PyRank
engineer1080

dedekind

Dedekind is a high-performance, differentiable programming language for scientific computing and machine learning. It features compile-time physical units, automatic differentiation, built-in solvers, and GPU/TPU acceleration.
PyPI GitHub
More links
Download Stats Examples Issues
Verified repo
Monthly downloads
347
Weekly downloads
127
GitHub stars
5

Downloads in the 30 and 7 days to October 10, 2026. Refreshed weekly.

Package Information

Latest Version
3.0.8
Requires Python
>=3.10
Licence
Apache License 2.0
Last Release
4 months ago
Author
Dedekind Contributors
First Release
May 23, 2026
Total Releases
9
Python Versions
3 3.10 3.11 3.12 3.13
No reported malware or high/critical advisories · Details

The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

Package Insights

What's this?
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
45
Popularity
10
Adoption
25
Community
0
Provenance
100
How the maintenance status was determined
Last commit
135 days ago
Monthly downloads
347
Active
Activity within 30 days
• Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
347
Stars
5
Stars per 1k downloads
14.41
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
• Unknown
Fewer than 1,000 monthly downloads, or no star data
Needs at least 1,000 monthly downloads to classify popularity.
How download momentum is calculated
Week daily avg
18
Month daily avg
12
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
More insights and metrics
Python version support tiers
requires_python
>=3.10
Cutting Edge
Minimum supported version is 3.14 or newer
Modern
Minimum supported version is 3.11 to 3.13
• Legacy
Supports 3.10 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
Apache License 2.0
• Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
135 days ago
Total releases
9
Releases/year
22.5
• Rapid
Last release within 365 days and > 12 releases/year
Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
2
Monthly downloads
347
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
• Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
8
 
Issue Burden
-
per 1k stars
Discussions
-
per 1k stars
Fork Ratio
-
contrib / forks
Stale Release
No
 
PR Velocity
Quiet
 
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

Linux 1

Python Versions

3.12
100.0% 1

Top Countries

1. US US
18
2. AT AT
2
3. DE DE
2
4. CA CA
1
5. SG SG
1

Weekly Downloads

GitHub Stars

Dependencies

3
16 optional dependencies

Used By

0
Not used by any tracked packages

Repository details

Forks
0
Issues
0
Last Commit
4 months ago
Contributors
2
Open PRs
0
Archived
No

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Keywords
scientific-computing domain-specific-language autograd units tensors physics machine-learning
Sources and methodology