问题描述
某些库(例如BLAS/LAPACK或某些优化库)会在编译时针对本地计算机体系结构进行优化.让我们以OpenBlas为例.有两种使用OpenBlas创建Docker容器的方法:
Some libraries such as BLAS/LAPACK or certain optimisation libraries get optimised for the local machine architecture upon compilation time. Lets take OpenBlas as an example. There exist two ways to create a Docker container with OpenBlas:
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使用Dockerfile，在其中您指定OpenBlas库的git克隆以及所有必要的编译标志和构建命令.
Use a Dockerfile in which you specify a git clone of the OpenBlas library together with all necessary compilation flags and build commands.
从Docker Hub中拉并运行其他人的Ubuntu + OpenBlas映像.
Pull and run someone else's image of Ubuntu + OpenBlas from the Docker Hub.
选项(1)确保为您的计算机构建并优化了OpenBlas.方案(2)呢?作为Docker的新手，我认为映像是固定的和静态的，因此运行该映像不会针对我的机器进行优化(可能基于AMD，而不是维护者的Intel CPU).让我感到困惑的是，图像 ipython/scipyserver 确实克隆了最新的OpenBlas在构建期间来自Github的大师.
Option (1) guarantees that OpenBlas is build and optimised for your machine. What about option (2)? As a Docker novice, I see images as something fixed and static, so running this image would not be optimised to my machine (which might be AMD-based instead of the maintainer's Intel CPU). What left me confused is that the image ipython/scipyserver does clone the latest OpenBlas master from Github during build.
我似乎误解了Docker映像和/或自动构建的概念，对此我将不胜感激.
I seem to misunderstand the concept of Docker images and/or automated builds, and I would very much appreciate a clarification.
推荐答案
不，我认为您非常正确.
No, I think you're pretty much right.
您链接到的映像是一个自动构建，因此将使用Docker构建服务器的内核和体系结构来编译OpenBlas.我确实注意到构建脚本设置了以下标志在构建OpenBlas时:
The image you link to is an automated build, so OpenBlas will be getting compiled using the kernel and architecture of the Docker build server. I did notice the build script sets the following flags when building OpenBlas:
DYNAMIC_ARCH=1 NO_AFFINITY=1 NUM_THREADS=32
大概可以使安装变得轻便，但会降低性能.另一种选择是为各种构建配置使用单独的映像.
Which presumably makes the install portable at a performance cost. An alternative would be to have a separate image for various build configurations.
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