIBUTING 指南 |
| 13.5/13.5 | 行为准则 |
| 0/7.2 | 议题模板 |
| 6.3/6.3 | PR 模板 |
所用输入
| has_readme | 是 |
| has_license | 是 |
| readme_badges | — |
| has_contributing | 是 |
| has_issue_template | 否 |
| has_code_of_conduct | 是 |
| readme_badge_services | — |
| has_pull_request_template | 是 |
生态采用（下载量）
59中等
评分方式
| 47.1/80 | 月度下载量 — pypi 合计每月 3,413 次下载 |
| 0/20 | 注册表被依赖数 — 该生态系统不报告此项 |
所用输入
| packages | ludwig |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 3,413 |
已排除计分（无数据或不适用）：注册表被依赖数。 其余权重已重新归一化。
可持续性与治理
项目能否在其成员之外延续——巴士系数、响应能力、由谁支持，以及软件包的维护状况？
79
维护者韧性（巴士系数）
77良好
评分方式
| 36/54 | 巴士系数 — 3 位贡献者贡献了半数提交 |
| 17.6/22.5 | 提交分布 — 头号贡献者编写了 22% 的提交 |
| 13.5/13.5 | 贡献者广度 — 98 位贡献者 |
| 10/10 | OpenSSF Scorecard：Contributors — project has 17 contributing companies or organizations |
所用输入
| bus_factor | 3 |
| contributors_sampled | 98 |
| top_contributor_share | 0.219 |
议题与 PR 响应能力
78良好
评分方式
| 42/42 | 议题解决 — 100% 的议题已关闭 |
| 25.9/30 | PR 接受 — 已裁定的 PR 中 2,588/2,999 已合并 |
| 0/13 | Newcomer PR acceptance — 30 天内没有首次贡献者的 PR 得到裁决 |
| 0/15 | OpenSSF Scorecard：Code-Review — Found 0/28 approved changesets -- score normalized to 0 |
所用输入
| merged_prs | 2,588 |
| open_issues | 1 |
| closed_issues | 1,094 |
| prs_merged_7d | — |
| prs_decided_7d | — |
| prs_merged_30d | — |
| prs_decided_30d | — |
| issue_closed_ratio | 0.999 |
| closed_unmerged_prs | 411 |
| first_time_authors_30d | — |
| first_time_prs_merged_30d | — |
| first_time_prs_decided_30d | — |
已排除计分（无数据或不适用）：newcomer_pr_acceptance。 其余权重已重新归一化。
所有权与托管责任
64中等
评分方式
| 30/30 | 所有权背书 — 组织持有 |
| 0/20 | 已验证域名 |
| 16.2/25 | 所有者影响力 — ludwig-ai 有 176 位关注者 |
| 18.2/25 | 既往记录 — 6 个公开仓库，账户约 6 年 |
所用输入
| followers | 176 |
| owner_type | Organization |
| is_verified | — |
| owner_login | ludwig-ai |
| public_repos | 6 |
| account_age_days | 2,261 |
软件包维护
100卓越
评分方式
| 25/25 | 已发布且可解析 — pypi 上有 1 个软件包 |
| 35/35 | 发布时效 — 最近一次发布于 0 天前 |
| 20/20 | 版本历史 — 77 个已发布版本 |
| 20/20 | 未被弃用 — 活跃，未被弃用或撤回 |
所用输入
| packages | ludwig |
| ecosystems | pypi |
| any_deprecated | 否 |
| min_days_since_publish | 0 |
工程质量
基础的工程与文档实践是否到位？
96
工程实践
94卓越
评分方式
| 24/24 | CI 工作流 — 6 个工作流 |
| 24/24 | 存在测试 |
| 16/16 | Linter 配置 — .flake8 |
| 9.6/9.6 | Pre-commit 钩子 |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard：CI-Tests — 2 out of 2 merged PRs checked by a CI test -- score normalized to 10 |
所用输入
| has_ci | 是 |
| has_tests | 是 |
| has_editorconfig | 否 |
| has_linter_config | 是 |
| has_precommit_config | 是 |
文档
100卓越
评分方式
| 30/30 | README |
| 25/25 | 文档目录 |
| 15/15 | 文档 / 主页站点 — http://ludwig.ai |
| 10/10 | 仓库描述 |
| 10/10 | 主题标签 — 20 个主题标签 |
| 10/10 | Wiki |
所用输入
| topics | deep-learning, deeplearning, deep, learning, machine-learning, machinelearning, natural-language-processing, natural-language, computer-vision, data-centric, data-science, pytorch, neural-network, ml, llm, llm-training, fine-tuning, llama, mistral, llama2 |
| has_wiki | 是 |
| homepage | http://ludwig.ai |
| has_readme | 是 |
| has_docs_dir | 是 |
| has_description | 是 |
安全
可见的安全与供应链实践是否稳固，且不存在未解决的高风险司法辖区暴露？
64
安全态势
64中等
评分方式
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — 无数据 |
| 2.5/2.5 | CI-Tests — 2 out of 2 merged PRs checked by a CI test -- score normalized to 10 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 0/7.5 | Code-Review — Found 0/28 approved changesets -- score normalized to 0 |
| 2.5/2.5 | Contributors — project has 17 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | 许可证 — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 5/5 | Security-Policy — security policy file detected |
| 0/7.5 | Signed-Releases — 无数据 |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
所用输入
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 6.4 |
已排除计分（无数据或不适用）：branch_protection, signed_releases。 其余权重已重新归一化。
AI 就绪度
该仓库在多大程度上具备与 AI 编码代理协同开发与维护的条件？权重刻意设小（4%）：代理工具链是一项真实的维护信号，但完全不具备的仓库仍可达到 100/100。
52
AI 代理上下文与指引
40薄弱
评分方式
| 0/45 | 代理指令 — 没有 CLAUDE.md / AGENTS.md / 编辑器规则 |
| 0/15 | 机器可读文档（llms.txt） |
| 40/40 | 可读的提交历史 — 98 次人类提交中有 90 次说明了意图（结构化标题或解释性正文） |
所用输入
| has_llms_txt | 否 |
| legible_history_share | 0.918 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
评分方式
| 0/18 | 一条命令的引导启动 |
| 22/22 | 自动化测试 |
| 11/11 | Lint / 格式化配置 — .flake8 |
| 11/11 | 静态类型检查 — ludwig/py.typed |
