peerherholz/workshop_newark repository overview

⁠Workshop Newark - 2019

This repository contains everything for the workshop in Newark 2019. There are three ways that you can interact with its content:

⁠1. Docker (recommended)

If you want to have the full experience, use the docker container peerherholz/workshop_newark. It provides the computational environment to run the notebooks on any system with all necessary dependencies installed. To install Docker⁠ on your system, follow one of those links:

• Ubuntu⁠ or Debian⁠
• Windows 7/8/9/10⁠ or Windows 10Pro⁠
• OS X (from El Capitan 10.11 on)⁠ or OS X (before El Capitan 10.11)⁠.

Once Docker is installed, open up the docker terminal and test if it works with the command: docker run hello-world

Note: Linux users might need to use sudo to run docker commands or follow the post-installation steps⁠.

Once docker is running on your system you can continue with downloading the docker image for this workshop. For this use the command:

docker pull peerherholz/workshop_newark

Once the download finished, proceed with the following steps:

1. Run the following command in a terminal: docker run -it --rm -p 8888:8888 peerherholz/workshop_newark
2. Copy paste the link that looks like http://20f109eba8e4:8888/?token=0312c1ef3b61d7a44ff5346d3d150c23249a548850e13868 into your webbrowser.
3. Replace the hash number 20f109eba8e4 after http:// with localhost or your local IP (probably 192.168.99.100) if you're on windows.
4. Once Jupyter Notebook is open, click on the program.ipynb notebook, and you're good to go.

And if you want to have access to the output data created within the docker container, add the command -v /path/to/your/output_folder:/output before peerherholz/workshop_Newark, where /path/to/your/output_folder should be a free folder on your system, such as /User/neuro/Desktop/output.

⁠Some useful Docker Commands

Show running containers
$ docker ps

Show all installed Docker images
$ docker images

Show all (also stopped) containers
$ docker ps -a

Remove a container
$ docker rm $CONTAINER_ID

Remove a docker image
$ docker rmi -f $IMAGE_ID

Start a stopped container & attach to it
$ docker start -ia $CONTAINER_ID

Note: when you stop a container (Ctrl-C), and then re-execute above "docker run" command, you will end up with a second container. If you want to access your previous container (e.g. with downloaded data), you must reconnect to it (see "docker start -ia" command below).

⁠2. Conda (if you want to install everything on your system yourself)

If you don't care about some of the software dependencies, or have them already installed on your system, you can use conda to create the necessary python environment to run the notebooks:

1. If you haven't yet, get conda on your system: https://conda.io/miniconda.html⁠
2. Download the environment.yml file from [here].(https://github.com/peerherholz/workshop_Newark/blob/master/environment.yml⁠)
3. Open up a conda terminal (or any other terminal), and create a new conda environment with the following command: conda env create --name workshop --file /path/to/file/environment.yml
4. Download the notebooks in this repository (here⁠), save them in the desired location, i.e. (Desktop/workshop).
5. Download the three datasets adhd⁠, ds000114⁠ and ds000228⁠ and put them into the workshop folder, i.e. (Desktop/workshop).
6. Open up a (docker) terminal, activate the conda environment with source activate workshop (for mac and linux) or with activate workshop (for windows), go into the folder where you saved the just downloaded notebooks (i.e. Desktop/workshop) and run the following command from the folder that contains the program.ipynb notebook: jupyter notebook

Note: This only provides you the notebooks from the workshop that are not already in the nipype_tutorial. Those notebooks you can download here: https://github.com/peerherholz/workshop_Newark⁠

⁠3. Jupyter NBViewer

All the notebooks (but not the slides) can be looked at via Jupyter nbviewer⁠. Like this you can see everything but cannot really interact with the scripts or run the code.

⁠Support

This work was supported in part by funding provided by Brain Canada⁠, in partnership with Health Canada⁠, for the Canadian Open Neuroscience Platform initiative⁠.