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If you know what you're doing in terms of python, there's a requirements.txt that you can use as you would like. Alternatively if you like Docker, there's install instructions for it in the wiki. If that's the case the only thing you really need to do is make sure you can run the Verify great success.ipynb notebook in this repo and you are good.

How to use this repo

This repository is the one that you will use to make sure you can execute all of the critical tasks required to make the most of your learning experience. In particular, after following this readme, you should:

  1. Have a GitHub acount
  2. Be able to clone a git repository
  3. Be able to start a jupyter notebook with python 3
  4. Run code within a jupyter notebook to (among other things) render some plots.


Please note that these are conservative estimates. If you are not 100% on any of these (except for the last), you should still be okay.

  • ~1GB of free memory on your computer
  • Some hundreds of megabytes free on your computer
  • 64-bit processor
  • The ability to read a README and follow the instructions in it


GitHub account

Sign up for a GitHub account if you don't already have one. Really, it's quite useful.

Step 1 - Install dependencies

Follow each of the links to download and install.

  • git
    • If you're on Windows or OS X and don't know what git is, GitHub Desktop is probably easiest.
  • anaconda
    • If you are on windows:
      • Make sure that there are no non-english characters in your username or on the path and that you have qt installed. Here is a reference on how to address these issues.

Step 2 - Clone a repo

In the future, you will clone other repositories so take note of these steps, you will indeed use them again. If you don't already know where you will want to clone the repo to on your machine, create a directory in your user's home directory called "repos" and clone everything to that.

Using GitHub Desktop

From the green "Clone or Download" button on the repo page, select "clone using github desktop" as shown below:


From the command line the easy way

git clone

From the command line the ssh way

Make sure that you have your ssh keys set up. Once they are you use almost the same clone command as above in "the easy way"

git clone

Take a note of the filepath on which you cloned the repo!

Note that if you choose this option, you will need to have your ssh keys set up.

Install graphviz and bokeh

If you are using the docker option, these steps aren't necessary.

From the Anaconda Navigator interface do the following:

  1. Select the root environment
  2. On the right hand panel, which to Not installed on the leftmost dropdown
  3. On the right hand search bar, search for
    1. bokeh - then install it
    2. graphviz - then install it
    3. python-graphviz - and install it

Once this is done, you are all good to go with bokeh but not graphviz! In order to get some binaries that graphviz needs on the system path you will probably need to install it globally.

  • On Debian-flavored linux you can use apt-get install graphviz.
  • On OSX, you can use brew install graphgiz
  • On windows you can download it from here
    • Please note that I have not tested this myself so we may need to do a bit of troubleshooting!

Step 3 - Start your environment

Open up anaconda and launch the jupyter notebook

launch jupyter notebook

Step 4

Make sure that jupyter notebook is running

In the freshly opened notebook, navigate to the root of this repository and open and run the Verify great success.ipynb notebook (you run cells by clicking on them and then pressing shift+enter). If you get a couple of pretty graphs, you are all good to go!

Final notes

Before the day

On the day of the workshop, we will be asking you to clone another repository. With the new repo, you will do everything the same as in step 4 but nagivate to a different repository.

On git usage

  • If you are trying to sync a repo and you get a conflicts error, navigate to the directory and execute git stash before you sync again.


Get your machine all ready for the Model Visualization workshop




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