| 10/10 | 可复现环境 — devcontainer, Dockerfile |
| 0/10 | 已体现的代理实践 — 最近 100 次提交中没有代理编写的提交 |
| 0/8 | 自动化维护 — 未观察到自动依赖更新 |
| 0/10 | OpenSSF Scorecard：Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
所用输入
| has_nix | 否 |
| has_tests | 是 |
| lockfiles | — |
| has_dockerfile | 是 |
| typed_language | 否 |
| bootstrap_files | — |
| has_devcontainer | 是 |
| has_linter_config | 是 |
| typecheck_configs | ludwig/py.typed |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
面向模型的代码可读性
82优秀
评分方式
| 27/45 | 可类型检查的代码 — Python，已配置类型检查（ludwig/py.typed） |
| 54.5/55 | 可控的文件大小 — 采样的 805 个源文件中有 7 个超过 60KB |
所用输入
| primary_language | Python |
| largest_source_bytes | 107,706 |
| source_files_sampled | 805 |
| oversized_source_files | 7 |
机器可读接口
40薄弱
评分方式
| 0/40 | API 模式（OpenAPI/GraphQL/proto） |
| 0/20 | MCP 服务器 |
| 40/40 | 可运行示例 — examples, notebooks |
所用输入
| example_dirs | examples, notebooks |
| has_mcp_signal | 否 |
| api_schema_files | — |
关键数据
11,746GitHub 星标
98贡献者
267最近 12 个月提交数
0距最近推送天数
73发布版本数
3巴士系数（bus factor）
1开放议题
PyPI软件包生态系统数
数据采集警告
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
- deps.dev does not index pypi:ludwig@0.17.8; advisories assessed against the repository dependency graph instead
- No resolved dependencies carried a version and a supported ecosystem
更多细节
Star 与 Fork 历史 0 ★ / 1,218 ⇿
0Star
1,218Fork
71发布
每颗 star 和每个 fork 的添加时间，来自 GitHub 并按天汇总。累计增长位于其构成来源——每日新增——的正上方，二者可相互对照：稳定的自然增长与短暂的突增形态截然不同。当这一差别可被衡量时，它会作为增长真实性予以报告。
仅显示最近的历史——该仓库超出采集窗口，因此未采集最早的历史记录。
主版本 0次版本 8修订 49
每个点涵盖 7 天。
OpenSSF Scorecard 6.4 / 10
6.4综合
来自开源项目 OpenSSF Scorecard 的独立、工具无关的安全评估。每项检查奖励的是安全实践本身，而非特定供应商的工具。Scorecard 无法判定的检查项标记为 不适用，并从安全评分中剔除（绝不按零分计）。
| 10 | Binary-Artifacts | no binaries found in the repo |
| 不适用 | Branch-Protection | internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md |
| 10 | CI-Tests | 2 out of 2 merged PRs checked by a CI test -- score normalized to 10 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 0 | Code-Review | Found 0/28 approved changesets -- score normalized to 0 |
| 10 | Contributors | project has 17 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 10 | Security-Policy | security policy file detected |
| 不适用 | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
直接依赖 40
| 注册表 | 软件包 | 版本约束 | 清单文件 |
|---|---|---|---|
| PyPI | numpy | >=1.24 | pyproject.toml |
| PyPI | pandas | >=2.0 | pyproject.toml |
| PyPI | scipy | >=1.10 | pyproject.toml |
| PyPI | tabulate | >=0.9 | pyproject.toml |
| PyPI | scikit-learn | >=1.3 | pyproject.toml |
| PyPI | tqdm | >=4.60 | pyproject.toml |
| PyPI | torch | >=2.11 | pyproject.toml |
| PyPI | torchaudio | >=2.11 | pyproject.toml |
| PyPI | torchcodec | >=0.1 | pyproject.toml |
| PyPI | torchvision | >=0.26 | pyproject.toml |
| PyPI | transformers | >=5.0 | pyproject.toml |
| PyPI | sentencepiece | >=0.2 | pyproject.toml |
| PyPI | spacy | >=2.3 | pyproject.toml |
| PyPI | PyYAML | >=6.0 | pyproject.toml |
| PyPI | absl-py | — | pyproject.toml |
| PyPI | kaggle | — | pyproject.toml |
| PyPI | requests | >=2.28 | pyproject.toml |
| PyPI | py-cpuinfo | — | pyproject.toml |
| PyPI | fsspec | — | pyproject.toml |
| PyPI | dataclasses-json | — | pyproject.toml |
| PyPI | jsonschema | >=4.17 | pyproject.toml |
| PyPI | tensorboard | — | pyproject.toml |
| PyPI | torchmetrics | >=1.0 | pyproject.toml |
| PyPI | torchinfo | — | pyproject.toml |
| PyPI | filelock | — | pyproject.toml |
| PyPI | psutil | — | pyproject.toml |
| PyPI | protobuf | >=4.0 | pyproject.toml |
| PyPI | gpustat | — | pyproject.toml |
| PyPI | rich | >=12.4.4 | pyproject.toml |
| PyPI | packaging | — | pyproject.toml |
| PyPI | retry | — | pyproject.toml |
| PyPI | sacremoses | — | pyproject.toml |
| PyPI | bitsandbytes | >=0.44.0 | pyproject.toml |
| PyPI | xlwt | — | pyproject.toml |
| PyPI | xlrd | — | pyproject.toml |
| PyPI | openpyxl | — | pyproject.toml |
| PyPI | pyarrow | >=14.0 | pyproject.toml |
| PyPI | lxml | — | pyproject.toml |
| PyPI | datasets | — | pyproject.toml |
| PyPI | safetensors | >=0.4 | pyproject.toml |
全部依赖 32
来自 GitHub 依赖图的完整解析依赖集合：22 个直接依赖与 10 个间接（传递）软件包。仓库提交锁文件时，传递闭包才是完整的。
| 注册表 | 软件包 | 版本 | 关系 |
|---|---|---|---|
| PyPI | bitsandbytes | — | 直接 |
| PyPI | jsonschema | — | 直接 |
| PyPI | numpy | — | 直接 |
| PyPI | pandas | — | 直接 |
