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add extensions
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.. toctree:: | ||
:maxdepth: 1 | ||
:caption: 目录: | ||
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zh_cn/tutorials/index | ||
zh_cn/models/index | ||
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# 代码结构概览 | ||
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* Coming soon!!! | ||
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实用工具 | ||
================================ | ||
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.. toctree:: | ||
:maxdepth: 1 | ||
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code_overview.md | ||
paddle_inference.md | ||
paddle_mobile_inference.md | ||
paddle_quantization.md | ||
multi_machine_training.md | ||
paddle_hub.md | ||
paddle_serving.md |
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# 多机训练 | ||
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分布式训练的高性能,是飞桨的核心优势技术之一,在分类任务上,分布式训练可以达到几乎线性的加速比。 | ||
[Fleet](https://github.com/PaddlePaddle/Fleet) 是用于 PaddlePaddle 分布式训练的高层 API,基于这套接口用户可以很容易切换到分布式训练程序。 | ||
为了可以同时支持单机训练和多机训练,[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 采用 Fleet API 接口,更多的分布式训练可以参考 [Fleet API设计文档](https://github.com/PaddlePaddle/Fleet/blob/develop/README.md)。 | ||
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# Paddle Hub | ||
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[PaddleHub](https://github.com/PaddlePaddle/PaddleHub) 是飞桨生态的预训练模型应用工具,开发者可以便捷地使用高质量的预训练模型结合 Fine-tune API 快速完成模型迁移到部署的全流程工作。 | ||
PaddleHub 收录了 [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 的所有预训练模型,更多使用细节请查看 [PaddleHub官网](https://www.paddlepaddle.org.cn/hub)。 |
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# Paddle-Lite | ||
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[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) 是飞桨推出的一套功能完善、易用性强且性能卓越的轻量化推理引擎。 | ||
轻量化体现在使用较少比特数用于表示神经网络的权重和激活,能够大大降低模型的体积,解决终端设备存储空间有限的问题,推理性能也整体优于其他框架。 | ||
[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 使用 Paddle-Lite 进行了[移动端模型的性能评估](../models/Mobile.md),具体流程参考 [Paddle-Lite 文档](https://paddle-lite.readthedocs.io/zh/latest/)。 |
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# 模型量化 | ||
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模型量化是 [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 的特色功能之一,支持动态和静态两种量化训练方式,对权重全局量化和 Channel-Wise 量化,同时以兼容 Paddle Mobile 的格式保存模型。 | ||
[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 使用该量化工具,量化了78.9%的mobilenet_v3_large_x1_0的蒸馏模型, 量化后SD855上预测速度从19.308ms加速到14.395ms,存储大小从21M减小到10M, top1识别准确率75.9%。 | ||
具体的训练方法可以参见 [PaddleSlim 量化训练](https://paddlepaddle.github.io/PaddleSlim/quick_start/quant_aware_tutorial.html)。 |
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# 模型服务化部署 | ||
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[Paddle Serving](https://github.com/PaddlePaddle/Serving) 旨在帮助深度学习开发者轻易部署在线预测服务,支持一键部署工业级的服务能力、客户端和服务端之间高并发和高效通信、并支持多种编程语言开发客户端等特点,详细使用请参考 [Paddle Serving 相关文档](https://github.com/PaddlePaddle/Serving)。 | ||
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