dataworkshop/environment repository overview

⁠Data Workshop Environment

⁠What?

Basic environment for Data Workshop⁠

⁠Why?

Minimum effort & maximum impact :squirrel:

• Jupyter Notebook⁠ and Jupyter Lab⁠
• Anaconda⁠ with Python 3.6.0
• Additional packages seaborn⁠, ggplot⁠, hyperopt⁠, hyperas⁠, ml_metrics⁠, xgboost⁠

⁠Prerequisites

Only Docker⁠ (installation Instruction for Mac⁠ and Windows⁠) :bowtie:

⁠Build

docker run --net=host --dns 127.0.0.1 --dns 8.8.8.8 --dns 8.8.4.4 -dit -p 8888-8889:8888-8889 --name dataworkshop-environment dataworkshop/environment

⁠Use

• Notebook - localhost:8888⁠
• Lab - localhost:8889⁠

Note: if you're a happy Docker Toolbox user to find the ip address use docker-machine ls.

⁠Example

The URL column (docker-machine ls) contains tcp://192.168.99.100:2376, so you should copy 192.168.99.100 and add notebook port 192.168.99.100**:8888** or lab port 192.168.99.100**:8889**.

⁠Re-use (already built container)

docker start dataworkshop-environment

Note: that in docker terminology

• run means build (a new container)
• start means start (already exists) container

⁠Stop

docker stop dataworkshop-environment

⁠Update (image)

To get the last changes from dockerhub⁠

docker pull dataworkshop/environment

⁠Remove container/image

container

docker rm dataworkshop-environment

or image

docker rmi dataworkshop/environment

⁠Runtime metrics

docker stats dataworkshop-environment

⁠Show Running Processes

docker top dataworkshop-environment

⁠For geek :neckbeard:

Note: run it in Dockerfile directory

docker build -t dataworkshop-environment .

⁠Running container without jupyter server

Jupyter servers are started by running start_script.sh in CMD section. However, you can easily override it, by specifying a command at the end of docker run... (in this case servers won't start)