Публічний реєстрПомірний
Звіт про здоров'я програмного забезпеченнясхема 0.27.0 · метрики 1.13.0 · 2026-07-30 11:27 UTC
bytedance / xpu-perf
[HPCA 2026] AI Accelerator Benchmark focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and versatility of software and hardware.
bytedance/xpu-perf має індекс здоров’я 51 зі 100, що відповідає смузі «Помірний». Найвищий показник — Sustainability & Governance (69/100), найнижчий — Security (16/100). Останнє оновлення було 99 днів тому. Більшість нещодавньої роботи виконують 2 учасники.
51
загалом / 100Індекс здоров'я програмного забезпечення
Метрики згруповано у зважені категорії на шкалі 1–100. Загальна оцінка починається як їхнє середнє; коли публічні дані активують Політику юрисдикцій високого ризику, рейтинг коригується й отримує верхню межу 49 («Під ризиком»). Готовність до ШІ не входить до індексу.
51
Відмінний85-100Зразковий; відповідає практично всім перевіреним критеріям
Добрий70-84Здоровий; незначні прогалини
Помірний50-69Прийнятний, але з помітними прогалинами; рекомендовано перевірку
У зоні ризику30-49Суттєві слабкі місця; впровадження потребує обережності
Критичний1-29Серйозні проблеми (покинутий, єдиний мейнтейнер, без базової гігієни)
Профіль оцінок
Кожна вісь — окрема категорія. Форма важить більше, ніж середнє: здоровий об'єкт заповнює всю фігуру, тоді як профіль із піками та провалами означає, що сила в одному вимірі маскує ризик в іншому.
Власність
Bytedance Inc.Організація
18 616 підписників414 публічних репозиторіївз квіт. 2013 р.
За цим репозиторієм стоїть організація — спільна, підзвітна опіка, здатна пережити будь-якого окремого мейнтейнера.
Метрики за категоріями
Життєздатність
Чи живий проєкт — чи пишеться код і чи виходять релізи?
41
Активність розробки
33У зоні ризику
Як обчислюється оцінка
| 9.9/36 | Свіжість push — останній push 99 дн. тому |
| 6.9/36 | Ритм комітів — 10/52 тижнів із комітами |
| 13.3/18 | Обсяг комітів — 29 комітів за останній рік |
| 0/10 | OpenSSF Scorecard: Maintained — немає даних |
Використані вхідні дані
| commits_last_year | 29 |
| human_commit_share | 1 |
| days_since_last_push | 99 |
| active_weeks_last_year | 10 |
Дисципліна релізів
52Помірний
Як обчислюється оцінка
| 27/27 | Випускає релізи — опубліковано 1 релізів |
| 7.2/36 | Свіжість релізів — останній реліз 703 дн. тому |
| 12.6/27 | Ритм релізів — ритм невідомий (єдиний реліз) |
| 0/10 | OpenSSF Scorecard: Signed-Releases — немає даних |
Використані вхідні дані
| releases_count | 1 |
| latest_release_tag | v1.0.0 |
| releases_from_tags | ні |
| days_since_latest_release | 703 |
| mean_days_between_releases | — |
Спільнота та впровадження
Чи має проєкт користувачів, завантаження, увагу та влаштовані умови для контриб’юторів?
67
Популярність і впровадження
65Помірний
Як обчислюється оцінка
| 41.6/60 | Зірки — 370 зірок |
| 17.6/25 | Форки — 130 форків |
| 5.8/15 | Спостерігачі — 12 спостерігачів |
Використані вхідні дані
| forks | 130 |
| stars | 370 |
| watchers | 12 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Здоров'я спільноти
70Добрий
Як обчислюється оцінка
| 22.5/22.5 | README |
| 22.5/22.5 | Ліцензія — визнана ліцензія (Apache-2.0) |
| 18/18 | Настанови CONTRIBUTING |
| 0/13.5 | Кодекс поведінки |
| 0/7.2 | Шаблон issue |
| 0/6.3 | Шаблон PR |
Використані вхідні дані
| has_readme | так |
| has_license | так |
| has_contributing | так |
| has_issue_template | ні |
| has_code_of_conduct | ні |
| has_pull_request_template | ні |
Сталість та врядування
Чи переживе проєкт своїх людей — бас-фактор, реактивність, хто за ним стоїть і як супроводжуються пакети?
