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**二. 模型优化**
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Paddle Lite 框架拥有优秀的加速、优化策略及实现,包含量化、子图融合、Kernel 优选等优化手段。优化后的模型更轻量级,耗费资源更少,并且执行速度也更快。
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这些优化通过 Paddle Lite 提供的 opt 工具实现。opt 工具还可以统计并打印出模型中的算子信息,并判断不同硬件平台下 Paddle Lite 的支持情况。您获取 PaddlePaddle 格式的模型之后,一般需要通该 opt 工具做模型优化。opt 工具的下载和使用,请参考[模型优化方法](https://paddle-lite.readthedocs.io/zh/latest/user_guides/model_optimize_tool.html)。
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这些优化通过 Paddle Lite 提供的 opt 工具实现。opt 工具还可以统计并打印出模型中的算子信息,并判断不同硬件平台下 Paddle Lite 的支持情况。您获取 PaddlePaddle 格式的模型之后,一般需要通该 opt 工具做模型优化。opt 工具的下载和使用,请参考[模型优化方法](https://paddle-lite.readthedocs.io/zh/release-v2.10_a/user_guides/model_optimize_tool.html)。
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**三. 下载或编译**
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Paddle Lite 提供了 Android/iOS/X86 平台的官方 Release 预测库下载,我们优先推荐您直接下载 [Paddle Lite 预编译库](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html)。
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您也可以根据目标平台选择对应的[源码编译方法](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#id2)。Paddle Lite 提供了源码编译脚本,位于 `./lite/tools/` 文件夹下,只需要[准备环境](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html)和[调用编译脚本](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#id2)两个步骤即可一键编译得到目标平台的 Paddle Lite 预测库。
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Paddle Lite 提供了 Android/iOS/X86 平台的官方 Release 预测库下载,我们优先推荐您直接下载 [Paddle Lite 预编译库](https://paddle-lite.readthedocs.io/zh/release-v2.10_a/quick_start/release_lib.html)。
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您也可以根据目标平台选择对应的[源码编译方法](https://paddle-lite.readthedocs.io/zh/release-v2.10_a/quick_start/release_lib.html#id2)。Paddle Lite 提供了源码编译脚本,位于 `./lite/tools/` 文件夹下,只需要[准备环境](https://paddle-lite.readthedocs.io/zh/release-v2.10_a/source_compile/compile_env.html)和[调用编译脚本](https://paddle-lite.readthedocs.io/zh/release-v2.10_a/quick_start/release_lib.html#id2)两个步骤即可一键编译得到目标平台的 Paddle Lite 预测库。
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**四. 预测示例**
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Paddle Lite 提供了 C++、Java、Python 三种 API,并且提供了相应 API 的完整使用示例:
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Paddle Lite is designed to support a wide range of hardwares and devices, and it enables mixed execution of a single model on multiple devices, optimization on various phases, and leight-weighted applications on devices.
As is shown in the figure above, analysis phase includes Machine IR module, and it enables optimizations like Op fusion and redundant computation pruning. Besides, excecution phase only involves Kernal execution, so it can be deployed on its own to ensure maximum light-weighted deployment.
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