TL;DR
Artificial Intelligence(AI) and Machine Learning(ML) are literally on fire these days. Powering a wide spectrum of use-cases ranging from self-driving cars to drug discovery and to God knows what. 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 basically package all the software required to run inside an image(a bunch of readonly layers) with a COW(Copy on Write) layer to persist the data.
Enough talk let's get started with building a Python data science container.
Python Data Science Packages
Our Python data science container makes use of the following super cool python packages:
- NumPy: NumPy or Numeric Python supports large, multi-dimensional arrays and matrices. It provides fast precompiled functions for mathematical and numerical routines. In addition, NumPy optimizes Python programming with powerful data structures for efficient computation of multi-dimensional arrays and matrices.
- SciPy: SciPy provides useful functions for regression, minimization, Fourier-transformation, and many more. Based on NumPy, SciPy extends its capabilities. SciPy's main data structure is again a multidimensional array, implemented by Numpy. The package contains tools that help with solving linear algebra, probability theory, integral calculus, and many more tasks.
- Pandas: Pandas offer versatile and powerful tools for manipulating data structures and performing extensive data analysis. It works well with incomplete, unstructured, and unordered real-world data - and comes with tools for shaping, aggregating, analyzing, and visualizing datasets.
- SciKit-Learn: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. It is one of the best-known machine-learning libraries for python. The Scikit-learn package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. The primary emphasis is upon ease of use, performance, documentation, and API consistency. With minimal dependencies and easy distribution under the simplified BSD license, SciKit-Learn is widely used in academic and commercial settings. Scikit-learn exposes a concise and consistent interface to the common machine learning algorithms, making it simple to bring ML into production systems.
- Matplotlib: Matplotlib is a Python 2D plotting library, capable of producing publication quality figures in a wide variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the Jupyter notebook, web application servers, and four graphical user interface toolkits.
- NLTK: NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.
Building the Data Science Container
Python is fast becoming the go-to language for data scientists and for this reason we are going to use Python as the language of choice for building our data science container.
The Base Alpine Linux Image
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. With minimal packages installed to reduce the attack surface on the underlying container. This makes Alpine an image of 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
Talk is cheap let's build the Dockerfile
Now let's work our way through the Dockerfile.
FROM alpine:latest
LABEL 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 libraries
ENV 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 English
ENV 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 which in turn helps in reducing the resultant image size.
Building and tagging the image
Now that we have our Dockerfile defined, navigate to the folder with the Dockerfile using the terminal and build the image using the following command:
$ 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').
Running the container
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 wanna work with Python 3.6. Lo, behold the Dockerfile for Python 3.6:
FROM alpine:latest
LABEL 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 libraries
ENV 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 English
ENV PYTHON_PACKAGES="\
numpy \
matplotlib \
scipy \
scikit-learn \
pandas \
nltk \
"
RUN apk add --no-cache --virtual build-dependencies python3 \
&& apk add --virtual build-runtime \
build-base python3-dev openblas-dev freetype-dev pkgconfig gfortran \
&& ln -s /usr/include/locale.h /usr/include/xlocale.h \
&& python3 -m ensurepip \
&& rm -r /usr/lib/python*/ensurepip \
&& pip3 install --upgrade pip setuptools \
&& ln -sf /usr/bin/python3 /usr/bin/python \
&& ln -sf pip3 /usr/bin/pip \
&& rm -r /root/.cache \
&& 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 ["python3"]
Build and tag the image like so:
$ docker build -t faizanbashir/python-datascience:3.6 -f Dockerfile .
Run the container like so:
$ docker container run --rm -it faizanbashir/python-datascience:3.6 python
With this, you have a ready to use container for doing all kinds of cool data science stuff.
Serving Puddin'
Figures, you have the time and resources to set up all this stuff. In case 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 and extend in your Dockerfile file or use as image in your docker-compose or stack file.
Aftermath
The world of AI, ML is getting pretty exciting these days and will continue to become even more exciting. Big players are investing heavily in these domains. About time you start harness the power of data, who knows it might lead to something wonderful.
faizanbashir
/
python-datascience
Docker image for python datascience container with NumPy, SciPy, Scikit-learn, Matplotlib, nltk, pandas packages installed.
#Docker image for Python Datascience containers
Top comments (13)
Comprehensive explanation!! Thank you for the accuracy. Do the user have to have all the packages and programs or can I take advantage of some alternatives?
Some python cmd might be appropriate in such cases??
Very informative & useful.
Keep up the good work guys!!!
Get the best deals of Leather Double Beds
in affordable prices from Home Done UK.
Well, the information is correct. I found the post to be useful, however, I am writing for the people who are facing an issue with their Samsung printer. Click this reference link samsung printer error code u1-2320 and get the idea about how to resolve it in just one click.
Hey, your article is well written and easy to understand. Most gaming users tried most popular game league of legends. If you want to set up a wallpaper of this game, you can download free from this site.
Thank you so much for briefing this topic. For any query related to Timeout Error in Belkin Router you can visit routercustomercare.co/blog/how-to-... and get instant tips.
Thanks for the information.
However, the same issue is occuring while trying to use the Xbox controller.
Check this article here.
wonderful site all site are very good working i have check all site and mostly site are very good working please keep it up. Canon Printer Error Code 006
Great guide. Is it necessary to have all the packages or I can utilize some alternative?
Some python cmd command would be helpful?
Thanks that will help.
To remove virus using cmd check this guide.
Thanks for sharing it is important for me. I also searched for that from here. Contact Facebook Belgie
Some comments may only be visible to logged-in visitors. Sign in to view all comments.