| PyPI | protobuf | — | 直接 |
| PyPI | pyarrow | — | 直接 |
| PyPI | pyyaml | — | 直接 |
| PyPI | requests | — | 直接 |
| PyPI | rich | — | 直接 |
| PyPI | safetensors | — | 直接 |
| PyPI | scikit-learn | — | 直接 |
| PyPI | scipy | — | 直接 |
| PyPI | sentencepiece | — | 直接 |
| PyPI | spacy | — | 直接 |
| PyPI | tabulate | — | 直接 |
| PyPI | torch | — | 直接 |
| PyPI | torchaudio | — | 直接 |
| PyPI | torchcodec | — | 直接 |
| PyPI | torchmetrics | — | 直接 |
| PyPI | torchvision | — | 直接 |
| PyPI | tqdm | — | 直接 |
| PyPI | transformers | — | 直接 |
| PyPI | configspace | — | 间接 |
| PyPI | dask | — | 间接 |
| PyPI | future | — | 间接 |
| PyPI | matplotlib | — | 间接 |
| PyPI | peft | — | 间接 |
| PyPI | predibase | — | 间接 |
| PyPI | ray | — | 间接 |
| PyPI | ruff | — | 间接 |
| PyPI | s3fs | — | 间接 |
| PyPI | torchao | — | 间接 |
依赖安全公告 未评估
本报告未能完成公告比对：No resolved dependencies carried a version and a supported ecosystem
原始 JSON 报告 机器可读
{
"data": {
"repo": {
"topics": [
"deep-learning",
"deeplearning",
"deep",
"learning",
"machine-learning",
"machinelearning",
"natural-language-processing",
"natural-language",
"computer-vision",
"data-centric",
"data-science",
"pytorch",
"neural-network",
"ml",
"llm",
"llm-training",
"fine-tuning",
"llama",
"mistral",
"llama2"
],
"is_fork": false,
"size_kb": 35703,
"has_wiki": true,
"homepage": "http://ludwig.ai",
"languages": {
"Shell": 1890,
"Python": 6094533,
"Dockerfile": 4793,
"Jupyter Notebook": 15696
},
"pushed_at": "2026-07-27T00:35:29Z",
"created_at": "2018-12-27T23:58:12Z",
"owner_type": "Organization",
"updated_at": "2026-07-27T00:35:33Z",
"description": "Low-code framework for building custom LLMs, neural networks, and other AI models",
"is_archived": false,
"is_disabled": false,
"license_spdx": "Apache-2.0",
"default_branch": "main",
"license_spdx_raw": "Apache-2.0",
"primary_language": "Python",
"significant_languages": [
"Python"
]
},
"owner": {
"blog": "https://ludwig.ai",
"name": "Ludwig",
"type": "Organization",
"login": "ludwig-ai",
"company": null,
"location": "San Francisco, CA",
"followers": 176,
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"body": "- Require torch>=2.11, torchaudio>=2.11, torchvision>=0.26, transformers>=5.0\n in core deps to match torchao>=0.17.0 (which requires torch>=2.11); fixes\n LLM fine-tuning crash caused by torchao/torch version mismatch in Docker images\n- Update all four Docker images to pin torch==2.12.0, torchvisio\n[…]\na; torchao>=0.17.0 replaces old >=0.9.0\n- Change AutoML default tabular combiner from tabnet to ft_transformer;\n add ft_transformer and tabtransformer to combiner_defaults with tuned hyperopt configs",
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"body": "0.17.0 was tagged before the version bump commit landed, so the\nPyPI build produced ludwig-0.16.2 and was rejected as a duplicate.\nThis patch release also includes:\n- Preprocessing pipeline hardening for output features without\n preprocessing config (e.g. anomaly type)\n- StudioCallback for Ludwig Studio metrics/hyperopt integration\n- Per-call callbacks on train() and hyperopt ray-free hardening",
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"body": "Two root-cause fixes that together bring lazy-decode throughput to\nnear-eager performance:\n\n1. Audio FBANK over-subscription fix\n LazyColumn for audio now uses max(1, cpu_count // torch_threads)\n workers instead of the previous default of min(16, cpu_count+4).\n FBANK is CPU-bound and already u\n[…]\n: already 99.9% util (async reader was correct)\n\nAlso add:\n- 30 unit tests in tests/ludwig/data/test_prefetch_batcher.py\n- scripts/benchmark_training_pipeline.py for per-step timing analysis",
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"body": "…ession tests and benchmark\n\n- _with_lazy_decode now emits a WARNING (not silent skip) when lazy=True but\n lazy_audio_params / lazy_image_params is absent in training_set_metadata,\n so stale preprocessing caches fail loudly rather than silently passing path\n strings to workers.\n- Add parametrized\n[…]\nnchmark_lazy_decode.py to measure throughput across\n eager_local / lazy_local / eager_ray / lazy_ray paths; confirms lazy=False\n (eager_ray) is unaffected by the decode pipeline change (~3 700 sps).",
