我注意到使用基本操作系统Alpine与CentOS或Debian在Docker容器中安装Pandas和Numpy（它的依赖关系）需要更长的时间。我在下面创建了一个小测试来演示时差。除了Alpine更新和下载构建依赖项以安装Pandas和Numpy的几秒钟之外，为什么setup.py需要比Debian安装多70倍的时间？
有没有办法加速使用Alpine作为基本图像的安装，或者是否有另一个与Alpine相当的基本图像，最好用于像Pandas和Numpy这样的软件包？
Dockerfile.debian
FROM python:3.6.4-slim-jessie
RUN pip install pandas
使用Pandas＆Numpy构建Debian映像：
[PandasDockerTest] time docker build -t debian-pandas -f Dockerfile.debian . --no-cache
Sending build context to Docker daemon 3.072kB
Step 1/2 : FROM python:3.6.4-slim-jessie
---> 43431c5410f3
Step 2/2 : RUN pip install pandas
---> Running in 2e4c030f8051
Collecting pandas
Downloading pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl (26.2MB)
Collecting numpy>=1.9.0 (from pandas)
Downloading numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl (12.2MB)
Collecting pytz>=2011k (from pandas)
Downloading pytz-2018.3-py2.py3-none-any.whl (509kB)
Collecting python-dateutil>=2 (from pandas)
Downloading python_dateutil-2.6.1-py2.py3-none-any.whl (194kB)
Collecting six>=1.5 (from python-dateutil>=2->pandas)
Downloading six-1.11.0-py2.py3-none-any.whl
Installing collected packages: numpy, pytz, six, python-dateutil, pandas
Successfully installed numpy-1.14.1 pandas-0.22.0 python-dateutil-2.6.1 pytz-2018.3 six-1.11.0
Removing intermediate container 2e4c030f8051
---> a71e1c314897
Successfully built a71e1c314897
Successfully tagged debian-pandas:latest
docker build -t debian-pandas -f Dockerfile.debian . --no-cache 0.07s user 0.06s system 0% cpu 13.605 total
Dockerfile.alpine
FROM python:3.6.4-alpine3.7
RUN apk --update add --no-cache g++
RUN pip install pandas
用Pandas＆Numpy建立阿尔卑斯山的形象：
[PandasDockerTest] time docker build -t alpine-pandas -f Dockerfile.alpine . --no-cache
Sending build context to Docker daemon 16.9kB
Step 1/3 : FROM python:3.6.4-alpine3.7
---> 4b00a94b6f26
Step 2/3 : RUN apk --update add --no-cache g++
---> Running in 4b0c32551e3f
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
(1/17) Upgrading musl (1.1.18-r2 -> 1.1.18-r3)
(2/17) Installing libgcc (6.4.0-r5)
(3/17) Installing libstdc++ (6.4.0-r5)
(4/17) Installing binutils-libs (2.28-r3)
(5/17) Installing binutils (2.28-r3)
(6/17) Installing gmp (6.1.2-r1)
(7/17) Installing isl (0.18-r0)
(8/17) Installing libgomp (6.4.0-r5)
(9/17) Installing libatomic (6.4.0-r5)
(10/17) Installing pkgconf (1.3.10-r0)
(11/17) Installing mpfr3 (3.1.5-r1)
(12/17) Installing mpc1 (1.0.3-r1)
(13/17) Installing gcc (6.4.0-r5)
(14/17) Installing musl-dev (1.1.18-r3)
(15/17) Installing libc-dev (0.7.1-r0)
(16/17) Installing g++ (6.4.0-r5)
(17/17) Upgrading musl-utils (1.1.18-r2 -> 1.1.18-r3)
Executing busybox-1.27.2-r7.trigger
OK: 184 MiB in 50 packages
Removing intermediate container 4b0c32551e3f
---> be26c3bf4e42
Step 3/3 : RUN pip install pandas
---> Running in 36f6024e5e2d
Collecting pandas
Downloading pandas-0.22.0.tar.gz (11.3MB)
Collecting python-dateutil>=2 (from pandas)
Downloading python_dateutil-2.6.1-py2.py3-none-any.whl (194kB)
Collecting pytz>=2011k (from pandas)
Downloading pytz-2018.3-py2.py3-none-any.whl (509kB)
Collecting numpy>=1.9.0 (from pandas)
Downloading numpy-1.14.1.zip (4.9MB)
Collecting six>=1.5 (from python-dateutil>=2->pandas)
Downloading six-1.11.0-py2.py3-none-any.whl
Building wheels for collected packages: pandas, numpy
Running setup.py bdist_wheel for pandas: started
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: finished with status 'done'
Stored in directory: /root/.cache/pip/wheels/e8/ed/46/0596b51014f3cc49259e52dff9824e1c6fe352048a2656fc92
Running setup.py bdist_wheel for numpy: started
Running setup.py bdist_wheel for numpy: still running...
Running setup.py bdist_wheel for numpy: still running...
