Software health reportschema 0.27.0 · metrics 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
PythonApache-2.0★ 11,746 stars⑂ 1,218 forkssince Dec 2018View on GitHub ↗
ludwig-ai/ludwig holds a health index of 94 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (96/100) and lowest on AI Readiness (52/100). It was last updated today. 3 contributors account for most of its recent work.
overall / 100
Exceptional
Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.
The weighted overall 81 is calibrated to 94 on the published index scale (record calibration 2026-08-02).
Ownership

LudwigOrganization
176 followers6 public repossince May 2020
This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.
Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish	Tags
PyPI	ludwig	0.17.8	3,413	77	0 days ago	computer-vision deep-learning ludwig machine-learning natural-language-processing
Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?
83Excellent · 21% of overall
Development activity

72Good
How it's scored
36/36	Push recency — last push 0 days ago
8.3/36	Commit cadence — 12/52 weeks with commits
18/18	Commit volume — 267 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year	267
human_commit_share	0.98
days_since_last_push	0
active_weeks_last_year	12
Release discipline

100Exceptional
How it's scored
27/27	Ships releases — 73 releases published
36/36	Release recency — latest release 0 days ago
27/27	Release cadence — a release every ~9 days
0/10	OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count	73
latest_release_tag	v0.17.8
releases_from_tags	no
days_since_latest_release	0
mean_days_between_releases	9
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.
Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?
86Excellent · 17% of overall
Popularity & adoption

98Exceptional
How it's scored
60/60	Stars — 11,746 stars
25/25	Forks — 1,218 forks
12.6/15	Watchers — 183 watchers
Inputs used
forks	1,218
stars	11,746
watchers	183
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history
Community health

92Excellent
How it's scored
22.5/22.5	README
22.5/22.5	License — recognized license (Apache-2.0)
18/18	CONTRIBUTING guide
13.5/13.5	Code of conduct
0/7.2	Issue template
6.3/6.3	PR template
Inputs used
has_readme	yes
has_license	yes
readme_badges	—
has_contributing	yes
has_issue_template	no
has_code_of_conduct	yes
readme_badge_services	—
has_pull_request_template	yes
Ecosystem adoption (downloads)

59Moderate
How it's scored
47.1/80	Monthly downloads — 3,413 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem
Inputs used
packages	ludwig
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	3,413
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.
Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?
79Good · 23% of overall
Maintainer resilience (bus factor)

77Good
How it's scored
36/54	Bus factor — 3 contributor(s) cover half of all commits
17.6/22.5	Commit distribution — top contributor authored 22% of commits
13.5/13.5	Contributor breadth — 98 contributors
10/10	OpenSSF Scorecard: Contributors — project has 17 contributing companies or organizations
Inputs used
bus_factor	3
contributors_sampled	98
top_contributor_share	0.219
Issue & PR responsiveness

78Good
How it's scored
42/42	Issue resolution — 100% of issues closed
25.9/30	PR acceptance — 2,588/2,999 decided PRs merged
0/13	Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15	OpenSSF Scorecard: Code-Review — Found 0/28 approved changesets -- score normalized to 0
Inputs used
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	—
Excluded from scoring (no data or not applicable): newcomer_pr_acceptance. Remaining weights renormalized.
Ownership & stewardship

64Moderate
How it's scored
30/30	Ownership backing — organization-owned
0/20	Verified domain
16.2/25	Owner reach — 176 followers of ludwig-ai
18.2/25	Track record — 6 public repos, account ~6 yr old
Inputs used
followers	176
owner_type	Organization
is_verified	—
owner_login	ludwig-ai
public_repos	6
account_age_days	2,261
Package maintenance

100Exceptional
How it's scored
25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 0 days ago
20/20	Version history — 77 published versions
20/20	Not deprecated — active, not deprecated or yanked
Inputs used
packages	ludwig
ecosystems	pypi
any_deprecated	no
min_days_since_publish	0
Engineering Quality

Are baseline engineering and documentation practices in place?
96Exceptional · 19% of overall
Engineering practices

94Exceptional
How it's scored
24/24	CI workflows — 6 workflow(s)
24/24	Tests present
16/16	Linter config — .flake8
9.6/9.6	Pre-commit hooks
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
Inputs used
has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	yes
Documentation

