docs

Author: nan

docs/welcome.md

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 TorchPipe is a multi-instance pipeline parallel library that provides a seamless integration between lower-level acceleration libraries (such as TensorRT and OpenCV) and RPC frameworks. It achieves strict decoupling from these libraries, offering a thread-safe function interface for the PyTorch frontend at a higher level. At the same time, it enables users to extend the backend capabilities at a lower level. TorchPipe's design ensures high service throughput while meeting latency requirements.
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-This document is continuously updated to reflect code changes, allowing you to:
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-- Quickly get started with the TorchPipe library by learning from the TorchPipe documentation.
-- Become a contributor to the TorchPipe documentation by tracking updates and participating in open source development.
-- Join the TorchPipe community to meet peers, engage in discussions, and exchange knowledge.
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-Let's begin exploring the official TorchPipe documentation.
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i18n/zh/docusaurus-plugin-content-docs/current/welcome.md

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 # 欢迎查看 torchpipe 文档！
 torchpipe是一个独立作用于底层加速库（如tensorrt，opencv, CVCUDA，torchscript）以及 RPC（如thrift, gRPC）之间的多实例流水线并行库。在满足时延前提下最大限度挖掘服务吞吐能力。
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-整个方案集并发安全和全链路流水线调度等特点于一身，支持 NVIDIA 硬件平台, 兼顾了开发效率与性能提速的特点。
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 torchpipe 对外提供面向 PyTorch 前端的线程安全函数接口，对内则提供面向用户的细粒度后端扩展。
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-随着 torchpipe 官方版本及文档的更新，文档也会不断调整，你可以：
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-- 学习 torchpipe 文档，快速上手 torchpipe 框架
-- 参与开源共建、追踪文档更新，成为 torchpipe 文档贡献者
-- 加入 torchpipe 社区、结识同行并参与更多讨论及交流
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-现在，让我们开启 torchpipe 官方文档的学习之旅吧！
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