Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-14 18:49 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.

PythonApache-2.0★ 383 stars⑂ 130 forkssince Apr 2023View on GitHub ↗
KindNetwork servicehow this is determined

bytedance/xpu-perf holds a health index of 50 out of 100, placing it in the Moderate band. It scores highest on Sustainability & Governance (69/100) and lowest on Security (16/100). It was last updated 145 days ago. 2 contributors account for most of its recent work.

50
overall / 100
Moderate

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.

50
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)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

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 50 is calibrated to 50 on the published index scale (record calibration 2026-08-02).

Ownership

Bytedance Inc.Organization
19,001 followers418 public repossince Apr 2013

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

35Weak · 21% of overall
How it's scored
9.9/36Push recency — last push 145 days ago
4.2/36Commit cadence — 6/52 weeks with commits
12.2/18Commit volume — 22 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year22
human_commit_share1
days_since_last_push145
active_weeks_last_year6
How it's scored
27/27Ships releases — 1 releases published
0/36Release recency — latest release 749 days ago
12.6/27Release cadence — cadence unknown (single release)
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count1
latest_release_tagv1.0.0
releases_from_tagsno
days_since_latest_release749
mean_days_between_releases—

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

67Good · 17% of overall
How it's scored
41.9/60Stars — 383 stars
17.6/25Forks — 130 forks
5.8/15Watchers — 12 watchers
Inputs used
forks130
stars383
watchers12
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

69Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
13.9/22.5Commit distribution — top contributor authored 38% of commits
13.5/13.5Contributor breadth — 15 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled15
top_contributor_share0.382
How it's scored
27.8/42Issue resolution — 66% of issues closed
23.4/30PR acceptance — 88/113 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs88
open_issues26
closed_issues51
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.662
closed_unmerged_prs25
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
25/25Owner reach — 19,001 followers of bytedance
25/25Track record — 418 public repos, account ~13 yr old
Inputs used
followers19,001
owner_typeOrganization
is_verifiedno
owner_loginbytedance
public_repos418
account_age_days4,900

Engineering Quality

Are baseline engineering and documentation practices in place?

56Moderate · 19% of overall
How it's scored
0/24CI workflows
24/24Tests present
16/16Linter config — .flake8, .pylintrc
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://xpu-perf.ai/
10/10Repository description
10/10Topics — 1 topics
0/10Wiki
Inputs used
topicspython
has_wikino
homepagehttps://xpu-perf.ai/
docs_sitehttps://xpu-perf.ai/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

16Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
How it's scored
20.2/35Direct dependencies free of known advisories — 1 affected: numpy 1.19.2 (moderate 5.3)
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
35.3/40No advisories left outstanding — 1 advisory-carrying package(s) unaddressed past 90 days; oldest published 1,731 days ago
Inputs used
sourceosv
advisories1,859
affected_packages18
assessed_packages38
unassessed_packages52
affected_by_severitycritical 12, high 5, moderate 1
direct_affected_packages1
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories. Remaining weights renormalized. Matched 38 resolved dependencies against OSV. 52 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. Reachability is not analyzed.

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.

62Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
19.7/40Legible commit history — 37 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.37
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — projects/train_perf/Megatron-LM/megatron/core/datasets/Makefile
22/22Automated tests
11/11Lint / format config — .flake8, .pylintrc
11/11Static type checking — projects/train_perf/Megatron-LM/mypy.ini
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesprojects/train_perf/Megatron-LM/megatron/core/datasets/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsprojects/train_perf/Megatron-LM/mypy.ini
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (projects/train_perf/Megatron-LM/mypy.ini)
54.4/55Manageable file sizes — 8/757 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes132,791
source_files_sampled757
oversized_source_files8
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — projects/infer_perf/llm_perf/server/server.proto
0/20MCP server
40/40Runnable examples — examples, recipes
Inputs used
example_dirsexamples, recipes
has_mcp_signalno
api_schema_filesprojects/infer_perf/llm_perf/server/server.proto
interfaces_expected_ofnetwork-service

Key facts

383GitHub stars
15contributors
22commits, last 12 months
145days since last push
1releases
2bus factor
26open issues
PyPIpackage ecosystems

Data collection 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 573 advisories (lookup cap); the remainder are reported as unknown severity
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/14 18:46:43 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 130 ⇿
0Stars
130Forks
1Releases

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.

02040608010012010552023-042024-122026-08
Major 1Minor 0Patch 0

Each point covers 4 days.