100Exceptional
How it's scored
30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — http://ludwig.ai
10/10	Repository description
10/10	Topics — 20 topics
10/10	Wiki
Inputs used
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	yes
homepage	http://ludwig.ai
has_readme	yes
has_docs_dir	yes
has_description	yes
Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?
64Moderate · 16% of overall
Security posture

64Moderate
How it's scored
7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — no data
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	License — 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 — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5	Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
source	openssf_scorecard
checks_evaluated	16
scorecard_version	v5.5.0
checks_inconclusive	2
scorecard_aggregate	6.4
Excluded from scoring (no data or not applicable): branch_protection, signed_releases. Remaining weights renormalized.
AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.
52Moderate · 4% of overall
Agent context & guidance

40Weak
How it's scored
0/45	Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15	Machine-readable docs (llms.txt)
40/40	Legible commit history — 90 of 98 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txt	no
legible_history_share	0.918
agent_instruction_files	—
agent_instruction_max_bytes	—
Verify loop (build / test / typecheck)

54Moderate
How it's scored
0/18	One-command bootstrap
22/22	Automated tests
11/11	Lint / format config — .flake8
11/11	Static type checking — ludwig/py.typed
10/10	Reproducible environment — devcontainer, Dockerfile
0/10	Demonstrated agent practice — no agent-authored commits among the last 100
0/8	Automated maintenance — no automated dependency updates observed
0/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nix	no
has_tests	yes
lockfiles	—
has_dockerfile	yes
typed_language	no
bootstrap_files	—
has_devcontainer	yes
has_linter_config	yes
typecheck_configs	ludwig/py.typed
agent_commit_share	0
toolchain_manifests	—
dependency_bot_commit_share	0
Code legibility for models

82Excellent
How it's scored
27/45	Type-checkable code — Python with type-check config (ludwig/py.typed)
54.5/55	Manageable file sizes — 7/805 source files over 60KB
Inputs used
primary_language	Python
largest_source_bytes	107,706
source_files_sampled	805
oversized_source_files	7
Machine-readable interfaces

40Weak
How it's scored
0/40	API schema (OpenAPI/GraphQL/proto)
0/20	MCP server
40/40	Runnable examples — examples, notebooks
Inputs used
example_dirs	examples, notebooks
has_mcp_signal	no
api_schema_files	—
Key facts

11,746GitHub stars
98contributors
267commits, last 12 months
0days since last push
73releases
3bus factor
1open issues
PyPIpackage ecosystems
Data collection 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
More detail