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"body": "… training actors\n\nLazy audio/image features store file paths in the dataset. PandasDataset wraps\nthese with LazyColumn objects so local training decodes per-batch. RayDataset had\nno equivalent, so Ray workers received raw path strings instead of tensors. The\nbatcher then tried to np.stack strings, \n[…]\naset — which is the whole point of lazy=True.\n\nAlso remove the workaround lazy=False from audio_feature() test helper; with the\nfix, lazy=True (schema default) works correctly in distributed training.",
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"headline": "fix(ray): decode lazy media features in Ray data pipeline, not inside…",
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"headline": "test(ci): rename integration groups to sequential letters, split dist…",
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"headline": "feat(data): lazy preprocessing for audio and image features (#4171)",
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"body": "…r in load_pretrained_from_config\n\ntorchao>=0.9.0 calls torch.utils._pytree.register_constant() at class-definition\ntime (via @register_as_pytree_constant), which was added in PyTorch 2.7.0. Users\non PyTorch 2.6.x get an AttributeError the moment transformers imports the torchao\nquantizer module — e\n[…]\nient HuggingFace Hub download failures; catching\n all Exception was causing 8 retries over ~2.5 minutes before surfacing the real\n error (AttributeError from the broken torchao import).\n\nFixes #4170",
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"headline": "fix(llm): require torch>=2.7 with llm extra; stop retrying non-OSErro…",
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"body": "…ed data\n\nPrevents OOM on image datasets (e.g. rendered_sst2) where streaming\n40k images into a large shuffle buffer exhausted all available RAM.\nSequential sampling from skip position is sufficient for diversity.\n\nAlso marks intentionally-deleted dataset configs as skipped in results.",
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"headline": "fix(datasets/smoke-test): use buffer=1 for diversity-retry skip-sampl…",
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"body": "…t outputs\n\nNatural Questions rows are ~1MB each; a 10k buffer wastes 10GB RAM.\nText/number output datasets have no minimum-diversity requirement, so\na 2k shuffle buffer is sufficient while keeping memory usage bounded.",
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"headline": "fix(datasets/smoke-test): reduce default shuffle buffer to 2k for tex…",
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"body": "…ification outputs\n\n100k buffer rows for text/number outputs (NQ, ASR, etc.) wastes RAM and time.\nOnly use the 100k buffer when output features are category/binary (need label\ndiversity in sorted datasets). Media datasets always use 5k.",
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"headline": "fix(datasets/smoke-test): smart shuffle buffer — large only for class…",
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"headline": "fix(datasets): change peoples_speech duration_ms from category to number",
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"body": "The GLUE ax diagnostic split only has a test split with all labels=-1\n(benchmark labels are hidden). Unusable for training smoke tests.",
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"body": "…on datasets\n\nWhen an output category/binary column has only 1 distinct value after sampling\n(dataset is sorted by label), retry by skipping 40k rows into the stream and\ntaking a second half-sample from a different label region. Handles cases like\nrendered_sst2 (SST-2 as images, sorted: all negatives first then positives).\n\nAlso adds skip parameter to stream_sample() for targeted offset sampling.",
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"headline": "fix(datasets/smoke-test): add diversity retry for sorted classificati…",
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"body": "Audio and image datasets stream large files — use a 5k buffer to avoid\nstreaming 100k large files. Text datasets use 100k buffer to ensure\nlabel diversity in sorted classification datasets (dbpedia_14, imdb, etc).",