69
Стійкість мейнтейнерів (бас-фактор)
58Помірний
Як обчислюється оцінка
| 25.2/54 | Бас-фактор — на 2 контриб’ютор(ів) припадає половина всіх комітів |
| 13.9/22.5 | Розподіл комітів — головний контриб’ютор — автор 38% комітів |
| 13.5/13.5 | Широта контриб’юторів — 15 контриб’юторів |
| 0/10 | OpenSSF Scorecard: Contributors — немає даних |
Використані вхідні дані
| bus_factor | 2 |
| contributors_sampled | 15 |
| top_contributor_share | 0,382 |
Реактивність на issue та PR
72Добрий
Як обчислюється оцінка
| 31.8/46.8 | Вирішення issue — закрито 68% issue |
| 29.8/38.3 | Прийняття PR — злито 88/113 вирішених PR |
| 0/15 | OpenSSF Scorecard: Code-Review — немає даних |
Використані вхідні дані
| merged_prs | 88 |
| open_issues | 24 |
| closed_issues | 51 |
| issue_closed_ratio | 0,68 |
| closed_unmerged_prs | 25 |
Власність та опіка
80Добрий
Як обчислюється оцінка
| 30/30 | Підтримка власника — у власності організації |
| 0/20 | Верифікований домен |
| 25/25 | Охоплення власника — 18 616 підписників у bytedance |
| 25/25 | Послужний список — 414 публічних репозиторіїв, вік облікового запису ~13 р. |
Використані вхідні дані
| followers | 18 616 |
| owner_type | Organization |
| is_verified | — |
| owner_login | bytedance |
| public_repos | 414 |
| account_age_days | 4 854 |
Інженерна якість
Чи наявні базові інженерні практики та документація?
56
Інженерні практики
50Помірний
Як обчислюється оцінка
| 0/24 | Процеси CI |
| 24/24 | Наявні тести |
| 16/16 | Конфігурація лінтера — .flake8, .pylintrc |
| 0/9.6 | Pre-commit-хуки |
| 0/6.4 | .editorconfig |
| 0/20 | OpenSSF Scorecard: CI-Tests — немає даних |
Використані вхідні дані
| has_ci | ні |
| has_tests | так |
| has_editorconfig | ні |
| has_linter_config | так |
| has_precommit_config | ні |
Документація
65Помірний
Як обчислюється оцінка
| 30/30 | README |
| 0/25 | Каталог документації |
| 15/15 | Сайт документації / домашня сторінка — https://xpu-perf.ai/ |
| 10/10 | Опис репозиторію |
| 10/10 | Теми — 1 тем |
| 0/10 | Wiki |
Використані вхідні дані
| topics | python |
| has_wiki | ні |
| homepage | https://xpu-perf.ai/ |
| has_readme | так |
| has_docs_dir | ні |
| has_description | так |
Безпека
Чи міцні видимі практики безпеки й ланцюга постачання, без непослабленої пов’язаності з юрисдикціями високого ризику?