Star and fork history 0 ★ / 1,218 ⇿
0Stars
1,218Forks
71Releases
When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.
Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.
Major 0Minor 8Patch 49
Each point covers 7 days.
OpenSSF Scorecard 6.4 / 10
6.4aggregate
Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-07-27 00:42 UTC
10	Binary-Artifacts	no binaries found in the repo
n/a	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
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
10	Vulnerabilities	0 existing vulnerabilities detected
Direct dependencies 40
Registry	Package	Version constraint	Manifest
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
All dependencies 32
Full resolved dependency set from the GitHub dependency graph: 22 direct and 10 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.
Registry	Package	Version	Relation
PyPI	bitsandbytes	`—`	direct
PyPI	jsonschema	`—`	direct
PyPI	numpy	`—`	direct
PyPI	pandas	`—`	direct
PyPI	protobuf	`—`	direct
PyPI	pyarrow	`—`	direct
PyPI	pyyaml	`—`	direct
PyPI	requests	`—`	direct
PyPI	rich	`—`	direct
PyPI	safetensors	`—`	direct
PyPI	scikit-learn	`—`	direct
PyPI	scipy	`—`	direct
PyPI	sentencepiece	`—`	direct
PyPI	spacy	`—`	direct
PyPI	tabulate	`—`	direct
PyPI	torch	`—`	direct
PyPI	torchaudio	`—`	direct
PyPI	torchcodec	`—`	direct
PyPI	torchmetrics	`—`	direct
PyPI	torchvision	`—`	direct
PyPI	tqdm	`—`	direct
PyPI	transformers	`—`	direct
PyPI	configspace	`—`	indirect
PyPI	dask	`—`	indirect
PyPI	future	`—`	indirect
PyPI	matplotlib	`—`	indirect
PyPI	peft	`—`	indirect
PyPI	predibase	`—`	indirect
PyPI	ray	`—`	indirect
PyPI	ruff	`—`	indirect
PyPI	s3fs	`—`	indirect
PyPI	torchao	`—`	indirect
Dependency advisories not assessed
Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem
Raw JSON report machine-readable
{
  "data": {
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        },
        {
          "tag": "v0.14.1",
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        {
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        {
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        },
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        },
        {
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          "kind": "other",
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        },
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          "tag": "v0.8.6",
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        },
        {
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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",
          "is_bot": false,
          "headline": "chore: bump version to 0.17.2; upgrade torch stack and automl defaults",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-23T00:43:39Z",
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          "body": "- Extract _NDJSONChannel helper to eliminate _open/_emit copy-paste\n- Use NumpyEncoder instead of default=str for Ludwig JSON convention\n- _NDJSONChannel.__del__ closes file handle if on_hyperopt_end never fires\n- Remove import-inside-method for time in trainer.py and trainer_utils.py\n- Replace logger.info() in SIGUSR1/2 signal handlers with print() to\n  avoid potential deadlock when logging lock is held by main thread\n- Exclude ephemeral ProgressTracker fields from JSON serialization",
          "is_bot": false,
          "headline": "Refactor StudioCallback and trainer pause/resume for correctness",
          "author_name": "w4nderlust",
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          "body": "… bug\n\nTwo root causes for CI failure in test_experiment[1919-0]:\n\n1. eval_loss() double-update bug: for non-MeanMetric eval_loss_metrics\n   (e.g. MSEMetric), calling self.eval_loss_metric(preds, targets) invokes\n   forward() which updates the metric's running state — but update_metrics()\n   already\n[…]\nx: module-scoped single_threaded_blas fixture sets\n   torch.set_num_threads(1) for the duration of test_reproducibility.py,\n   eliminating BLAS non-determinism without affecting the rest of the suite.",
          "is_bot": false,
          "headline": "fix: deterministic reproducibility tests; fix eval_loss double-update…",
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          "body": "Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>",
          "is_bot": true,
          "headline": "[pre-commit.ci] pre-commit suggestions (#4191)",
          "author_name": "pre-commit-ci[bot]",
          "author_login": "pre-commit-ci[bot]",
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          "oid": "45a33bf9c6e8512aa943ad30db197832bf1750ed",
          "body": ".claude/worktrees/ paths were committed as gitlink submodule entries,\nbreaking CI checkout with submodules:recursive. Removed from index\nand added to .gitignore.",
          "is_bot": false,
          "headline": "Remove accidental Claude worktree gitlinks from index",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-18T21:35:03Z",
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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",
          "is_bot": false,
          "headline": "chore: bump version to 0.17.1",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
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          "body": "…ssing config\n\nOutput features like `anomaly` do not include a `preprocessing` key in their\nconfig dict (unlike all standard input/output features). This caused KeyErrors\nin four places in the preprocessing pipeline:\n\n- build_preprocessing_parameters: skip features with no PREPROCESSING key\n- build_\n[…]\nin the metric registry) that prevents training; these fixes are\ndefensive guards that prevent the preprocessing stage from crashing on any\nfuture output feature type that omits a preprocessing config.",
          "is_bot": false,
          "headline": "Fix preprocessing pipeline crash for output features without preproce…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
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          "body": "…onments\n\n- Add callbacks parameter to LudwigModel.train() so per-call callbacks can\n  be merged with model-level callbacks without rebuilding the model\n- Make ray imports in execution.py optional so OptunaExecutor works without ray\n- Add on_hyperopt_trial_start/end dispatch in OptunaExecutor so callbacks\n  receive trial lifecycle events during optuna-based hyperopt runs",
          "is_bot": false,
          "headline": "Add per-call callbacks to train(), harden hyperopt for ray-free envir…",
          "author_name": "w4nderlust",
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          "oid": "44512d165418a86c0378cf3dd1b1ef7fbf7c2fb7",
          "body": "- Rename ludwig/callbacks.py → ludwig/callbacks/__init__.py so that\n  ludwig.callbacks.studio is importable as a submodule\n- Add on_hyperopt_trial_start / on_hyperopt_trial_end / on_hyperopt_end\n  hooks to StudioCallback: write trial_start, trial_end, hyperopt_end\n  events to <group_output_dir>/trials.jsonl for Ludwig Studio to stream\n- Add optional group_id / group_output_dir constructor params\n- Track best_eval_metric_value per trial via on_epoch_end for reporting",
          "is_bot": false,
          "headline": "Convert callbacks.py to package, add hyperopt hooks to StudioCallback",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
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          "body": "StudioCallback (ludwig/callbacks/studio.py):\n- Built-in Ludwig callback for Ludwig Studio integration\n- Streams lifecycle events (phase transitions, per-epoch metrics) to\n  <output_dir>/metrics.jsonl (line-buffered NDJSON)\n- Emits progress_pct and eta_seconds on every metric event using the\n  new Pr\n[…]\n\n- Register SIGUSR2 handler: resume from SIGUSR1 pause\n- Initialize _training_paused=False in __init__\n- Restore SIG_DFL at end of training\n- Populate progress_tracker fields after batcher initializes",
          "is_bot": false,
          "headline": "Add StudioCallback, SIGUSR1/2 pause-resume, enhanced ProgressTracker",
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          "body": "… (CWE-502) (#4190)",
          "is_bot": false,
          "headline": "fix(security): remove pickle from auto-dispatch and harden torch.load…",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-17T19:05:10Z",
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          "body": "…#4189)",
          "is_bot": false,
          "headline": "fix(api): remove stale type: ignore and dead tuple check in train() (…",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-17T17:41:55Z",
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          "oid": "7c526ab6beda3a965c36af1d618f98a366583575",
          "body": "…guide (#4187)",
          "is_bot": false,
          "headline": "docs: fix stale BaseFeatureMixin references in adding_a_feature_type …",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-17T17:11:14Z",
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          "body": null,
          "is_bot": false,
          "headline": "refactor: full codebase improvement plan (Phase 0–7) (#4186)",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-17T07:53:31Z",
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          "oid": "0cd82690695d1930deafd59563470c4b90ccbb2b",
          "body": "…; fix visualize types\n\n- Add name() and description() classmethods to VeRA, LoHa, LoKr, FourierFT, BOFT adapter configs\n- Annotate **kwargs: Any in api.py public methods and kfold_cross_validate\n- Annotate hyperopt_hiplot_cli/hyperopt_hiplot with full type signatures\n- Add dict annotation to metadata parameter in confidence_thresholding_2thresholds_{2,3}d",
          "is_bot": false,
          "headline": "fix(docs): add name/description to adapter schemas; annotate **kwargs…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T20:48:52Z",
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          "body": null,
          "is_bot": false,
          "headline": "chore: bump version to 0.17.0",
          "author_name": "w4nderlust",
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          "committed_at": "2026-05-16T20:30:03Z",
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        {
          "oid": "b6b6bc917d8af755263ba6f497a421d22e794806",
          "body": "…hed memmap (#4173)",
          "is_bot": false,
          "headline": "feat(data): preprocessing mode enum + prefetch_size config + lazy_cac…",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T19:14:03Z",
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        },
        {
          "oid": "84d180d796860fc71780a3e930a0f7875d8928ef",
          "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",
          "is_bot": false,
          "headline": "feat(data): prefetch background decoder for lazy audio/image features",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T05:21:55Z",
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        },
        {
          "oid": "991efde40be4c8275164a59c8b1f121176a11f39",
          "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).",
          "is_bot": false,
          "headline": "fix(ray): warn on missing lazy_audio/image_params; add lazy-mode regr…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T04:34:22Z",
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        },
        {
          "oid": "39a774cd2df5828163a85b7a6ac9d8adddea699c",
          "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.",
          "is_bot": false,
          "headline": "fix(ray): decode lazy media features in Ray data pipeline, not inside…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T04:00:47Z",
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        },
        {
          "oid": "c6b3b1fd5c10fa1cc0a34ebb9cc670094b5bb264",
          "body": "…ributed tests 6x (#4172)",
          "is_bot": false,
          "headline": "test(ci): rename integration groups to sequential letters, split dist…",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T02:56:15Z",
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          "oid": "4e94923dde41f7ab97b91d73dcc40a8b7cca2f90",
          "body": null,
          "is_bot": false,
          "headline": "feat(data): lazy preprocessing for audio and image features (#4171)",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T00:22:30Z",
          "body_truncated": false,
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        },
        {
          "oid": "0743a27c15a0b0df9f606529c39fe983cdde5893",
          "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",
          "is_bot": false,
          "headline": "fix(llm): require torch>=2.7 with llm extra; stop retrying non-OSErro…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T04:11:22Z",
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        },
        {
          "oid": "910a45ed6aaa16e332b9dc3da38beab9ffd0c88d",
          "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.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): use buffer=1 for diversity-retry skip-sampl…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:37:45Z",
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        },
        {
          "oid": "426f348ff761cb586c040ccdda419b2ef6a563e4",
          "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.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): reduce default shuffle buffer to 2k for tex…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:16:31Z",
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        },
        {
          "oid": "a17f0b756b7adc3e9e1cbb55c194c686baa31103",
          "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.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): smart shuffle buffer — large only for class…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:14:55Z",