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"body": "…iversity\n\nSorted datasets like imdb (25k rows/class), rotten_tomatoes (5k/class),\nand dbpedia_14 (40k/class) require a larger shuffle buffer to ensure\nat least 2 distinct label values appear in the 1000-row sample.",
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"headline": "fix(datasets/smoke-test): increase shuffle buffer to 100k for label d…",
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"body": "- Fix CauldronVQALoader._transform(): add default split_name=\"train\" so\n apply_custom_loader() can call it without positional arg\n- Fix NewYorkerCaptionContestLoader._transform(): same default arg fix\n- Fix NaturalQuestionsLoader: safe indexing when short_answers is []\n- Fix OpenBookQALoader: remov\n[…]\nor streaming smoke tests:\n fsd50k (labels always null), legalbench (all subsets <32 rows),\n mmmu (5 rows per split), gift_eval (streaming schema incompatible),\n samsum (dataset removed from HF Hub)",
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"body": "…fixes (#4169)",
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"headline": "fix(automl): dataset-aware sampling, transformer LR cap, SearchSpace …",
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"headline": "revert: remove tabpfn optional-dep workaround — tabpfn is now installed",
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"body": "…, dataset quality (#4168)",
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"headline": "feat: Mega-AutoML infrastructure — YAML search space, config pipeline…",
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"headline": "refactor: remove stale duplicate text/encoders.py; flatten deep nesti…",
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"body": "…package (#4154)",
"is_bot": false,
"headline": "refactor: split 4144-line visualize.py into domain-scoped visualize/ …",
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"headline": "Release v0.16.2",
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"headline": "fix: rewrite torch_utils tests to not fail in no-CUDA CI environments",
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"headline": "fix: update test_serve_v2 to use numpy_to_python (renamed from _numpy…",
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"is_bot": false,
"headline": "fix: remove redundant dtype check in text_feature; add visualize __ma…",
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"body": "…tations, docstring fixes",
"is_bot": false,
"headline": "refactor: major api.py cleanup — guard clauses, extraction, type anno…",
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"body": "…st strategy\n\nTwo regression tests to prevent re-introduction of the bugs fixed in\nthe recent patch releases:\n\n1. tests/ludwig/utils/test_import_safety.py (#4142)\n Simulates a broken torchao/PyTorch environment where transformers'\n lazy loader raises ModuleNotFoundError for PreTrainedModel. Veri\n[…]\nr: Distributed strategy not\n initialized. The existing category-output test missed this because\n SoftmaxCrossEntropyMetric inherits MeanMetric and takes a shortcut\n that bypasses sync_context().",
"is_bot": false,
"headline": "test: regression tests for transformers import safety and Ray tune di…",
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"body": "…learning_rate_fn\n\nBoth Ray remote tuning functions run metric evaluation (train_step →\nupdate_metrics → eval_loss → TorchMetrics sync_context) without ever\ninitializing a distributed strategy. This caused:\n\n RuntimeError: Distributed strategy not initialized\n\nin get_current_dist_strategy() inside \n[…]\nwhat train_fn and eval_fn already do: call\ninit_dist_strategy(\"local\") immediately after initialize_pytorch() so\nthe metric sync context is available for the duration of the tuning pass.\n\nFixes #4149.",
"is_bot": false,
"headline": "fix: call init_dist_strategy(\"local\") in tune_batch_size_fn and tune_…",
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"headline": "Release v0.16.1",