Running setup.py bdist_wheel for numpy: still running...
Running setup.py bdist_wheel for numpy: finished with status 'done'
Stored in directory: /root/.cache/pip/wheels/9d/cd/e1/4d418b16ea662e512349ef193ed9d9ff473af715110798c984
Successfully built pandas numpy
Installing collected packages: six, python-dateutil, pytz, numpy, pandas
Successfully installed numpy-1.14.1 pandas-0.22.0 python-dateutil-2.6.1 pytz-2018.3 six-1.11.0
Removing intermediate container 36f6024e5e2d
---> a93c59e6a106
Successfully built a93c59e6a106
Successfully tagged alpine-pandas:latest
docker build -t alpine-pandas -f Dockerfile.alpine . --no-cache 0.54s user 0.33s system 0% cpu 16:08.47 total
基于Debian的图像仅使用python pip来安装.whl格式的包：
Downloading pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl (26.2MB)
Downloading numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl (12.2MB)
WHL格式是作为一种安装Python软件的更快，更可靠的方法而开发的，而不是每次从源代码重建。只需将WHL文件移动到要安装的目标系统上的正确位置，而源分发需要在安装之前进行构建步骤。
基于Alpine平台的图像不支持轮组pandas和numpy。这就是为什么当我们在构建过程中使用python pip安装它们时，我们总是从alpine的源文件中编译它们：
Downloading pandas-0.22.0.tar.gz (11.3MB)
Downloading numpy-1.14.1.zip (4.9MB)
我们可以在图像构建期间看到以下内部容器：
/ # ps aux
PID USER TIME COMMAND
1 root 0:00 /bin/sh -c pip install pandas
7 root 0:04 {pip} /usr/local/bin/python /usr/local/bin/pip install pandas
21 root 0:07 /usr/local/bin/python -c import setuptools, tokenize;__file__='/tmp/pip-build-en29h0ak/pandas/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n
496 root 0:00 sh
660 root 0:00 /bin/sh -c gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -DTHREAD_STACK_SIZE=0x100000 -fPIC -Ibuild/src.linux-x86_64-3.6/numpy/core/src/pri
661 root 0:00 gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -DTHREAD_STACK_SIZE=0x100000 -fPIC -Ibuild/src.linux-x86_64-3.6/numpy/core/src/private -Inump
662 root 0:00 /usr/libexec/gcc/x86_64-alpine-linux-musl/6.4.0/cc1 -quiet -I build/src.linux-x86_64-3.6/numpy/core/src/private -I numpy/core/include -I build/src.linux-x86_64-3.6/numpy/core/includ
663 root 0:00 ps aux
如果我们稍微修改Dockerfile：
FROM python:3.6.4-alpine3.7
RUN apk add --no-cache g++ wget
RUN wget https://pypi.python.org/packages/da/c6/0936bc5814b429fddb5d6252566fe73a3e40372e6ceaf87de3dec1326f28/pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl
RUN pip install pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl
我们收到以下错误：
Step 4/4 : RUN pip install pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl
---> Running in 0faea63e2bda
pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl is not a supported wheel on this platform.
The command '/bin/sh -c pip install pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl' returned a non-zero code: 1
不幸的是，在Alpine图像上安装pandas的唯一方法是等到构建完成。
当然，如果您想在CI中使用带有pandas的Alpine图像，最好的方法是将其编译一次，将其推送到任何注册表并将其用作基本图像以满足您的需求。
编辑：如果你想使用与pandas的阿尔卑斯山图像，你可以拉我的nickgryg/alpine-pandas码头图像。它是一个python图像，在Alpine平台上预编译了pandas。它应该节省你的时间。
[更新：]
答案：它没有！
在任何Alpine Dockerfile中你都可以做到*
RUN apk add py2-numpy@community py2-scipy@community py-pandas@edge
这是因为numpy，scipy和现在的pandas都是在alpine上预制的：
https://pkgs.alpinelinux.org/packages?name=*numpy
https://pkgs.alpinelinux.org/packages?name=*scipy&branch=edge
https://pkgs.alpinelinux.org/packages?name=*pandas&branch=edge
每次避免重建或使用Docker层的一种方法是使用预构建的原生Alpine Linux / .apk软件包，例如：
https://github.com/sgerrand/alpine-pkg-py-pandas
https://github.com/nbgallery/apks
你可以构建这些.apks并在你喜欢的Dockerfile中的任何地方使用它们:)
这也节省了你必须在事实之前将其他所有东西都烘焙到Docker镜像中 - 即预先构建你喜欢的任何Docker镜像的灵活性。
PS我在https://gist.github.com/jtlz2/b0f4bc07ce2ff04bc193337f2327c13b上放了一个Dockerfile存根，大致显示了如何构建图像。这些包括重要步骤（*）：
RUN echo "@community http://dl-cdn.alpinelinux.org/alpine/edge/community" >> /etc/apk/repositories
RUN apk update
RUN apk add --update --no-cache libgfortran