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        {
          "oid": "427279981b479dfa138b5fcf3c275117fa7bcac6",
          "body": null,
          "is_bot": false,
          "headline": "fix(datasets): change peoples_speech duration_ms from category to number",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:09:47Z",
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        },
        {
          "oid": "92e1b20cac811b9a5a12173f39e6750417f90748",
          "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.",
          "is_bot": false,
          "headline": "fix(datasets): remove glue_diagnostic config (hidden test labels)",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:06:00Z",
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        },
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          "oid": "2c83326c57c879a7d7aeb8ec416e34264193f18c",
          "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.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): add diversity retry for sorted classificati…",
          "author_name": "w4nderlust",
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          "committed_at": "2026-05-14T03:05:25Z",
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        },
        {
          "oid": "e596e904ef2121cb901799c825a6e95df3074d51",
          "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).",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): use media-aware shuffle buffer size",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:00:51Z",
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        },
        {
          "oid": "de8bb8d16bada7382cf125e3308a086bd8328afd",
          "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.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): increase shuffle buffer to 100k for label d…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T02:58:56Z",
          "body_truncated": false,
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        },
        {
          "oid": "6ae10bbea70483a438570848f5ae7a5300c899f9",
          "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)",
          "is_bot": false,
          "headline": "fix(datasets): fix loaders and configs for smoke test failures",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T02:58:17Z",
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        },
        {
          "oid": "693c1d86d053fdb5036250074278199c6b8ae3c7",
          "body": "…sets\n\nAdds 8 loader modules and 51 YAML configs covering every complex-column\npattern — nothing skipped.\n\ntranslation_loader: HF translation dict → src/tgt text columns\n  (opus100_en_fr/es, wmt14/16/19_de_en, opus_books_en_fr)\n\nner_loader: token/tag lists → space-joined strings\n  (wikiann_en/de/zh,\n[…]\ns, textvqa, vqav2, docvqa, scienceqa_vqa,\n   mathvista, mmmu)\n\nmisc_loaders: one-off transforms\n  (klue_sts, multirc, sciq, gift_eval)\n\nAlso adds invoice_data and cord_v2 (image→text, plain HFLoader).",
          "is_bot": false,
          "headline": "feat(datasets): add custom loaders and configs for 51 complex HF data…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-13T19:56:32Z",
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              {
                "key": "contributor_breadth",
                "name": "Contributor breadth",
                "detail": "98 contributors",
                "points": 13.5,
                "status": "met",
                "details": [
                  {
                    "code": "contributors_sampled",
                    "params": {
                      "count": 98
                    }
                  }
                ],
                "max_points": 13.5
              },
              {
                "key": "openssf_scorecard_contributors",
                "name": "OpenSSF Scorecard: Contributors",
                "detail": "project has 17 contributing companies or organizations",
                "points": 10,
                "status": "met",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "responsiveness",
            "band": "good",
            "name": "Issue & PR responsiveness",
            "note": "Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "newcomer_pr_acceptance"
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                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 78,
            "inputs": {
              "merged_prs": 2588,
              "open_issues": 1,
              "closed_issues": 1094,
              "prs_merged_7d": null,
              "prs_decided_7d": null,
              "prs_merged_30d": null,
              "prs_decided_30d": null,
              "issue_closed_ratio": 0.999,
              "closed_unmerged_prs": 411,
              "first_time_authors_30d": null,
              "first_time_prs_merged_30d": null,
              "first_time_prs_decided_30d": null
            },
            "components": [
              {
                "key": "issue_resolution",
                "name": "Issue resolution",
                "detail": "100% of issues closed",
                "points": 42,
                "status": "partial",
                "details": [
                  {
                    "code": "issues_closed_share",
                    "params": {
                      "share": 100
                    }
                  }
                ],
                "max_points": 42
              },
              {
                "key": "pr_acceptance",
                "name": "PR acceptance",
                "detail": "2588/2999 decided PRs merged",
                "points": 25.9,
                "status": "partial",
                "details": [
                  {
                    "code": "decided_prs_merged",
                    "params": {
                      "merged": 2588,
                      "decided": 2999
                    }
                  }
                ],
                "max_points": 30
              },
              {
                "key": "newcomer_pr_acceptance",
                "name": "Newcomer PR acceptance",
                "detail": "no first-time contributor's PR decided in 30d",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_newcomer_prs",
                    "params": {
                      "days": 30
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                  }
                ],
                "max_points": 13
              },
              {
                "key": "openssf_scorecard_code_review",
                "name": "OpenSSF Scorecard: Code-Review",
                "detail": "Found 0/28 approved changesets -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              }
            ]
          },
          {
            "key": "stewardship",
            "band": "moderate",
            "name": "Ownership & stewardship",
            "note": null,
            "notes": [],
            "value": 64,
            "inputs": {
