Data Science Docker Image

This image is created from the official Ubuntu 14.04 Docker image and contains popular Python packages for data science.

Introduction

This repository defines the "civisanalytics/datascience-python" Docker image. This Docker image provides an environment with data science tools from the Python ecosystem. This image is the execution environment for Python jobs in the Civis data science platform, and it includes the Civis Python API client.

Installation

Either build the Docker image locally

docker build -t datascience-python .

or download the image from DockerHub

docker pull civisanalytics/datascience-python:latest

The latest tag (Docker's default if you don't specify a tag) will give you the most recently-built version of the datascience-python image. You can replace the tag latest with a version number such as 1.0 to retrieve a reproducible environment.

Usage

Inside the datascience-python Docker image, Python packages are installed in the root environment. For a full list of included Python libraries, see the environment.yml file.

To start a Docker container from the datascience-python image and interact with it from a bash prompt, use

docker run -i -t civisanalytics/datascience-python:latest /bin/bash

You can run a Python command with

docker run civisanalytics/datascience-python:latest python -c "import pandas; print(pandas.__version__)"

The image contains environment variables which allow you to find the current version. There are four environment variables defined:

VERSION
VERSION_MAJOR
VERSION_MINOR
VERSION_MICRO

VERSION contains the full version string, e.g. "1.0.3". VERSION_MAJOR, VERSION_MINOR, and VERSION_MICRO each contain a single integer.

Creating Equivalent Local Environments

The environment.yml file in this repo can be used to create a python environment that is equivalent to the one in the container. This environment will be named datascience. The environment installs in Ubuntu Linux (this is the OS of the Dockerfile). It will install in OS X, but the xgboost install requires either the gcc v5 or the clang-omp compiler, neither of which are natively provided in OS X. If you wish to set up this environment in OS X, you may either

• Remove xgboost from the environment.yml file before using it to create the environment
• Use Homebrew to install gcc-5. You can do that via brew install gcc@5 --without-multilib. Be warned that this installation will take a long time.

Contributing

See CONTRIBUTING for information about contributing to this project.

If you make any changes, be sure to build a container to verify that it successfully completes:

docker build -t datascience-python:test .

and describe any changes in the change log.

For Maintainers

This repo has autobuild enabled. Any PR that is merged to master will be built as the latest tag on Dockerhub. Once you are ready to create a new version, go to the "releases" tab of the repository and click "Draft a new release". Github will prompt you to create a new tag, release title, and release description. The tag should use semantic versioning in the form "vX.X.X"; "major.minor.micro". The title of the release should be the same as the tag. Include a change log in the release description. Once the release is tagged, DockerHub will automatically build three identical containers, with labels "major", "major.minor", and "major.minor.micro".

License

BSD-3

See LICENSE.md for details.