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"body": "…CKING in llm_utils and text_feature\n\nOn Python 3.12, function annotations are evaluated eagerly at module load\ntime. When torchao and PyTorch are version-mismatched (torchao calls\ntorch.utils._pytree.register_constant which doesn't exist in older PyTorch\nbuilds), transformers' lazy loader for class\n[…]\ne them to\n TYPE_CHECKING and add `from __future__ import annotations`.\n- text_feature.py: PreTrainedTokenizer was imported at module level but only\n used in type annotations. Same fix.\n\nFixes #4142.",
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"headline": "refactor: migrate from black+isort+flake8 to unified ruff toolchain",
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"oid": "868e40b17807fb5dfdd89aefc1d4e306bbbc04cb",
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"headline": "fix: replace assert with explicit exceptions; fix mutable default arg…",
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"headline": "Release v0.16.0",
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"body": "…ython 3.12\n\nIn Python 3.12, function return-type annotations are evaluated eagerly at\nmodule load time. hf_utils.py imported PreTrainedModel at the top level,\nbut transformers 5.x uses a lazy import system that requires torch to be\nfully initialized — which in Ray worker processes it isn't yet duri\n[…]\nn our dev environment.\n\nMove PreTrainedModel under TYPE_CHECKING and add from __future__ import\nannotations to defer annotation evaluation, fixing the ModuleNotFoundError\non Python 3.12 + conda + Ray.",
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"headline": "fix: defer PreTrainedModel import to TYPE_CHECKING to fix import on P…",
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"body": "…nd LLM readability\n\nComplete redesign:\n- New capability matrix table in What's New (PatchTST/N-BEATS, advanced PEFT, VLM, HyperNetwork\n combiner, Nash-MTL/Pareto-MTL, LLM config gen, ModelInspector, Ray Serve, KServe)\n- Collapsed capabilities into details sections (LLM fine-tuning, multimodal/tabu\n[…]\nmerged into concise bullet list\n- Added navigation bar (Docs / Getting Started / Examples / Discord)\n- Improved keyword density for search and LLM retrieval (model names, technique names, config keys)",
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"headline": "docs: modernize README — add all 0.15 features, restructure for SEO a…",
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"headline": "fix: set dask convert-string:False at import time to prevent UnicodeD…",
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"headline": "fix: eliminate partd race condition by using tasks-based Dask shuffle…",
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"headline": "fix+test: encoder input_shape contract + ultra-slow e2e tests for Pat…",
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"headline": "feat: world-class timeseries forecasting — PatchTST, N-BEATS, MASE, s…",
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"headline": "feat: advanced PEFT adapters — PiSSA/EVA/CorDA, TinyLoRA, C3A, OFT, H…",
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"headline": "fix: missing patches from 0.15.1 + ultra-slow test suite (#4145)",
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"headline": "fix: GPU underutilization in Ray backend + Python 3.14 annotation cra…",
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"headline": "fix: sort imports in dataset_synthesizer.py (isort)",
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"body": "CacheableDataframe.get_cache_directory() returns tempfile.gettempdir()\n(not the pytest tmpdir), so path assertions for use_df+no_cache_dir must\nuse the system temp dir rather than the test-scoped tmpdir fixture.\n\nAlso updates pre-commit config to python3.14 (python3.12 removed after OS\nupgrade) and drops docformatter whose untokenize dep is broken on 3.14.",
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"headline": "fix: update cache test expectations for in-memory DataFrame paths",
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"params": {}
}
],
"max_points": 7.5
},
{
"key": "ci_tests",
"name": "CI-Tests",
"detail": "2 out of 2 merged PRs checked by a CI test -- score normalized to 10",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "cii_best_practices",
