Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-09 02:08 UTC

aws / sagemaker-python-sdk

A library for training and deploying machine learning models on Amazon SageMaker

PythonApache-2.0★ 2,260 stars⑂ 1,298 forkssince Nov 2017View on GitHub ↗
KindLibraryhow this is determined
#aws#mxnet#tensorflow#machine-learning#python#pytorch#sagemaker#huggingface

aws/sagemaker-python-sdk holds a health index of 96 out of 100, placing it in the Exceptional band. It scores highest on Vitality (95/100) and lowest on Security (42/100). It was last updated today. 17 contributors account for most of its recent work.

96
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.

96
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 83 is calibrated to 96 on the published index scale (record calibration 2026-08-02).

Ownership

Amazon Web ServicesOrganization · verified domain
16,712 followers552 public repossince Aug 2012

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIsagemaker3.21.017,708,04868914 days agoaiawsamazonmlmxnettensorflowpytorchhuggingface
PyPIsagemaker-core2.21.05,683,76511114 days ago—
PyPIsagemaker-mlops1.21.01,001,3562914 days ago—
PyPIsagemaker-serve1.21.0998,5423014 days ago—
PyPIsagemaker-train1.21.0988,8832914 days agoaiawsamazonmlmxnettensorflowpytorchhuggingface

Metrics by category

Vitality

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

95Exceptional · 21% of overall

Development activity

92Excellent
How it's scored
36/36Push recency — last push 0 days ago
29.1/36Commit cadence — 42/52 weeks with commits
18/18Commit volume — 406 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year406
human_commit_share0.97
days_since_last_push0
active_weeks_last_year42

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 14 days ago
27/27Release cadence — a release every ~4.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv3.21.0
releases_from_tagsno
days_since_latest_release14
mean_days_between_releases4.8

Community & Adoption

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

94Exceptional · 17% of overall

Popularity & adoption

91Excellent
How it's scored
54.4/60Stars — 2,260 stars
25/25Forks — 1,298 forks
11.6/15Watchers — 125 watchers
Inputs used
forks1,298
stars2,260
watchers125
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, readthedocs.org, shields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

100Exceptional
How it's scored
80/80Monthly downloads — 24,380,696 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagessagemaker, sagemaker-core, sagemaker-train
dependents—
ecosystemspypi
total_downloads—
monthly_downloads24,380,696
unverified_packages_excludedsagemaker-mlops, sagemaker-serve
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?

93Exceptional · 23% of overall

Maintainer resilience (bus factor)

97Exceptional
How it's scored
54/54Bus factor — 17 contributor(s) cover half of all commits
19.8/22.5Commit distribution — top contributor authored 12% of commits
13.5/13.5Contributor breadth — 99 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor17
contributors_sampled99
top_contributor_share0.122

Issue & PR responsiveness

76Good
How it's scored
36.1/42Issue resolution — 86% of issues closed
24.8/30PR acceptance — 3,399/4,104 decided PRs merged
3.7/13Newcomer PR acceptance — 2/7 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs3,399
open_issues242
closed_issues1,477
prs_merged_7d4
prs_decided_7d5
prs_merged_30d44
prs_decided_30d55
issue_closed_ratio0.859
closed_unmerged_prs705
first_time_authors_30d5
first_time_prs_merged_30d2
first_time_prs_decided_30d7

Ownership & stewardship

100Exceptional
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
25/25Owner reach — 16,712 followers of aws
25/25Track record — 552 public repos, account ~14 yr old
Inputs used
followers16,712
owner_typeOrganization
is_verifiedyes
owner_loginaws
public_repos552
account_age_days5,124

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 5 package(s) on pypi
35/35Publish recency — latest publish 14 days ago
20/20Version history — 689 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagessagemaker, sagemaker-core, sagemaker-mlops, sagemaker-serve, sagemaker-train
ecosystemspypi
any_deprecatedno
min_days_since_publish14

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall

Engineering practices

80Excellent
How it's scored
24/24CI workflows — 12 workflow(s)
24/24Tests present
16/16Linter config — .pylintrc, pyproject.toml ([tool.black]), sagemaker-core/pyproject.toml ([tool.black]), sagemaker-mlops/pyproject.toml ([tool.black]), sagemaker-serve/pyproject.toml ([tool.black]), sagemaker-train/pyproject.toml ([tool.black]), tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://sagemaker.readthedocs.io/
10/10Repository description
10/10Topics — 8 topics
0/10Wiki
Inputs used
topicsaws, mxnet, tensorflow, machine-learning, python, pytorch, sagemaker, huggingface
has_wikino
homepagehttps://sagemaker.readthedocs.io/
docs_sitehttps://sagemaker.readthedocs.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

42Weak · 16% of overall

Security posture

27At Risk
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
20/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsdocs/requirements.txt, pyproject.toml, sagemaker-core/pyproject.toml, sagemaker-mlops/pyproject.toml, sagemaker-serve/pyproject.toml, sagemaker-train/pyproject.toml
has_codeql_workflowyes
has_security_policyno
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages173
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:sagemaker@3.21.0 runtime dependency closure — what installing the published package pulls in — 173 packages. 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.

