Public record
Software health reportschema 0.11.0 · metrics 2.10.0 · 2026-07-16 06:27 UTC

aws / sagemaker-python-sdk

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

PythonApache-2.0★ 2,253 stars⑂ 1,282 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 98 out of 100, placing it in the Exceptional band. It scores highest on Sustainability & Governance (95/100) and lowest on Security (55/100). It was last updated today. 17 contributors account for most of its recent work.

98
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.

98
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 86 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

Amazon Web ServicesOrganization
16,076 followers551 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
PyPIsagemaker3.16.0-6830 days agoaiawsamazonmlmxnettensorflowpytorchhuggingface
PyPIsagemaker-core2.16.0-1060 days ago—
PyPIsagemaker-mlops1.16.0-240 days ago—
PyPIsagemaker-serve1.16.0-250 days ago—
PyPIsagemaker-train1.16.01,331,416240 days agoaiawsamazonmlmxnettensorflowpytorchhuggingface

Metrics by category

Vitality

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

93Exceptional · 21% of overall

Development activity

89Excellent
How it's scored
36/36Push recency — last push 0 days ago
24.9/36Commit cadence — 36/52 weeks with commits
18/18Commit volume — 304 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year304
human_commit_share—
days_since_last_push0
active_weeks_last_year36

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 1 days ago
27/27Release cadence — a release every ~7.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv2.257.5
releases_from_tagsno
days_since_latest_release1
mean_days_between_releases7.5
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?

94Exceptional · 17% of overall

Popularity & adoption

91Excellent
How it's scored
54.4/60Stars — 2,253 stars
25/25Forks — 1,282 forks
11.7/15Watchers — 126 watchers
Inputs used
forks1,282
stars2,253
watchers126
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_badges—
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services—
has_pull_request_templateyes

Ecosystem adoption (downloads)

100Exceptional
How it's scored
80/80Monthly downloads — 1,331,416 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagessagemaker, sagemaker-core, sagemaker-train
dependents—
ecosystemspypi
total_downloads—
monthly_downloads1,331,416
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?

95Exceptional · 23% of overall

Maintainer resilience (bus factor)

97Exceptional
How it's scored
54/54Bus factor — 17 contributor(s) cover half of all commits
19.7/22.5Commit distribution — top contributor authored 12% of commits
13.5/13.5Contributor breadth — 100 contributors
10/10OpenSSF Scorecard: Contributors — project has 12 contributing companies or organizations
Inputs used
bus_factor17
contributors_sampled100
top_contributor_share0.125

Issue & PR responsiveness

84Excellent
How it's scored
34.6/42Issue resolution — 82% of issues closed
24.9/30PR acceptance — 3,262/3,935 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
13.5/15OpenSSF Scorecard: Code-Review — Found 29/30 approved changesets -- score normalized to 9
Inputs used
merged_prs3,262
open_issues300
closed_issues1,401
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio0.824
closed_unmerged_prs673
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

100Exceptional
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
25/25Owner reach — 16,076 followers of aws
25/25Track record — 551 public repos, account ~13 yr old
Inputs used
followers16,076
owner_typeOrganization
is_verified—
owner_loginaws
public_repos551
account_age_days5,070
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 5 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 683 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_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

85Excellent · 19% of overall

Engineering practices

82Excellent
How it's scored
24/24CI workflows — 9 workflow(s)
24/24Tests present
16/16Linter config — .pylintrc, tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
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?

55Moderate · 16% of overall

Security posture

55Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
5.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.2/2.5CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6.8/7.5Code-Review — Found 29/30 approved changesets -- score normalized to 9
2.5/2.5Contributors — project has 12 contributing companies or organizations
0/10Dangerous-Workflow — dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
4.5/5SAST — SAST tool detected but not run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 146 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.5
Excluded from scoring (no data or not applicable): Packaging, 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.

83Excellent · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt present
0/40Legible commit history — no data
Inputs used
has_llms_txtyes
llms_txt_url—
legible_history_share—
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes7,702
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

74Good
How it's scored
18/18One-command bootstrap — docs/Makefile, sagemaker-core/docs/Makefile
22/22Automated tests
11/11Lint / format config — .pylintrc, tox.ini
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no data
0/8Automated maintenance — no data
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
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_share—
toolchain_manifests—
dependency_bot_commit_share—
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.

Code legibility for models

53Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
53.3/55Manageable file sizes — 38/1,197 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes1,579,353
source_files_sampled1,197
oversized_source_files38

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

2,253GitHub stars
100contributors
304commits, last 12 months
0days since last push
100releases
17bus factor
300open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 5.5 / 10
5.5aggregate

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 · 2026-07-16 06:25 UTC

10Binary-Artifactsno binaries found in the repo
7Branch-Protectionbranch protection is not maximal on development and all release branches
9CI-Tests29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
9Code-ReviewFound 29/30 approved changesets -- score normalized to 9
10Contributorsproject has 12 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
9SASTSAST tool detected but not run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities146 existing vulnerabilities detected
Direct dependencies 63
RegistryPackageVersion constraintManifest
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PyPIsagemaker-train>=1.16.0,<2.0.0pyproject.toml
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PyPIsagemaker-mlops>=1.16.0,<2.0.0pyproject.toml
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All dependencies 149

Full resolved dependency set from the GitHub dependency graph: 46 direct and 103 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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