Bite the bullet and preinstall it so that plotting libs that default
to using desktop rendering just work (matplotlib, ggplot, ...)
out of the box without having to get configuration right beforehand
(e.g., %matplotlib inline ahead of matplotlib import)
Only adds ~100k to the image size
(c) Copyright IBM Corp. 2015
* Pass $@ args to start-notebook.sh
* Set tini as entrypoint, but keep start-notebook.sh as easily overridable CMD
* su to jovyan user within start-notebook.sh script
Contribution (c) Copyright IBM Corp. 2015
* Create user jovyan with UID=1000 in the default users group in the Dockerfile
* Set group ownership of user home and conda to root to avoid 'users' group from host access when mounted
* Set stick bit on both paths so root owns subdirs too
* Change jovyan UID if NB_UID is specified and is not the default 1000
Contribution (c) Copyright IBM Corp. 2015
* Only create jovyan and set perms if user does not exist
* Allow docker host mount for NB_WORK: copy skel files if useradd fails because home dir already exists
Contribution (c) Copyright IBM Corp. 2015
* Always remain as root during install
* Put kernel specs in system path, not user home
* Create user work directory at startup
* Note this is in 4.0 and up images, not 3.2
Contribution (c) Copyright IBM Corp. 2015
* Change minimal-notebook to install notebook=4.0*
* Change other Dockerfile to point to 4.0 Docker Hub tag (to be built)
* Change config and pem file paths for jupyter
* Install ipywidgets in all containers that have a Python stack
* Update all READMEs to describe v3.2 and v4.0 since Docker Hub only shows one README for all tags
Contribution (c) Copyright IBM Corp. 2015
