Artificial Intelligence(AI) and Machine Learning(ML) are very popular these days. They power a wide spectrum of use-cases ranging from self-driving cars to drug discovery. AI and ML have a bright and thriving future ahead of them.
On the other hand, Docker revolutionized the computing world through the introduction of ephemeral lightweight containers. Containers package all the software required to run inside an
Let’s get started with building a Python Data Science container.
Python is fast becoming the go-to language for data scientists. For this reason, we are going to use Python to build our Data Science container.
Alpine Linux is a tiny Linux distribution designed for power users who appreciate security, simplicity, and resource efficiency.
As claimed by Alpine:
Small. Simple. Secure. Alpine Linux is a security-oriented, lightweight Linux distribution based on musl libc and busybox.
The Alpine image is surprisingly tiny with a size of no more than 8MB for containers. Minimal packages are installed to reduce the attack surface on the underlying container, making Alpine a good choice for our data science container.
Downloading and Running an Alpine Linux container is as simple as:
$ docker container run --rm alpine:latest cat /etc/os-release
In our, Dockerfile we can simply use the Alpine base image as:
FROM alpine:latest
Now, let’s work our way through the Dockerfile:
FROM alpine:latestLABEL MAINTAINER="Faizan Bashir <faizan.ibn.bashir@gmail.com>"# Linking of locale.h as xlocale.h# This is done to ensure successfull install of python numpy package# see https://forum.alpinelinux.org/comment/690#comment-690 for more information.WORKDIR /var/www/# SOFTWARE PACKAGES# * musl: standard C library# * lib6-compat: compatibility libraries for glibc# * linux-headers: commonly needed, and an unusual package name from Alpine.# * build-base: used so we include the basic development packages (gcc)# * bash: so we can access /bin/bash# * git: to ease up clones of repos# * ca-certificates: for SSL verification during Pip and easy_install# * freetype: library used to render text onto bitmaps, and provides support font-related operations# * libgfortran: contains a Fortran shared library, needed to run Fortran# * libgcc: contains shared code that would be inefficient to duplicate every time as well as auxiliary helper routines and runtime support# * libstdc++: The GNU Standard C++ Library. This package contains an additional runtime library for C++ programs built with the GNU compiler# * openblas: open source implementation of the BLAS(Basic Linear Algebra Subprograms) API with many hand-crafted optimizations for specific processor types# * tcl: scripting language# * tk: GUI toolkit for the Tcl scripting language# * libssl1.0: SSL shared librariesENV PACKAGES="\dumb-init \musl \libc6-compat \linux-headers \build-base \bash \git \ca-certificates \freetype \libgfortran \libgcc \libstdc++ \openblas \tcl \tk \libssl1.0 \"# PYTHON DATA SCIENCE PACKAGES# * numpy: support for large, multi-dimensional arrays and matrices# * matplotlib: plotting library for Python and its numerical mathematics extension NumPy.# * scipy: library used for scientific computing and technical computing# * scikit-learn: machine learning library integrates with NumPy and SciPy# * pandas: library providing high-performance, easy-to-use data structures and data analysis tools# * nltk: suite of libraries and programs for symbolic and statistical natural language processing for EnglishENV PYTHON_PACKAGES="\numpy \matplotlib \scipy \scikit-learn \pandas \nltk \"RUN apk add --no-cache --virtual build-dependencies python --update py-pip \&& apk add --virtual build-runtime \build-base python-dev openblas-dev freetype-dev pkgconfig gfortran \&& ln -s /usr/include/locale.h /usr/include/xlocale.h \&& pip install --upgrade pip \&& pip install --no-cache-dir $PYTHON_PACKAGES \&& apk del build-runtime \&& apk add --no-cache --virtual build-dependencies $PACKAGES \&& rm -rf /var/cache/apk/*CMD ["python"]
The FROM
directive is used to set alpine:latest
as the base image.
Using the WORKDIR directive, we set the /var/www
as the working directory for our container.
The ENV PACKAGES
lists the software packages required for our container like git
, blas
, and libgfortran
.
The Python packages for our Data Science container are defined in the ENV PACKAGES.
We have combined all the commands under a single Dockerfile RUN
directive to reduce the number of layers. This will, in turn, help with reducing the resultant image size.
Now that we have our Dockerfile defined navigate to the folder with the Dockerfile using the terminal. Then, build the image using:
$ docker build -t faizanbashir/python-datascience:2.7 -f Dockerfile .
The -t
flag is used to name a tag in the ‘name:tag’ format. The -f
tag is used to define the name of the Dockerfile (Default is PATH/Dockerfile
).
We have successfully built and tagged the docker image, now we can run the container using the following command:
$ docker container run --rm -it faizanbashir/python-datascience:2.7 python
Voila, we are greeted by the sight of a Python shell ready to perform all kinds of cool Data Science stuff.
Python 2.7.15 (default, Aug 16 2018, 14:17:09) [GCC 6.4.0] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>>
Our container comes with Python 2.7, but don’t be sad if you want work with Python 3.6 as we do have the Dockerfile for Python 3.6:
{% gist 9443a7149cc53f81d84d0d356f871ec7 %}
Build and tag the image:
$ docker build -t faizanbashir/python-datascience:3.6 -f Dockerfile .
Run the container:
$ docker container run --rm -it faizanbashir/python-datascience:3.6 python
You now have a ready-to-use container for all kinds of cool Data Science stuff.
However, all the information above assumes you have the time and resources to set all this stuff up. In the case that you don’t, you can pull the existing images that I have already built and pushed to Docker’s registry Docker Hub using:
# For Python 2.7 pull
$ docker pull faizanbashir/python-datascience:2.7
# For Python 3.6 pull
$ docker pull faizanbashir/python-datascience:3.6
After pulling the images, you can use the image, extend the same in your Dockerfile file, or use it as an image in your docker-compose or stack file.
Our Python Data Science container makes use of the following Python packages:
I hope this article helped you with how to build containers for your Data Science projects.
You can check out the code at faizanbashir/python-datascience.
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