Name | Version | Summary | date |
superduper-mongodb |
0.4.1 |
SuperDuper MongoDB is a Python library that provides a high-level API for working with MongoDB. It is built on top of pymongo and provides a more user-friendly interface for working with MongoDB. |
2024-11-16 19:59:28 |
oemer |
0.1.8 |
End-to-end Optical Music Recognition (OMR) system. |
2024-11-16 04:27:36 |
lancedb |
0.16.0 |
lancedb |
2024-11-15 20:54:31 |
jaclang |
0.7.26 |
Jac is a unique and powerful programming language that runs on top of Python, offering an unprecedented level of intelligence and intuitive understanding. |
2024-11-15 20:29:20 |
polyaxon |
2.5.2 |
Command Line Interface (CLI) and client to interact with Polyaxon API. |
2024-11-15 15:34:35 |
haupt |
2.5.2 |
Lineage metadata API, artifacts streams, sandbox, ML-API, and spaces for Polyaxon. |
2024-11-15 15:34:29 |
superduper-ibis |
0.4.1 |
Superduper ibis is a plugin for ibis-framework that allows you to use Superduper as a backend for your ibis queries. |
2024-11-15 10:43:16 |
flowcept |
0.6.11 |
Capture and query workflow provenance data using data observability |
2024-11-14 22:49:51 |
rasa-pro |
3.10.10 |
State-of-the-art open-core Conversational AI framework for Enterprises that natively leverages generative AI for effortless assistant development. |
2024-11-14 16:37:09 |
hypster |
0.2.31 |
A flexible configuration system for Python projects |
2024-11-14 09:44:13 |
scikit-base |
0.12.0 |
Base classes for sklearn-like parametric objects |
2024-11-13 21:49:13 |
pylance |
0.19.2 |
python wrapper for Lance columnar format |
2024-11-13 20:38:25 |
reinforced-lib |
1.1.4 |
Reinforcement learning library |
2024-11-13 18:30:55 |
tsml |
0.5.0 |
A development sandbox for time series machine learning algorithms. |
2024-11-13 11:23:30 |
superduper-framework |
0.4.5 |
🔮 Bring AI to your favourite database 🔮 |
2024-11-13 10:30:09 |
rlmodule |
0.1.6.3 |
Flexible reinforcement learning models instantiators library |
2024-11-12 10:27:11 |
skforecast |
0.14.0 |
Skforecast is a Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others. |
2024-11-11 13:34:49 |
skfolio |
0.5.1 |
Portfolio optimization built on top of scikit-learn |
2024-11-09 11:31:38 |
decode-mcd |
1.0.0rc0 |
MCD generates counterfactuals that meet multiple, customizable objectives in both the feature and performance spaces. |
2024-11-08 12:29:54 |
zigzag-dse |
3.7.4 |
ZigZag - Deep Learning Hardware Design Space Exploration |
2024-11-08 10:06:43 |