openstarlab event modeliing package
Project description
OpenSTARLab Event Modeling package
Introduction
The OpenSTARLab Event package is the fundamental package for event modeling. It is designed to provide a simple and efficient way to train, inference, and simulate events. This package supports the data preprocessed by the OpenSTARLab PreProcessing package.
This package is continuously evolving to support future OpenSTARLab projects. If you have any suggestions or encounter any bugs, please feel free to open an issue.
Installation
- Install pytorch (recommended version 2.4.0 linux pip python3.8 cuda12.1)
pip install torch torchvision torchaudio
- To install this package via PyPI
pip install openstarlab-event
- To install manually
git clone git@github.com:open-starlab/Event.git
cd ./Event
pip install -e .
Current Features
Sports
RoadMap
- Release the package
- Provide pre-trained models
Other Information
Development torch version
version 2.4.0 linux pip python3.8 cuda12.1
Developer
 Calvin Yeung 💻 |
 Keisuke Fujii 🧑💻 |
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
openstarlab_event-0.1.17.tar.gz
(61.0 kB
view details)
Built Distribution
File details
Details for the file openstarlab_event-0.1.17.tar.gz.
File metadata
- Download URL: openstarlab_event-0.1.17.tar.gz
- Upload date:
- Size: 61.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.8.19
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ad92d055a644604deddaa5920f701784152bbe296620a60f156b7848c58e7236 |
|
| MD5 | cbf3ed6249b2b8fc0b246c2383170cee |
|
| BLAKE2b-256 | 5bc68c2f0f56449ee0927b69635a0efe59ae4824eb449d3db6b94ca13a6b9477 |
File details
Details for the file openstarlab_event-0.1.17-py3-none-any.whl.
File metadata
- Download URL: openstarlab_event-0.1.17-py3-none-any.whl
- Upload date:
- Size: 91.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.8.19
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d265e954986eb68be2a9704734e6b391f81847d1747a4b5307f14689f480234f |
|
| MD5 | b93f8a8d313f084599fe6f21f56d5b42 |
|
| BLAKE2b-256 | 9ce77aa124a5f7f00e45f8c163a3d88939d41121ffafcd63bdf80b640456bd84 |