              "followers": 176,
              "owner_type": "Organization",
              "is_verified": null,
              "owner_login": "ludwig-ai",
              "public_repos": 6,
              "account_age_days": 2261
            },
            "components": [
              {
                "key": "ownership_backing",
                "name": "Ownership backing",
                "detail": "organization-owned",
                "points": 30,
                "status": "met",
                "details": [
                  {
                    "code": "owner_organization",
                    "params": {}
                  }
                ],
                "max_points": 30
              },
              {
                "key": "verified_domain",
                "name": "Verified domain",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "owner_reach",
                "name": "Owner reach",
                "detail": "176 followers of ludwig-ai",
                "points": 16.2,
                "status": "partial",
                "details": [
                  {
                    "code": "owner_followers",
                    "params": {
                      "count": 176,
                      "login": "ludwig-ai"
                    }
                  }
                ],
                "max_points": 25
              },
              {
                "key": "track_record",
                "name": "Track record",
                "detail": "6 public repos, account ~6 yr old",
                "points": 18.2,
                "status": "partial",
                "details": [
                  {
                    "code": "public_repos",
                    "params": {
                      "count": 6
                    }
                  },
                  {
                    "code": "account_age_years",
                    "params": {
                      "years": 6
                    }
                  }
                ],
                "max_points": 25
              }
            ]
          },
          {
            "key": "package_maintenance",
            "band": "exceptional",
            "name": "Package maintenance",
            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "packages": [
                "ludwig"
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              "ecosystems": "pypi",
              "any_deprecated": false,
              "min_days_since_publish": 0
            },
            "components": [
              {
                "key": "published_resolvable",
                "name": "Published & resolvable",
                "detail": "1 package(s) on pypi",
                "points": 25,
                "status": "met",
                "details": [
                  {
                    "code": "packages_published",
                    "params": {
                      "count": 1,
                      "ecosystems": "pypi"
                    }
                  }
                ],
                "max_points": 25
              },
              {
                "key": "publish_recency",
                "name": "Publish recency",
                "detail": "latest publish 0 days ago",
                "points": 35,
                "status": "met",
                "details": [
                  {
                    "code": "publish_recency",
                    "params": {
                      "days": 0
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                  }
                ],
                "max_points": 35
              },
              {
                "key": "version_history",
                "name": "Version history",
                "detail": "77 published versions",
                "points": 20,
                "status": "met",
                "details": [
                  {
                    "code": "published_versions",
                    "params": {
                      "count": 77
                    }
                  }
                ],
                "max_points": 20
              },
              {
                "key": "not_deprecated",
                "name": "Not deprecated",
                "detail": "active, not deprecated or yanked",
                "points": 20,
                "status": "met",
                "details": [
                  {
                    "code": "package_not_deprecated",
                    "params": {}
                  }
                ],
                "max_points": 20
              }
            ]
          }
        ],
        "description": "Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?"
      },
      {
        "key": "engineering",
        "band": "exceptional",
        "name": "Engineering Quality",
        "value": 96,
        "weight": 0.19,
        "metrics": [
          {
            "key": "engineering_practices",
            "band": "exceptional",
            "name": "Engineering practices",
            "note": null,
            "notes": [],
            "value": 94,
            "inputs": {
              "has_ci": true,
              "has_tests": true,
              "has_editorconfig": false,
              "has_linter_config": true,
              "has_precommit_config": true
            },
            "components": [
              {
                "key": "ci_workflows",
                "name": "CI workflows",
                "detail": "6 workflow(s)",
                "points": 24,
                "status": "met",
                "details": [
                  {
                    "code": "ci_workflows",
                    "params": {
                      "count": 6
                    }
                  }
                ],
                "max_points": 24
              },
              {
                "key": "tests_present",
                "name": "Tests present",
                "detail": null,
                "points": 24,
                "status": "met",
                "details": [],
                "max_points": 24
              },
              {
                "key": "linter_config",
                "name": "Linter config",
                "detail": ".flake8",
                "points": 16,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": ".flake8"
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                  }
                ],
                "max_points": 16
              },
              {
                "key": "pre_commit_hooks",
                "name": "Pre-commit hooks",
                "detail": null,
                "points": 9.6,
                "status": "met",
                "details": [],
                "max_points": 9.6
              },
              {
                "key": "editorconfig",
                "name": ".editorconfig",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 6.4
              },
              {
                "key": "openssf_scorecard_ci_tests",
                "name": "OpenSSF Scorecard: CI-Tests",
