The boilerplate and utilities for Data Science in Python3.
- Python3
- Hand-picked, de facto standard Python libraries
- Use IPython Notebook
- Time-proven practices in Initium Lab
- Boilerplates, sample codes, cheat sheets, quick hacks, ...
- [TODO] Integration with production-ready Javascript libraries
- [TODO] Workflow for Continuous Deployment of mining results
docker-compose up
.- Visit port 8888 on
docker-machine ip XXXX
to operate the notebook. - Be default, this current dir is mapped into the container as working folder. You can pass code/data in directly.
Choose either way:
docker pull hupili/urchin
docker build -t hupili/urchin .
docker run -v urchin:/app -p 8888:8888 -it hupili/urchin ipython notebook --ip 0.0.0.0
You can find the volumen on your docker-machine in following folder:
/var/lib/docker/volumes/urchin/_data
Initial setup:
- Fork and Clone this repository
- Instal Python3
virtualenv -p python3 venv
orpyvenv venv
source venv/bin/activate
pip install -r requirements.txt
Following usage:
source venv/bin/activate
(save time if you use virtualenvwrapper..venv
is configured)- Use
ipython notebook
to launch the environment - Copy any interesting stuff from
boilerplates
to the root and hack away