PyRank
supersayan-labs

supersayan

A high-performance Python library for fully homomorphic encryption (FHE) in deep learning, seamlessly integrated with PyTorch.
PyPI GitHub
More links
Download Stats
Monthly downloads
119
Weekly downloads
50
GitHub stars
5

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

Package Information

Latest Version
0.1.3
Requires Python
>=3.9
Licence
Unknown
Last Release
1 year ago
First Release
August 28, 2025
Total Releases
4
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
10
Popularity
10
Adoption
20
Community
0
Provenance
0
How the maintenance status was determined
Last commit
414 days ago
Monthly downloads
119
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
119
Stars
5
Stars per 1k downloads
42.02
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
7
Month daily avg
4
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
>=3.9
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
Not specified
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
Package does not declare a license.
How the release cadence was determined
Last release
414 days ago
Total releases
4
Releases/year
3.5
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
3
Monthly downloads
119
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
10
 
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

No OS data available

Python Versions

No Python version data available

Top Countries

1. US US
2
2. DE DE
1

Weekly Downloads

GitHub Stars

Dependencies

12
6 optional dependencies
  • build>=0.10.0[publish]
    extra == "publish"
  • pytest>=7.0.0[dev]
    extra == "dev"
  • pytest-benchmark>=4.0.0[dev]
    extra == "dev"
  • ruff>=0.4.0[dev]
    extra == "dev"
  • scalene>=1.5.51[dev]
    extra == "dev"
  • twine>=4.0.0[publish]
    extra == "publish"

Used By

0
Not used by any tracked packages

Repository details

Forks
0
Issues
0
Last Commit
1 year ago
Contributors
3
Open PRs
0
Archived
No

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Keywords
homomorphic-encryption FHE deep-learning pytorch privacy-preserving cryptography neural-networks secure-computation machine-learning julia
Sources and methodology