"name": "CII-Best-Practices",
"detail": "no effort to earn an OpenSSF best practices badge detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 2.5
},
{
"key": "code_review",
"name": "Code-Review",
"detail": "Found 0/28 approved changesets -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "contributors",
"name": "Contributors",
"detail": "project has 17 contributing companies or organizations",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "dangerous_workflow",
"name": "Dangerous-Workflow",
"detail": "no dangerous workflow patterns detected",
"points": 10,
"status": "met",
"details": [],
"max_points": 10
},
{
"key": "dependency_update_tool",
"name": "Dependency-Update-Tool",
"detail": "update tool detected",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "fuzzing",
"name": "Fuzzing",
"detail": "project is not fuzzed",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "license",
"name": "License",
"detail": "license file detected",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "packaging",
"name": "Packaging",
"detail": "packaging workflow detected",
"points": 5,
"status": "met",
"details": [],
"max_points": 5
},
{
"key": "pinned_dependencies",
"name": "Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "sast",
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"detail": "SAST tool is not run on all commits -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "security_policy",
"name": "Security-Policy",
"detail": "security policy file detected",
"points": 5,
"status": "met",
"details": [],
"max_points": 5
},
{
"key": "signed_releases",
"name": "Signed-Releases",
"detail": "no releases found",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 7.5
},
{
"key": "token_permissions",
"name": "Token-Permissions",
"detail": "detected GitHub workflow tokens with excessive permissions",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "vulnerabilities",
"name": "Vulnerabilities",
"detail": "0 existing vulnerabilities detected",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "exceptional",
"name": "High-Risk Jurisdiction Exposure",
"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
"inputs": {
"meaning": "self-published location evidence; not nationality or citizenship",
"red_flag": false,
"exposures": [],
"policy_countries": [
"Russia",
"Iran",
"North Korea"
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"commit_weight_rule": {
"min_commits": 50,
"min_commit_share": 0.1
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"review_only_matches": 0,
"below_threshold_exposures": [],
"assessed_self_published_locations": 8
},
"components": [
{
"key": "policy_exposure_multiplier",
"name": "Policy exposure multiplier",
"detail": "no confirmed policy-scope location match",
"points": 100,
"status": "met",
"details": [
{
"code": "jurisdiction_no_match",
"params": {}
}
],
"max_points": 100
}
]
}
],
"description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
},
{
"key": "ai_readiness",
"band": "moderate",
"name": "AI Readiness",
"value": 52,
"weight": 0.04,
"metrics": [
{
"key": "ai_agent_context",
"band": "weak",
"name": "Agent context & guidance",
"note": null,
"notes": [],
"value": 40,
"inputs": {
"has_llms_txt": false,
"legible_history_share": 0.918,
"agent_instruction_files": [],
"agent_instruction_max_bytes": null
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "no CLAUDE.md / AGENTS.md / editor rules",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_instructions",
"params": {}
}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
"name": "Legible commit history",
"detail": "90 of 98 human commits state their intent (structured subject or explanatory body)",
"points": 40,
"status": "met",
"details": [
{
"code": "legible_history",
"params": {
"legible": 90,
"sampled": 98
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}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "moderate",
"name": "Verify loop (build / test / typecheck)",
"note": null,
"notes": [],
"value": 54,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [],
"has_dockerfile": true,
"typed_language": false,
"bootstrap_files": [],
"has_devcontainer": true,
"has_linter_config": true,
"typecheck_configs": [
