| Name | Version | Summary | date |
| custom-decision-trees |
3.0.0 |
A package for building customizable decision trees and random forests. |
2025-09-16 14:47:52 |
| admet-ai |
1.4.0 |
ADMET-AI |
2025-09-16 00:26:20 |
| zenml-nightly |
0.85.0.dev20250915 |
ZenML: Write production-ready ML code. |
2025-09-15 00:58:25 |
| apache-tvm-ffi |
0.1.0b0 |
tvm ffi |
2025-09-14 01:10:15 |
| effectful |
0.2.0 |
Metaprogramming infrastructure |
2025-09-10 21:38:51 |
| lightning-pose |
1.9.2 |
Semi-supervised pose estimation using pytorch lightning |
2025-09-10 16:37:13 |
| pet-mad |
1.4.2 |
A universal interatomic potential for advanced materials modeling |
2025-09-10 04:51:47 |
| caspian-ml |
1.1.0 |
A deep learning library focused entirely around NumPy. |
2025-09-10 00:37:31 |
| metatrain |
2025.10 |
Training and evaluating machine learning models for atomistic systems. |
2025-09-09 14:53:01 |
| swanlab |
0.6.9 |
Python library for streamlined tracking and management of AI training processes. |
2025-09-09 05:03:00 |
| lightning-action |
0.2.3 |
Action segmentation framework built with PyTorch Lightning |
2025-09-08 22:20:08 |
| phiml |
1.14.2 |
Unified API for machine learning |
2025-09-07 11:02:37 |
| framework3 |
1.0.20 |
A flexible framework for machine learning pipelines |
2025-09-06 21:00:47 |
| crypticorn |
2.20.0 |
Maximise Your Crypto Trading Profits with Machine Learning |
2025-09-06 16:25:46 |
| property-driven-ml |
0.2.7 |
A general framework for property-driven machine learning with logical constraints |
2025-09-06 13:38:45 |
| spatialreasoners |
1.0.1 |
SpatialReasoners: A framework for training Spatial Reasoning Models in any domain |
2025-09-05 20:25:55 |
| cutml |
0.1.0 |
Comprehensive Unified Traditional Machine Learning with Auto-Explainability |
2025-09-05 07:19:50 |
| h2o-featurestore |
2.1.0 |
Feature Store Client for Python |
2025-09-04 09:54:53 |
| xgrammar |
0.1.24 |
Efficient, Flexible and Portable Structured Generation |
2025-09-04 08:34:05 |
| rhai-innovation-mini-trainer |
0.1.1 |
Simple training repo which is used to house reference implementations of emerging training algorithms, such as Orthogonal Subspace Fine Tuning (OSFT). |
2025-09-03 02:40:08 |