Package Insights
Score based on PyPI data only - no linked source repository
N/A
Health Score
Not available
requires GitHub data
How the maintenance status was determined
Last release
64 days ago
Monthly downloads
26,167,170
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
26,167,170
Stars
Unknown
Stars per 1k downloads
—
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
No star data — can't compute stars-to-downloads ratio.
How download momentum is calculated
Week daily avg
665,967
Month daily avg
872,239
Momentum
-23.6%
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100Python version support tiers
requires_python
Not specified
Cutting Edge
Minimum supported version is 3.12 or newer
Modern
Minimum supported version is 3.9, 3.10, or 3.11
Legacy
Supports 3.8 or older (or Python 2)
•
Unknown
No requires_python specified or unparseable
Package does not declare a minimum Python version.
License categories
License
BSD
•
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
64 days ago
Total releases
40
Releases/year
2.7
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
Unknown
Monthly downloads
26,167,170
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
Contributor count is missing — this usually indicates the package has no linked GitHub repository.
Engagement
0
Issue Burden
-
per 1k stars
Discussions
-
per 1k stars
Fork Ratio
-
contrib / forks
Stale Release
No
PR Velocity
-
Weekly Downloads
GitHub Stars
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.
Downloads by OS
Linux
435.5K
Windows
13.3K
macOS
2.7K
Python Versions
3.12
61.8%
272.8K
3.10
27.5%
121.5K
3.11
6.8%
30.1K
3.13
2.6%
11.5K
3.14
1.3%
5.8K
Top Countries
1.
US
322.3K
US
2.
FI
42.6K
FI
3.
FR
14.7K
FR
4.
KR
14.3K
KR
5.
CN
6.1K
CN
6.
IN
5.1K
IN
7.
TW
4.5K
TW
8.
HK
4.0K
HK
9.
DE
3.7K
DE
10.
NL
3.1K
NL
Dependencies
0
No dependencies
Used By
270
- sglang
- pynvml
- flashinfer-python
- inference-gpu
- inference-cli
- nvitop
- nvidia-modelopt
- scalene
- aiperf
- dask-cuda
- tabpfn-common-utils
- swanlab
- bentoml
- codecarbon
- cuequivariance-ops-cu12
- inference
- dask-cudf-cu12
- ucxx-cu12
- nvidia-resiliency-ext
- xinference
- openrunner-sdk
- nni
- mani-skill
- coreason-runtime
- effgen
- axolotl
- whichllm
- cuequivariance-ops-cu13
- cudf-polars-cu12
- nsight-python
- tensorrt-llm
- vantage6-node
- optimum-benchmark
- python-pycg
- openllm
- pixlstash
- liblaf-nox-recipes
- nv-attestation-sdk
- humming-kernels
- lightly-train
- hint-ppi
- gpustack-runtime
- mani-skill-nightly
- visiontext
- anemoi-training
- inference-core
- tot-sdk
- autodraft-sd
- boltzgen
- inference-cpu
Package Information
License
BSD
First Release
June 13, 2011
Last Release
2 months ago
Last Commit
-
Latest Version
13.595.45
Total Releases
40
Contributors
-
Open PRs
-
Archived
-
Author
NVIDIA Corporation
Wheels
py3
Tracking Since
January 6, 2026
Data Updated
May 17, 2026
Data Freshness
Fresh