16
Стан безпеки
1Критичний
Як обчислюється оцінка
| 0/30 | Політика безпеки (SECURITY.md) |
| 0/25 | Конфігурація Dependabot |
| 0/25 | Файли фіксації залежностей |
| 0/20 | Процес CodeQL |
Використані вхідні дані
| source | file_signals |
| lockfiles | — |
| manifests | pyproject.toml |
| has_codeql_workflow | ні |
| has_security_policy | ні |
| has_dependabot_config | ні |
Сповіщення про залежності
74Добрий
Як обчислюється оцінка
| 20.2/35 | Прямі залежності без відомих сповіщень — уражено 1: numpy 1.19.2 (moderate 5.3) |
| 0/25 | Непрямі залежності без відомих сповіщень — транзитивний набір не відокремлюється від залежностей розробки й тестування в цьому обсязі |
| 35.3/40 | Немає задавнених сповіщень — 1 пакет(ів) зі сповіщеннями без реакції понад 90 дн.; найдавніше опубліковано 1 685 дн. тому |
Використані вхідні дані
| source | osv |
| advisories | 1 854 |
| affected_packages | 18 |
| assessed_packages | 38 |
| unassessed_packages | 52 |
| affected_by_severity | critical 9, high 7, moderate 2 |
| direct_affected_packages | 1 |
Виключено з оцінювання (немає даних або не застосовно): Непрямі залежності без відомих сповіщень. Залишкові ваги перенормовано. Звірено 38 резолвлених залежностей із OSV. 52 не вдалося оцінити — немає резолвленої версії, непідтримувана екосистема або поза межами звітованого переліку пакетів. Цей репозиторій не публікує пакета, який резолвить індекс, тож натомість оцінено граф залежностей репозиторію. Цей граф змішує закріплені версії для розробки й тестування зі справді постачаними залежностями, тож оцінюються лише задекларовані runtime-залежності; транзитивні знахідки подаються як контекст і в оцінку не входять. Досяжність не аналізується.
Готовність до ШІ
Наскільки репозиторій оснащений для розробки та супроводу за участі ШІ-агентів? Незалежний, експериментальний бейдж — вага 0.0, тож він подається окремо і не впливає на загальний індекс здоров'я.
62
Контекст і настанови для агентів
20Критичний
Як обчислюється оцінка
| 0/45 | Інструкції для агентів — немає CLAUDE.md / AGENTS.md / правил редактора |
| 0/15 | Машиночитана документація (llms.txt) |
| 19.7/40 | Читабельна історія комітів — намір зазначено у 37 з 100 людських комітів (структурований заголовок або пояснювальний текст) |
Використані вхідні дані
| has_llms_txt | ні |
| legible_history_share | 0,37 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Як обчислюється оцінка
| 18/18 | Розгортання однією командою — projects/train_perf/Megatron-LM/megatron/core/datasets/Makefile |
| 22/22 | Автоматизовані тести |
| 11/11 | Конфігурація лінтера / форматера — .flake8, .pylintrc |
| 11/11 | Статична перевірка типів — projects/train_perf/Megatron-LM/mypy.ini |
| 10/10 | Відтворюване середовище — Dockerfile |
| 0/10 | Підтверджена практика роботи з агентами — серед останніх 100 комітів немає створених агентом |
| 0/8 | Автоматизоване супроводження — автоматичних оновлень залежностей не виявлено |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — немає даних |
Використані вхідні дані
| has_nix | ні |
| has_tests | так |
| lockfiles | — |
| has_dockerfile | так |
| typed_language | ні |
| bootstrap_files | projects/train_perf/Megatron-LM/megatron/core/datasets/Makefile |
| has_devcontainer | ні |
| has_linter_config | так |
| typecheck_configs | projects/train_perf/Megatron-LM/mypy.ini |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
Читабельність коду для моделей
81Добрий
Як обчислюється оцінка
| 27/45 | Типізований код — Python з конфігурацією перевірки типів (projects/train_perf/Megatron-LM/mypy.ini) |
| 54.4/55 | Керовані розміри файлів — 8/757 файлів вихідного коду понад 60 КБ |
Використані вхідні дані
| primary_language | Python |
| largest_source_bytes | 132 791 |
| source_files_sampled | 757 |
| oversized_source_files | 8 |
Машиночитні інтерфейси
80Добрий
Як обчислюється оцінка
| 40/40 | Схема API (OpenAPI/GraphQL/proto) — projects/infer_perf/llm_perf/server/server.proto |
| 0/20 | Сервер MCP |
| 40/40 | Придатні до запуску приклади — examples, recipes |
Використані вхідні дані
| example_dirs | examples, recipes |
| has_mcp_signal | ні |
| api_schema_files | projects/infer_perf/llm_perf/server/server.proto |
Ключові факти
370зірок GitHub
15контриб'юторів
29комітів за останні 12 місяців
99днів від останнього пушу
1релізів
2бас-фактор
24відкритих issue
PyPIпакетних екосистем
Попередження щодо збору даних
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
- Could not fetch pypi package 'xpu-perf' from its registry
- Advisory severity resolved for 120 of 731 advisories (lookup cap); the remainder are reported as unknown severity
- OpenSSF Scorecard did not return a usable result (exit code -9); skipping Scorecard checks
Докладніше
Історія зірок і форків 0 ★ / 130 ⇿
0Зірки
130Форки
1Релізи
Коли додано кожну зірку й форк — зібрано з GitHub і згруповано за днями. Кумулятивне зростання розміщено просто над денними додаваннями, з яких воно складається, тож їх видно одне проти одного: рівномірне органічне накопичення виглядає зовсім інакше, ніж різкий короткочасний сплеск. Там, де цю різницю можна виміряти, її подано як автентичність росту.
Мажорні 1Мінорні 0Патчі 0
Кожна точка охоплює 3 днів.
Прямі залежності 5
| Реєстр | Пакет | Обмеження версії | Маніфест |
|---|---|---|---|
| PyPI | prettytable | — | pyproject.toml |
| PyPI | jsonlines | — | pyproject.toml |
| PyPI | flask | — | pyproject.toml |
| PyPI | requests | — | pyproject.toml |
| PyPI | numpy | <=1.26.4 | pyproject.toml |
Усі залежності 90
Повний розв'язаний набір залежностей із графа залежностей GitHub: 8 прямих і 82 непрямих (транзитивних) пакетів. Транзитивне замикання є повним, коли в репозиторії закомічено lockfile.
| Реєстр | Пакет | Версія | Зв'язок |
|---|---|---|---|
| PyPI | flask | — | пряма |
| PyPI | jsonlines | — | пряма |
| PyPI | numpy | — | пряма |
| PyPI | numpy | 1.19.2 | пряма |
| PyPI | numpy | 1.23.0 | пряма |
| PyPI | numpy | 1.23.4 | пряма |
| PyPI | prettytable | — | пряма |
| PyPI | requests | — | пряма |
| PyPI | accelerate | — | непряма |
| PyPI | attrs | 21.2.0 | непряма |
| PyPI | backoff | — | непряма |
| PyPI | bert | — | непряма |
| PyPI | bert-tensorflow | 1.0.1 | непряма |
| PyPI | black | 23.1.0 | непряма |
| PyPI | decorator | — | непряма |
| PyPI | einops | — | непряма |
| PyPI | flask-restful | — | непряма |
| PyPI | fpdf | — | непряма |
| PyPI | google-api-python-client | — | непряма |
| PyPI | google-cloud-core | 2.3.2 | непряма |
| PyPI | graphviz | 0.8.4 | непряма |
| PyPI | grpcio | — | непряма |
| PyPI | isort | — | непряма |
| PyPI | keras2onnx | — | непряма |
| PyPI | matplotlib | — | непряма |
| PyPI | matplotlib | 3.3.4 | непряма |
| PyPI | matplotlib | 3.4.2 | непряма |
| PyPI | multipledispatch | — | непряма |
| PyPI | mypy-protobuf | 2.8 | непряма |
| PyPI | nltk | — | непряма |
| PyPI | nvidia-modelopt | — | непряма |
| PyPI | onnx | — | непряма |
| PyPI | onnx | 1.12.0 | непряма |
| PyPI | onnx | 1.13.1 | непряма |
| PyPI | onnxconverter-common | 1.12.2 | непряма |
| PyPI | onnxruntime | — | непряма |
| PyPI | onnxruntime | 1.12.0 | непряма |
| PyPI | opencv-python | — | непряма |
| PyPI | opencv-python-headless | — | непряма |
| PyPI | packaging | — | непряма |
| PyPI | pandas | — | непряма |
| PyPI | pandas | 1.3.3 | непряма |
| PyPI | pandas | 1.5.1 | непряма |
| PyPI | prompt-toolkit | — | непряма |
| PyPI | protobuf | — | непряма |
| PyPI | protobuf | 3.19.4 | непряма |
| PyPI | protobuf | 3.19.6 | непряма |
| PyPI | protobuf | 3.20.2 | непряма |
| PyPI | psutil | — | непряма |
| PyPI | pulp | — | непряма |
| PyPI | pybind11 | — | непряма |
| PyPI | pydot | — | непряма |
| PyPI | pytest | — | непряма |
| PyPI | pytest-cov | — | непряма |
| PyPI | pytest-mock | — | непряма |
| PyPI | pytest-random-order | — | непряма |
| PyPI | pyyaml | — | непряма |
| PyPI | scikit-learn | — | непряма |
| PyPI | scipy | — | непряма |
| PyPI | sentencepiece | — | непряма |
| PyPI | sentencepiece | 0.1.96 | непряма |
| PyPI | setuptools | 67.5 | непряма |
| PyPI | synr | 0.4 | непряма |
| PyPI | tensorflow | — | непряма |
| PyPI | tensorflow | 1.15.0 | непряма |
| PyPI | tensorflow | 2.4.0 | непряма |
| PyPI | tensorflow | 2.7.2 | непряма |
| PyPI | tensorflow | 2.8.4 | непряма |
| PyPI | tensorflow-datasets | 4.7.0 | непряма |
| PyPI | tensorstore | — | непряма |
| PyPI | tf2onnx | — | непряма |
| PyPI | tiktoken | — | непряма |
| PyPI | tokenization | — | непряма |
| PyPI | torch | — | непряма |
| PyPI | torch | 1.12.1 | непряма |
| PyPI | torch | 1.13.1 | непряма |
| PyPI | torch | 1.9.1 | непряма |
| PyPI | torchvision | — | непряма |
| PyPI | torchvision | 0.10.1 | непряма |
| PyPI | torchvision | 0.13.1 | непряма |
| PyPI | tqdm | — | непряма |
| PyPI | transformers | — | непряма |
| PyPI | transformers | 4.40.0 | непряма |
| PyPI | triton | 2.1.0 | непряма |
| PyPI | typing-extensions | 3.7.4.3 | непряма |
| PyPI | virtualenv | 16.7.9 | непряма |
| PyPI | wandb | — | непряма |
| PyPI | wrapt | — | непряма |
| PyPI | wrapt | 1.14.1 | непряма |
| PyPI | zarr | — | непряма |
Сповіщення про залежності 18
Цей репозиторій не публікує пакета, який розпізнає індекс, тож оцінено його власний граф залежностей — 38 пакетів, серед яких є й піниї розробки та тестування, що ніколи не постачаються: 18 мають відомі сповіщення, з них 1 прямі. 52 не вдалося оцінити — немає резолвленої версії, непідтримувана екосистема або поза наведеним переліком пакетів.