Direct dependencies 5
RegistryPackageVersion constraintManifest
PyPIprettytable—pyproject.toml
PyPIjsonlines—pyproject.toml
PyPIflask—pyproject.toml
PyPIrequests—pyproject.toml
PyPInumpy<=1.26.4pyproject.toml
All dependencies 90

Full resolved dependency set from the GitHub dependency graph: 8 direct and 82 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIflask—direct
PyPIjsonlines—direct
PyPInumpy—direct
PyPInumpy1.19.2direct
PyPInumpy1.23.0direct
PyPInumpy1.23.4direct
PyPIprettytable—direct
PyPIrequests—direct
PyPIaccelerate—indirect
PyPIattrs21.2.0indirect
PyPIbackoff—indirect
PyPIbert—indirect
PyPIbert-tensorflow1.0.1indirect
PyPIblack23.1.0indirect
PyPIdecorator—indirect
PyPIeinops—indirect
PyPIflask-restful—indirect
PyPIfpdf—indirect
PyPIgoogle-api-python-client—indirect
PyPIgoogle-cloud-core2.3.2indirect
PyPIgraphviz0.8.4indirect
PyPIgrpcio—indirect
PyPIisort—indirect
PyPIkeras2onnx—indirect
PyPImatplotlib—indirect
PyPImatplotlib3.3.4indirect
PyPImatplotlib3.4.2indirect
PyPImultipledispatch—indirect
PyPImypy-protobuf2.8indirect
PyPInltk—indirect
PyPInvidia-modelopt—indirect
PyPIonnx—indirect
PyPIonnx1.12.0indirect
PyPIonnx1.13.1indirect
PyPIonnxconverter-common1.12.2indirect
PyPIonnxruntime—indirect
PyPIonnxruntime1.12.0indirect
PyPIopencv-python—indirect
PyPIopencv-python-headless—indirect
PyPIpackaging—indirect
PyPIpandas—indirect
PyPIpandas1.3.3indirect
PyPIpandas1.5.1indirect
PyPIprompt-toolkit—indirect
PyPIprotobuf—indirect
PyPIprotobuf3.19.4indirect
PyPIprotobuf3.19.6indirect
PyPIprotobuf3.20.2indirect
PyPIpsutil—indirect
PyPIpulp—indirect
PyPIpybind11—indirect
PyPIpydot—indirect
PyPIpytest—indirect
PyPIpytest-cov—indirect
PyPIpytest-mock—indirect
PyPIpytest-random-order—indirect
PyPIpyyaml—indirect
PyPIscikit-learn—indirect
PyPIscipy—indirect
PyPIsentencepiece—indirect
PyPIsentencepiece0.1.96indirect
PyPIsetuptools67.5indirect
PyPIsynr0.4indirect
PyPItensorflow—indirect
PyPItensorflow1.15.0indirect
PyPItensorflow2.4.0indirect
PyPItensorflow2.7.2indirect
PyPItensorflow2.8.4indirect
PyPItensorflow-datasets4.7.0indirect
PyPItensorstore—indirect
PyPItf2onnx—indirect
PyPItiktoken—indirect
PyPItokenization—indirect
PyPItorch—indirect
PyPItorch1.12.1indirect
PyPItorch1.13.1indirect
PyPItorch1.9.1indirect
PyPItorchvision—indirect
PyPItorchvision0.10.1indirect
PyPItorchvision0.13.1indirect
PyPItqdm—indirect
PyPItransformers—indirect
PyPItransformers4.40.0indirect
PyPItriton2.1.0indirect
PyPItyping-extensions3.7.4.3indirect
PyPIvirtualenv16.7.9indirect
PyPIwandb—indirect
PyPIwrapt—indirect
PyPIwrapt1.14.1indirect
PyPIzarr—indirect
Dependency advisories 18

This repository publishes no package the index resolves, so its own dependency graph was assessed — 38 packages, which also include development and test pins that never ship: 18 carry known advisories, of which 1 are direct. 52 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
black23.1.0indirectcritical426.3.1
onnx1.12.0indirectcritical261.22.0
onnx1.13.1indirectcritical241.22.0
tensorflow1.15.0indirectcritical7452.12.1
tensorflow2.4.0indirectcritical7602.12.1
tensorflow2.7.2indirectcritical942.12.1
tensorflow2.8.4indirectcritical462.12.1
torch1.12.1indirectcritical302.13.0
torch1.13.1indirectcritical282.13.0
torch1.9.1indirectcritical302.13.0
transformers4.40.0indirectcritical445.10.0
virtualenv16.7.9indirectcritical420.36.1
protobuf3.19.4indirecthigh66.33.5
protobuf3.19.6indirecthigh46.33.5
protobuf3.20.2indirecthigh46.33.5
sentencepiece0.1.96indirecthigh20.2.1
setuptools67.5indirecthigh683.0.0
numpy1.19.2directmoderate21.22

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

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 v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics.