为什么在Alpine Linux上安装Pandas会花费很多时间 [英] Why does it take ages to install Pandas on Alpine Linux

问题描述

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(它的依赖项)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用来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所花的时间比Debian上安装的时间多70倍?

I've noticed that installing Pandas and Numpy (it's dependency) in a Docker container using the base OS Alpine vs. CentOS or Debian takes much longer. I created a little test below to demonstrate the time difference. Aside from the few seconds Alpine takes to update and download the build dependencies to install Pandas and Numpy, why does the setup.py take around 70x more time than on Debian install?

是否有任何方法可以使用Alpine作为基本映像来加快安装速度，或者是否有另一个与Alpine大小相当的基本映像，更适合用于Pandas和Numpy等软件包?

Is there any way to speed up the install using Alpine as the base image or is there another base image of comparable size to Alpine that is better to use for packages like Pandas and Numpy?

Dockerfile.debian

FROM python:3.6.4-slim-jessie

RUN pip install pandas

使用Pandas& amp;构建Debian图像脾气暴躁:

[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

使用Pandas& amp;制作高山图像脾气暴躁:

[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

推荐答案

基于Debian的映像仅使用python pip安装.whl格式的软件包:

Debian based images use only python pip to install packages with .whl format:

  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格式是一种比每次都从源代码重新构建更快，更可靠的安装Python软件的方法. WHL文件仅需移动到要安装的目标系统上的正确位置，而源发行版则需要在安装之前进行构建.

WHL format was developed as a quicker and more reliable method of installing Python software than re-building from source code every time. WHL files only have to be moved to the correct location on the target system to be installed, whereas a source distribution requires a build step before installation.

基于Alpine平台的图像中不支持车轮套件pandas和numpy.这就是为什么在构建过程中使用python pip安装它们时，我们总是从alpine的源文件中编译它们的原因:

Wheel packages pandas and numpy are not supported in images based on Alpine platform. That's why when we install them using python pip during the building process, we always compile them from the source files in alpine:

  Downloading pandas-0.22.0.tar.gz (11.3MB)
  Downloading numpy-1.14.1.zip (4.9MB)

在图像构建过程中，我们可以在容器中看到以下内容:

and we can see the following inside container during the image building:

/ # 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映像上安装pandas的唯一方法是等待构建完成.

Unfortunately, the only way to install pandas on an Alpine image is to wait until build finishes.

当然，例如，如果要在CI中将Alpine映像与pandas一起使用，最好的方法是将其编译一次，将其推送到任何注册表中，然后将其用作满足您需要的基础映像.

Of course if you want to use the Alpine image with pandas in CI for example, the best way to do so is to compile it once, push it to any registry and use it as a base image for your needs.

如果您想在pandas中使用Alpine图片，可以拉我的 nickgryg/alpine- pandas docker image.这是在Alpine平台上带有预编译的pandas的python图像.这样可以节省您的时间.

If you want to use the Alpine image with pandas you can pull my nickgryg/alpine-pandas docker image. It is a python image with pre-compiled pandas on the Alpine platform. It should save your time.

这篇关于为什么在Alpine Linux上安装Pandas会花费很多时间的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！