84Excellent · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — .github/copilot-instructions.md, AGENTS.md, CLAUDE.md, GEMINI.md
15/15Machine-readable docs (llms.txt) — llms.txt present
40/40Legible commit history — 97 of 97 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_url—
legible_history_share1
agent_instruction_files.github/copilot-instructions.md, AGENTS.md, CLAUDE.md, GEMINI.md
agent_instruction_max_bytes7,702

Verify loop (build / test / typecheck)

77Good
How it's scored
18/18One-command bootstrap — docs/Makefile, sagemaker-core/docs/Makefile
22/22Automated tests
11/11Lint / format config — .pylintrc, pyproject.toml ([tool.black]), sagemaker-core/pyproject.toml ([tool.black]), sagemaker-mlops/pyproject.toml ([tool.black]), sagemaker-serve/pyproject.toml ([tool.black]), sagemaker-train/pyproject.toml ([tool.black]), tox.ini
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 3 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesdocs/Makefile, sagemaker-core/docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.03

Code legibility for models

53Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
53.1/55Manageable file sizes — 43/1,257 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes1,579,352
source_files_sampled1,257
oversized_source_files43

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — notebooks, sample
Inputs used
example_dirsnotebooks, sample
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

2,260GitHub stars
99contributors
406commits, last 12 months
0days since last push
100releases
17bus factor
242open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • First-time contributor figures cover 12 of 24 authors (cap 12)
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/09 02:06:59 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 ★ / 1,298 ⇿
0Stars
1,298Forks
100Releases

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.

2004006008001,0001,2001,4001,29882019-092023-032026-09
Major 1Minor 56Patch 43

Each point covers 7 days.

Direct dependencies 63
RegistryPackageVersion constraintManifest
PyPIsagemaker-core>=2.21.0,<3.0.0pyproject.toml
PyPIsagemaker-train>=1.21.0,<2.0.0pyproject.toml
PyPIsagemaker-serve>=1.21.0,<2.0.0pyproject.toml
PyPIsagemaker-mlops>=1.21.0,<2.0.0pyproject.toml
PyPIboto3>=1.43.20,<2.0.0sagemaker-core/pyproject.toml
PyPIpydantic>=2.0.0,<3.0.0sagemaker-core/pyproject.toml
PyPIPyYAML>=6.0, <7.0sagemaker-core/pyproject.toml
PyPIjsonschema<5.0.0sagemaker-core/pyproject.toml
PyPIplatformdirs>=4.0.0, <5.0.0sagemaker-core/pyproject.toml
PyPIrich>=13.0.0, <15.0.0sagemaker-core/pyproject.toml
PyPImock>4.0, <5.0sagemaker-core/pyproject.toml
PyPIimportlib-metadata<7.0,>=1.4.0sagemaker-core/pyproject.toml
PyPItyping_extensions>=4.9.0sagemaker-core/pyproject.toml
PyPIpytz>=2021.1sagemaker-core/pyproject.toml
PyPIrequests>=2.20.0,<3.0.0sagemaker-core/pyproject.toml
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PyPIsmdebug_rulesconfig>=1.0.1sagemaker-core/pyproject.toml
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PyPIscipy>=1.5.0sagemaker-core/pyproject.toml
PyPIcloudpickle>=2.0.0sagemaker-core/pyproject.toml
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PyPIsagemaker_schema_inference_artifacts—sagemaker-serve/pyproject.toml
PyPIpytest—sagemaker-serve/pyproject.toml
PyPItqdm—sagemaker-serve/pyproject.toml
PyPIpsutil—sagemaker-serve/pyproject.toml
PyPItritonclient—sagemaker-serve/pyproject.toml
PyPIonnx—sagemaker-serve/pyproject.toml
PyPIonnxruntime—sagemaker-serve/pyproject.toml
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PyPIparamiko>=2.11.0sagemaker-train/pyproject.toml
PyPIjinja2>=3.0,<4.0sagemaker-train/pyproject.toml
PyPIsagemaker-mlflow>=0.0.1,<1.0.0sagemaker-train/pyproject.toml
PyPImlflow>=3.0.0,<4.0.0sagemaker-train/pyproject.toml
PyPInest_asyncio>=1.5.0sagemaker-train/pyproject.toml
PyPIrequests>=2.28.0sagemaker-train/pyproject.toml
PyPIpydantic>=2.0.0sagemaker-train/pyproject.toml
All dependencies 148

Full resolved dependency set from the GitHub dependency graph: 45 direct and 103 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIboto3—direct
PyPIbotocore—direct
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PyPIjinja2—direct
PyPIjsonschema—direct
PyPImlflow—direct
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PyPIpytz—direct
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PyPIrequests—direct
PyPIrich—direct
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PyPIbuild1.2.1indirect
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Dependency advisories 0

Installing pypi:sagemaker@3.21.0 pulls in 173 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

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

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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 — PyPI.