setup.md

Programming environment

Python

We will be using Python for this course because it is open source and widely used in machine learning and data science. We will use Python 3 (in particular 3.7 or 3.8). Python 2 is not supported in this course.

We recommend the Anaconda Python distribution because it comes bundled with a bunch of useful packages (NumPy, SciPy, scikit-learn, Jupyter notebook) pre-installed. You can download Anaconda from their website for free.

For resources on learning Python, see the resources page.

Virtual environment

What and Why

A virtual environment is a Python environment such that the Python interpreter, libraries and scripts installed into it are isolated from those installed in other virtual environments, and (by default) any libraries installed in a “system” Python, i.e., one which is installed as part of your operating system. (https://docs.python.org/3/library/venv.html) For example, you may want a certain version of tensorflow for one project but another version for a different project. Virtual environments helps us to build environment isolation between different projects and make sure any change to dependencies affects only the projects that need it.

Setting up a virtual environment

• Option 1: Conda environments (preferred)

  1. Make sure that conda is installed by running

     conda env list
     

     You should see a list of environments as the output. If Anaconda/Miniconda is not installed, you can download Anaconda from here or Miniconda (a small, bootstrap version of Anaconda) from here
  2. Download cpsc330env.yml and put it in your working directory
  3. Create an environment by

     conda env create -f cpsc330env.yml
     

     which allows conda to download the dependencies needed for this course and put them in a virtual environment named cpsc330env. You can check that the environemnt is installed successfully by running conda env list again. cpsc330env should show up in the output.
  4. Activate the environment with

     conda activate cpsc330env
     

     After a successful activation, something like (cpsc330env) should show up in the terminal.
  5. We are all set! You can now run homework and lecture materials within the virtual environment.

     jupyter lab
     

  6. To deactivate the environment, run

     conda deactivate
     

  For more information on conda environments, see here.

• Option 2: Virtualenv

  1. You can choose this option if your have python version 3.7.x or 3.8.x installed on your system. To find out, run

     python3 --version
     

  2. Install Virtualenv with

     python3 -m pip install --user virtualenv
     

  3. Navigate to your working directory and create new virtual environment with

     virtualenv -p python3 cpsc330env
     

     This will create a folder in your current directory that stores all the packages for this virtual environment.
  4. Activate the environment OS X/Linux:

     source cpsc330env/bin/activate
     

     Windows:

     cpsc330env\Scripts\activate
     

     If you happen to use csh or fish shell, source the corresponding activate file. After a successful activation, something like (cpsc330env) should show up in the terminal.
  5. Download requirements.txt and put it in your working directory. Then install the dependencies listed with

     pip install -r requirements.txt
     

  6. A few more dependencies that we'll need to install manually
     • Install nodejs from here
     • Install graphviz from here
  7. We are all set! You can now run homework and lecture materials within the virtual environment.

     jupyter lab
     

  8. To deactivate the virtual environment, run

     deactivate
     

  For more information on virtualenv, see here.

Additional steps to enable canvas utitlity

Activate the virtual environment, then run the following commands

jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension update --all 
jupyter lab build

Git

Please also see the git setup instructions as you will need git as well for the course.