xiaodaidocker2019/julia-data-science-base repository overview

⁠Intro

Julia Data Science Docker with data science packages compiled for instant loading!

Time-to-first-plot (TTFP) is often regarded as one of Julia's main pain points. There are packages to alleviate the pain such as PackageCompiler.jl and more recently PacakgeCompilerX.jl. These packages work by "compiling" the packages so that using Pkgs will be fast.

This is an experimental first attempt at making data science packages used by me into a docker image with compiled packages.

⁠Usage

Firstly, install Docker, then run the below command to run a Julia REPL from docker. If you are running Windows, I recommend installing git so you have access to git bash.

On Windows you IP can be found using ipconfig and on Linux with ifconfig. This is need if you wish to do plotting from the docker image

Basic

docker run --rm \
	-e DISPLAY=YOUR_IP:0.0 \
	-e JUPYTER_ENABLE_LAB=yes \
	-v "$PWD":/home/jovyan/work\
	-it -p 8888:8888 \
	xiaodaidocker2019/julia-data-science-base

Attach storage Often one may wish to save the data to somewhere on the hard drive, you may do this by attaching a local folder to the directory somedir

docker run --rm \
	-e DISPLAY=YOUR_IP:0.0 \
	-e JUPYTER_ENABLE_LAB=yes \
	-v "$PWD":/home/jovyan/work\
	-it -p 8888:8888 \
	-v /path/to/local/dir::/home/jovyan/somedir \
	xiaodaidocker2019/julia-data-science-base

⁠Packages

The below packages are compiled using PackageCompilerX.jl into the image

The below packages are included but not compiled

⁠Known packages not working

• XGBoost.jl