| Пакет | Версія | Зв'язок | Критичність | Сповіщень | Виправлено в |
|---|---|---|---|---|---|
| black | 23.1.0 | непряма | критична | 5 | 26.3.1 |
| onnx | 1.12.0 | непряма | критична | 24 | 1.22.0 |
| onnx | 1.13.1 | непряма | критична | 22 | 1.22.0 |
| tensorflow | 1.15.0 | непряма | критична | 745 | 2.11.1 |
| tensorflow | 2.4.0 | непряма | критична | 760 | 2.11.1 |
| torch | 1.12.1 | непряма | критична | 30 | 2.13.0 |
| torch | 1.13.1 | непряма | критична | 28 | 2.13.0 |
| torch | 1.9.1 | непряма | критична | 30 | 2.13.0 |
| transformers | 4.40.0 | непряма | критична | 42 | 5.5.0 |
| protobuf | 3.19.4 | непряма | висока | 6 | 6.33.5 |
| protobuf | 3.19.6 | непряма | висока | 4 | 6.33.5 |
| protobuf | 3.20.2 | непряма | висока | 4 | 6.33.5 |
| sentencepiece | 0.1.96 | непряма | висока | 2 | 0.2.1 |
| setuptools | 67.5 | непряма | висока | 6 | 83.0.0 |
| tensorflow | 2.7.2 | непряма | висока | 94 | 2.11.1 |
| tensorflow | 2.8.4 | непряма | висока | 46 | 2.11.1 |
| numpy | 1.19.2 | пряма | помірна | 2 | 1.22 |
| virtualenv | 16.7.9 | непряма | помірна | 4 | 20.36.1 |
Сповіщення означає, що версія, записана в графі залежностей, потрапляє в уражений діапазон. Досяжність не аналізується, а граф містить піниї розробки й тестування — знахідка може стосуватися інструментів, а не поставленого коду.
Звіт у форматі JSON машиночитний
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"inputs": {
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"components": [
{
"key": "type_checkable_code",
"name": "Type-checkable code",
"detail": "Python with type-check config (projects/train_perf/Megatron-LM/mypy.ini)",
"points": 27,
"status": "partial",
"details": [
{
"code": "typecheck_config_language",
"params": {
"files": "projects/train_perf/Megatron-LM/mypy.ini",
"language": "Python"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "8/757 source files over 60KB",
"points": 54.4,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 757,
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}
}
],
"max_points": 55
}
]
},
{
"key": "ai_interfaces",
"band": "good",
"name": "Machine-readable interfaces",
"note": null,
"notes": [],
"value": 80,
"inputs": {
"example_dirs": [
"examples",
"recipes"
],
"has_mcp_signal": false,
"api_schema_files": [
"projects/infer_perf/llm_perf/server/server.proto"
]
},
"components": [
{
"key": "api_schema_openapi_graphql_proto",
"name": "API schema (OpenAPI/GraphQL/proto)",
"detail": "projects/infer_perf/llm_perf/server/server.proto",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "projects/infer_perf/llm_perf/server/server.proto"
}
}
],
"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, recipes",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "examples, recipes"
}
}
],
"max_points": 40
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
],
"metrics_version": "1.13.0"
},
"warnings": [
"Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
"Could not fetch pypi package 'xpu-perf' from its registry",
"Advisory severity resolved for 120 of 731 advisories (lookup cap); the remainder are reported as unknown severity",
"OpenSSF Scorecard did not return a usable result (exit code -9); skipping Scorecard checks"
],
"report_type": "repository",
"generated_at": "2026-07-30T11:27:36.779050Z",
"schema_version": "0.27.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/b/bytedance/xpu-perf.svg",
"full_name": "bytedance/xpu-perf",
"license_state": "standard",
"license_spdx": "Apache-2.0"
}Оцінки — це сигнали, а не гарантії. Вони відображають публічно видимі практики на GitHub — це не аудит коду й не гарантія безпеки.
Відсутні дані виключаються, а ваги перенормовуються — нуль за відсутність ніколи не ставиться. Методологія версіонована й відкрита: метрики v1.13.0, схема v0.27.0 — повна методологія · вікі метрик.
Як окремий результат виглядає на тлі всього реєстру: сукупна статистика.