                "detail": "2 out of 2 merged PRs checked by a CI test -- score normalized to 10",
                "points": 20,
                "status": "met",
                "details": [],
                "max_points": 20
              }
            ]
          },
          {
            "key": "documentation",
            "band": "exceptional",
            "name": "Documentation",
            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "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": true,
              "homepage": "http://ludwig.ai",
              "has_readme": true,
              "has_docs_dir": true,
              "has_description": true
            },
            "components": [
              {
                "key": "readme",
                "name": "README",
                "detail": null,
                "points": 30,
                "status": "met",
                "details": [],
                "max_points": 30
              },
              {
                "key": "documentation_directory",
                "name": "Documentation directory",
                "detail": null,
                "points": 25,
                "status": "met",
                "details": [],
                "max_points": 25
              },
              {
                "key": "documentation_homepage_site",
                "name": "Documentation / homepage site",
                "detail": "http://ludwig.ai",
                "points": 15,
                "status": "met",
                "details": [],
                "max_points": 15
              },
              {
                "key": "repository_description",
                "name": "Repository description",
                "detail": null,
                "points": 10,
                "status": "met",
                "details": [],
                "max_points": 10
              },
              {
                "key": "topics",
                "name": "Topics",
                "detail": "20 topics",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "topics_count",
                    "params": {
                      "count": 20
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "wiki",
                "name": "Wiki",
                "detail": null,
                "points": 10,
                "status": "met",
                "details": [],
                "max_points": 10
              }
            ]
          }
        ],
        "description": "Are baseline engineering and documentation practices in place?"
      },
      {
        "key": "security",
        "band": "moderate",
        "name": "Security",
        "value": 64,
        "weight": 0.16,
        "metrics": [
          {
            "key": "security_posture",
            "band": "moderate",
            "name": "Security posture",
            "note": "Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "branch_protection",
                    "signed_releases"
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                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 64,
            "inputs": {
              "source": "openssf_scorecard",
              "checks_evaluated": 16,
              "scorecard_version": "v5.5.0",
              "checks_inconclusive": 2,
              "scorecard_aggregate": 6.4
            },
            "components": [
              {
                "key": "binary_artifacts",
                "name": "Binary-Artifacts",
                "detail": "no binaries found in the repo",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "branch_protection",
                "name": "Branch-Protection",
                "detail": "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",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "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",
                "name": "SAST",
                "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
              },
              "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": {}
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                ],
                "max_points": 100
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            ]
          }
        ],
        "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": [
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                    "code": "no_agent_instructions",
                    "params": {}
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                ],
                "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": [],
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              "typecheck_configs": [
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              "agent_commit_share": 0,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
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            "components": [
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                "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",
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              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": ".flake8",
                "points": 11,
                "status": "met",
                "details": [
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                    "code": "file_list",
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                  }
                ],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": "ludwig/py.typed",
                "points": 11,
                "status": "met",
                "details": [
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                    "code": "file_list",
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                ],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": "devcontainer, Dockerfile",
                "points": 10,
                "status": "met",
                "details": [
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                    "code": "file_list",
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                      "files": "devcontainer, Dockerfile"
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                  }
                ],
                "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": {
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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
                    }
                  }
                ],
                "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"
                    }
                  }
                ],
                "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"
      ],
      "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"
}
Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.
Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.5.0, schema v0.27.0 — full methodology · metrics wiki.
How one result sits in the wider record: aggregate statistics — PyPI.
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