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)需要花费更长的时间。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所需的时间比Debian的安装要多70倍?
是否有任何方法可以使用Alpine作为基础映像来加快安装速度,或者有另一个与Alpine大小相当的基础映像更适合用于Pandas和Numpy等软件包?
Dockerfile.debian
FROM python:3.6.4-slim-jessie RUN pip install pandas
用Pandas和Numpy构建Debian图像:
[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
使用Pandas和Numpy建立高山图像:
[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的映像仅python pip用于安装以下.whl格式的软件包:
python pip
.whl
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,而源分发需要在安装之前进行构建。
轮包pandas和numpy基于高山平台,图像不支持。这就是为什么python pip在构建过程中使用它们进行安装时,我们总是从alpine的源文件中编译它们:
pandas
numpy
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过程中看到以下内部容器:
/ # 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Dockerfile稍作修改:
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
我们得到以下错误:
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pandasAlpine映像上安装的唯一方法是等到构建完成。
当然pandas,例如,如果要在CI中使用Alpine映像,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。
编辑: 如果您想使用Alpine图像,pandas可以拉我的nickgryg / alpine-pandas docker图像。这是pandas在Alpine平台上预编译的python图像。它应该可以节省您的时间。