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Merge pull request #67 from Winifred43/master

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add more template doc
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栾鹏 2022-08-22 16:29:52 +08:00 committed by GitHub
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README.md
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@ -33,26 +33,31 @@ tips:
| :----- | :---- | :---- |
| linux | base | Custom stand-alone operating environment, free to implement all custom stand-alone functions |
| datax | import export | Import and export of heterogeneous data sources |
| media-download | data processing | Distributed download of media files |
| video-audio | data processing | Distributed extraction of audio from video |
| video-img | data processing | Distributed extraction of pictures from video |
| hadoop | data processing | hdfs,hbase,sqoop,spark client |
| sparkjob | data processing | spark serverless |
| volcanojob | data processing | volcano multi-machine distributed framework |
| ray | data processing | python ray multi-machine distributed framework |
| volcano | data processing | volcano multi-machine distributed framework |
| xgb | machine learning | xgb model training and inference |
| ray-sklearn | machine learning | sklearn based on ray framework supports multi-machine distributed parallel computing |
| pytorchjob-train | model train | Multi-machine distributed training of pytorch |
| horovod-train | model train | Multi-machine distributed training of horovod |
| tfjob | model train | Multi-machine distributed training of tensorflow |
| xgb | machine learning | xgb model training and inference |
| tfjob | deep learning | Multi-machine distributed training of tensorflow |
| pytorchjob | deep learning | Multi-machine distributed training of pytorch |
| horovod | deep learning | Multi-machine distributed training of horovod |
| paddle | deep learning | Multi-machine distributed training of paddle |
| mxnet | deep learning | Multi-machine distributed training of mxnet |
| kaldi | deep learning | Multi-machine distributed training of kaldi |
| tfjob-train | model train | distributed training of tensorflow: plain and runner |
| tfjob-runner | model train | distributed training of tensorflow: runner method |
| tfjob-plain | model train | distributed training of tensorflow: plain method |
| kaldi-train | model train | Multi-machine distributed training of kaldi |
| tf-model-evaluation | model evaluate | distributed model evaluation of tensorflow2.3 |
| tf-offline-predict | model inference | distributed offline model inference of tensorflow2.3 |
| model-offline-predict | model inference | distributed offline model inference of framework |
| deploy-service | model deploy | deploy inference service |
| model-register | model service | register model to platform |
| model-offline-predict | model service | distributed offline model inference of framework |
| deploy-service | model service | deploy inference service |
| media-download | multimedia data processing | Distributed download of media files |
| video-audio | multimedia data processing | Distributed extraction of audio from video |
| video-img | multimedia data processing | Distributed extraction of pictures from video |
| object-detection-on-darknet | machine vision | object-detection with darknet yolov3 |
| ner |natural language | Named Entity Recognition |
# Deploy

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README_CN.md
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| :----- | :---- | :---- |
| 自定义镜像 | 基础命令 | 完全自定义单机运行环境，可自由实现所有自定义单机功能 |
| datax | 导入导出 | 异构数据源导入导出 |
| media-download | 数据处理 | 分布式媒体文件下载 |
| video-audio | 数据处理 | 分布式视频提取音频 |
| video-img | 数据处理 | 分布式视频提取图片 |
| hadoop | 数据处理 | hadoop大数据组件，hdfs,hbase,sqoop,spark |
| sparkjob | 数据处理 | spark serverless 分布式数据计算 |
| ray | 数据处理 | python ray框架 多机分布式功能，适用于超多文件在多机上的并发处理 |
| xgb | 机器学习 | xgb模型训练 |
| ray-sklearn | 机器学习 | 基于ray框架的sklearn支持算法多机分布式并行计算 |
| volcano | 数据处理 | volcano框架的多机分布式，可自由控制代码，利用环境变量实现多机worker的工作与协同 |
| pytorchjob-train | 训练 | pytorch的多机多卡分布式训练 |
| horovod-train | 训练 | horovod的多机多卡分布式训练 |
| tfjob | 训练 | tf分布式训练，k8s云原生方式 |
| tfjob-train | 训练 | tf分布式训练，内部支持plain和runner两种方式 |
| tfjob-runner | 训练 | tf分布式-runner方式 |
| tfjob-plain | 训练 | tf分布式-plain方式 |
| kaldi-train | 训练 | kaldi音频分布式训练 |
| tf-model-evaluation | 模型评估 | tensorflow2.3分布式模型评估 |
| tf-offline-predict | 离线推理 | tf模型离线推理 |
| model-offline-predict | 离线推理 | 所有框架的分布式模型离线推理 |
| deploy-service | 服务部署 | 部署云原生推理服务 |
| ray-sklearn | 机器学习 | 基于ray框架的sklearn支持算法多机分布式并行计算 |
| xgb | 机器学习 | xgb模型训练 |
| tfjob | 深度学习 | tf分布式训练，k8s云原生方式 |
| pytorchjob | 深度学习 | pytorch的多机多卡分布式训练 |
| horovod-train | 深度学习 | horovod的多机多卡分布式训练 |
| horovod | 深度学习 | horovod 的多机多卡分布式训练 |
| paddle | 深度学习 | paddle的多机多卡分布式训练 |
| mxnet | 深度学习 | mxnet的多机多卡分布式训练 |
| kaldi | 深度学习 | kaldi的多机多卡分布式训练 |
| tfjob-train | tf分布式 | tf分布式训练，内部支持plain和runner两种方式 |
| tfjob-runner | tf分布式 | tf分布式-runner方式 |
| tfjob-plain | tf分布式 | tf分布式-plain方式 |
| kaldi-train | tf分布式 | kaldi音频分布式训练 |
| tf-model-evaluation | tf分布式 | tensorflow2.3分布式模型评估 |
| tf-offline-predict | tf分布式 | tf模型离线推理 |
| model-register | 模型服务化 | 注册模型 |
| model-offline-predict | 模型服务化 | 所有框架的分布式模型离线推理 |
| deploy-service | 模型服务化 | 部署云原生推理服务 |
| media-download | 多媒体处理 | 分布式媒体文件下载 |
| video-audio | 多媒体处理 | 分布式视频提取音频 |
| video-img | 多媒体处理 | 分布式视频提取图片 |
| object-detection | 机器视觉 | 基于darknet yolov3 的目标识别|
| ner | 自然语言| 命名实体识别 |
# 平台部署