#2021SC@SDUSC
What is it
是由 TensorFlow 开发的通用深度学习框架，它重点关注自然语言处理相关的序列建模方法，包括机器翻译、语音识别和语音合成等.Lingvo 模型由灵活且易于扩展的模块化构建块组成，实验配置集中且高度可定制。该框架直接支持分布式训练和量化推理，包含大量实用工具、辅助函数和最新研究思想的现有实现。
开发原则
creat by 知乎·雅各布
组件
Lingvo框架总览 项目源码链接
https://github.com/tensorflow/lingvohttps://github.com/tensorflow/lingvo
https://gitee.com/mirrors/lingvohttps://gitee.com/mirrors/lingvo
论文链接：1902.08295.pdf (arxiv.org)https://arxiv.org/pdf/1902.08295.pdf
安装There are two ways to set up Lingvo: installing a fixed version through pip, or cloning the repository and building it with bazel. Docker configurations are provided for each case.
If you would just like to use the framework as-is, it is easiest to just install it through pip. This makes it possible to develop and train custom models using a frozen version of the Lingvo framework. However, it is difficult to modify the framework code or implement new custom ops.
If you would like to develop the framework further and potentially contribute pull requests, you should avoid using pip and clone the repository instead.
pip:
The Lingvo pip package can be installed with pip3 install lingvo.
See the codelab for how to get started with the pip package.
From sources:
The prerequisites are:
- a TensorFlow 2.6 installation,
- a C++ compiler (only g++ 7.3 is officially supported), and
- the bazel build system.
Refer to docker/dev.dockerfile for a set of working requirements.
git clone the repository, then use bazel to build and run targets directly. The python -m module commands in the codelab need to be mapped onto bazel run commands.
分工lingvo由Models、Tasks、Layers、Input Generators、Params、Experiment Configuration、Job Runners、nestedMap和Custom ops九个组件组成。
我负责前四个组件。