"ludwig/py.typed"
],
"agent_commit_share": 0,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 18
},
{
"key": "automated_tests",
"name": "Automated tests",
"detail": null,
"points": 22,
"status": "met",
"details": [],
"max_points": 22
},
{
"key": "lint_format_config",
"name": "Lint / format config",
"detail": ".flake8",
"points": 11,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": ".flake8"
}
}
],
"max_points": 11
},
{
"key": "static_type_checking",
"name": "Static type checking",
"detail": "ludwig/py.typed",
"points": 11,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "ludwig/py.typed"
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}
],
"max_points": 11
},
{
"key": "reproducible_environment",
"name": "Reproducible environment",
"detail": "devcontainer, Dockerfile",
"points": 10,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "devcontainer, Dockerfile"
}
}
],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "no agent-authored commits among the last 100",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_authored_commits",
"params": {
"sampled": 100
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}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "no automated dependency updates observed",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_dependency_automation",
"params": {}
}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
}
]
},
{
"key": "ai_code_legibility",
"band": "excellent",
"name": "Code legibility for models",
"note": null,
"notes": [],
"value": 82,
"inputs": {
"primary_language": "Python",
"largest_source_bytes": 107706,
"source_files_sampled": 805,
"oversized_source_files": 7
},
"components": [
{
"key": "type_checkable_code",
"name": "Type-checkable code",
"detail": "Python with type-check config (ludwig/py.typed)",
"points": 27,
"status": "partial",
"details": [
{
"code": "typecheck_config_language",
"params": {
"files": "ludwig/py.typed",
"language": "Python"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "7/805 source files over 60KB",
"points": 54.5,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 805,
"oversized": 7
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}
],
"max_points": 55
}
]
},
{
"key": "ai_interfaces",
"band": "weak",
"name": "Machine-readable interfaces",
"note": null,
"notes": [],
"value": 40,
"inputs": {
"example_dirs": [
"examples",
"notebooks"
],
"has_mcp_signal": false,
"api_schema_files": []
},
"components": [
{
"key": "api_schema_openapi_graphql_proto",
"name": "API schema (OpenAPI/GraphQL/proto)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 40
},
{
"key": "mcp_server",
"name": "MCP server",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 20
},
{
"key": "runnable_examples",
"name": "Runnable examples",
"detail": "examples, notebooks",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "examples, notebooks"
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}
],
"max_points": 40
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight: agent tooling is a real maintenance signal, but its absence must never gate the top of the scale (calibration saturates at raw 91, so 100/100 remains reachable with AI Readiness at zero)."
}
],
"classification": {
"top": [
"library"
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"labels": [
"library"
],
"scores": {
"library": 8
},
"primary": "library",
"evidence": [
{
"tier": "distribution",
"label": "library",
"source": "registry:pypi",
"weight": 6
},
{
"tier": "description",
"label": "library",
"source": "description:library",
"weight": 2
}
],
"artifacts": [],
"confidence": "medium",
"host_extension": false,
"runs_as_process": false,
"consumed_by_code": true
},
"metrics_version": "2.5.0"
},
"warnings": [
"Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
"deps.dev does not index pypi:ludwig@0.17.8; advisories assessed against the repository dependency graph instead",
"No resolved dependencies carried a version and a supported ecosystem"
],
"report_type": "repository",
"generated_at": "2026-07-27T00:43:05.617223Z",
"schema_version": "0.27.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/l/ludwig-ai/ludwig.svg",
"full_name": "ludwig-ai/ludwig",
"license_state": "standard",
"license_spdx": "Apache-2.0"
}