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A python toolkit for Trajectory Deep Learning.


License Docs Python PyTorch Lightning

TrajDL提供了轨迹数据挖掘领域中的多个SOTA深度学习模型的实现,为研究人员、工程师提供易用、高效、可靠的开发工具,可以快速开展实验和应用开发。TrajDL有几个关键特性:

  • 基于Arrow,Pytorch和Lightning

    TrajDL的数据部分构建在Arrow之上,模型部分构建在Pytorch之上,训练与验证流程构建在Lightning之上,充分结合各个框架工具的优势。

  • 高效的工具

    TrajDL提供了高效的工具,比如高效的Dataset,Tokenizer,GridSystem。出色的零拷贝特性可以显著降低数据的处理时间,节省内存使用。高效的Tokenizer和GridSystem可以随时转换数据,无需预先处理数据。

  • 可扩展性

    TrajDL高度模块化,不会约束用户的代码,用户可以随时从TrajDL里面取出自己需要使用的工具。TrajDL还打通了与Polars,Pandas,PyArrow等工具的接口,用户使用常用的科学计算工具处理后的数据可以轻松导入到TrajDL的数据体系。另外TrajDL同时支持API与配置文件两种方式开展实验与开发,尽可能提升用户体验。

  • 包含SOTA模型的实验复现脚本

    TrajDL提供了SOTA模型的复现脚本,用户可以通过脚本重现论文内的实验结果,部分场景下TrajDL具备比论文场景更优的效果。

文档 📕

简体中文文档参阅:简体中文文档

English documentation will be provided in subsequent versions.

Benchmark 🚀

scripts/benchmark目录下存储了TrajDL提供的benchmark复现脚本,针对各个论文使用TrajDL进行了实验复现。

License

本项目使用Apache License 2.0,详见LICENSE。

Metadata

Release files for trajdl 0.1.0

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Source distribution (sdist)

Source distribution for trajdl 0.1.0
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Table of built distributions (wheels) for trajdl 0.1.0
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trajdl-0.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-64 Details
trajdl-0.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64 Details
trajdl-0.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ x86-64 Details

Total release size: 1.8 MB

Release files / trajdl-0.1.0.tar.gz

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