在一个系统上构建Tensorflow，在另一个系统上部署 [英] Build Tensorflow on one system, deploy on another

问题描述

如何将 TensorFlow 部署在与构建它的计算机不同的计算机上？需要复制哪些文件？在每台目标PC上从源代码构建都是不切实际的。就我而言，我需要从源代码构建，因为 TensorFlow 的标准安装并未针对我的目标进行优化（非GPU构建，但具有AVX / AVX2），不是那样应该有所作为。我正在建造&

How can I deploy TensorFlow on a different computer from the one I build it on? Which files need to be copied across? Building from source on each and every target PC is impractical. In my case I need to build from source since the standard install of TensorFlow is not optimized for my target (non-GPU build but with AVX/AVX2 available), not that that should make any difference. I am building & deploying on Windows PCs, which almost certainly will make a difference.

推荐答案

思考： python 。 Tensorflow 本质上是一个python软件包，并且python软件包使用 pip 安装。

Think: python. Tensorflow is essentially a python package, and python packages are installed with pip.

在这种特定情况下，使用 pip3 install可以很容易地在目标系统上安装 TensorFlow （1.5版）的标准安装- -根据标准TensorFlow指示升级tensorflow 。但是，当我测试了已经开发的示例时，由于 AVX 和 AVX2 指令而被警告安装不是最佳的可用，但未被使用。

In this specific case, the standard installation of TensorFlow (version 1.5) was easily installed on my target system using pip3 install --upgrade tensorflow as per standard TensorFlow instructions. But when I tested examples I had already developed I was warned that the install was not optimal, since AVX and AVX2 instructions were available, but not being used.

从源头重建 Tensorflow 来利用 AVX2 ，请遵循此处的说明，尤其是：

To rebuild Tensorflow from source to make use of AVX2, follow instructions here, in particular:

• 从github获取源代码： git clone https://github.com/tensorflow/tensorflow


• 选择Bazel或CMake构建选项（我选择了 CMake ，这需要 SWIG ）


• 自定义构建以指定使用 AVX 或 AVX2 （对我来说，我在 CMake 步骤中将 -Dtensorflow_WIN_CPU_SIMD_OPTIONS = / arch：AVX2 添加到选项中） / li>
  
• 构建tensorflow python pip软件包（ CMake 中的最后一条说明p指令）

• Obtain the source from github: git clone https://github.com/tensorflow/tensorflow
• Choose either the Bazel or CMake build option (I chose CMake, which required SWIG)
• Customise the build to specify use of AVX or AVX2 (for me, I added -Dtensorflow_WIN_CPU_SIMD_OPTIONS=/arch:AVX2 to the options during the CMake step)
• build the tensorflow python pip package (very last instruction in the CMake step-by-step instructions)

一旦您收到了包裹（滚轮或 .whl ）文件，将其移至目标PC，然后使用 pip3 install tensorflow-< version-specific-details> .whl 。

Once you have the package (a wheel or .whl) file, move it to the target PC, and install it using pip3 install tensorflow-<version-specific-details>.whl.

此过程已经过测试：

• 开发PC：Windows 7-64位，Python 3.6.4（64位），SWIG 3.0.12


• 目标PC：Windows 8.1 Pro（64位），Python 3.6.4（ 64位）

出于记录目的，使用 AVX2 指令我的网络培训速度提高了大约20％。另外，尽管已知的限制之一是需要使用Python 3.5进行CMake构建，但到目前为止，我发现使用Python 3.6.4并没有问题。

For the record, the use of AVX2 instructions gave me approximately a 20% speed increase on my network training. Also, despite one of the known limitations being the need to use Python 3.5 for the CMake build, I have found no issue (so far) using Python 3.6.4.

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