https://github.com/dataquestio/ds-containers
Containers for data science
https://github.com/dataquestio/ds-containers
Last synced: 7 months ago
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Containers for data science
- Host: GitHub
- URL: https://github.com/dataquestio/ds-containers
- Owner: dataquestio
- Created: 2015-11-20T21:59:07.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2017-07-25T22:20:35.000Z (almost 9 years ago)
- Last Synced: 2025-08-15T22:07:36.634Z (8 months ago)
- Language: Shell
- Size: 4.88 KB
- Stars: 78
- Watchers: 21
- Forks: 47
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
An easy way to get started with data science, using Docker. Containers that come preinstalled with ipython notebook and key packages in the scientific stack.
## Getting started
* Run `sh build_container.sh python2` (switch `python2` for another container to build that one`).
* You can also pull the container by running `docker pull dataquestio/python2-starter`. Replace `python2` with any other container you want.
* Run `docker run -d -p 8888:8888 -v ORIGIN_FOLDER:/home/ds/notebooks dataquestio/python2-starter`
* Replace `python2-starter` with the container you want.
* Replace `ORIGIN_FOLDER` with a folder on your local machine that you want to persist notebooks in.
* Open your browser and start working with IPython notebook.
* On Linux, the url will be `localhost:8888`.
* On Windows/OSX, run `docker-machine ip default` (replace `default` with the name of your machine). Then, you'll be able to access IPython notebook at `CONTAINER_IP:8888`.
## Contributions
Contributions are welcome -- please submit a PR if you want to modify the Dockerfiles, or add to the requirements.