General Resources

• LaTeX Quick Start Guide: here (originally prepared for COMP 170)
• Website that looks up LaTeX symbols based on drawn input

Python Environment

To standardize coding requirements for COMP 135, we have prepared a Python (3.6 or higher) environment that can run on any standard OS (Mac, Windows, Linux). Python code submitted for the class should be able to run in that environment, which has been verified to work for the standard Tufts EECS Unix account home directory.

Early in the course, TA's will offer some support for installing Python on your own computer, but it is up to you to maintain that environment. We recommend doing an install ASAP, to ensure that you have it up and running well before any deadlines. No extensions will be given due to installation issues.

1. Install Miniconda: A relatively lightweight version of the Anaconda package manager can be used to install the rest of the environment for COMP 135. This can be installed according to the instructions found at: the conda site.

Hint for Linux/Mac Users: if asked whether or not to add conda to your .bashrc (or similar) file, say yes.

2. Install the COMP 135 Environment: Once conda is installed, and can be called from the console/terminal, it can be used to install the standard Python environment and tools. Download the specification file we have provided (it should be saved to your computer as ml135_env.yml). Once downloaded, you can execute the following command from the directory containing that file, in order to install all of the libraries required (this may take a few minutes, and takes up about 1.5 GB of space):

conda env create -f ml135_env.yml

3. Using the Environment: Once the environment has been created, you no longer need the downloaded YML file. In order to use the environment when coding for the class, you can execute the following command from the console/terminal (you will do this each time you want to code in the standard environment):

conda activate ml135_env

Once the environment is active, it will ensure that launching Python or related tools gives you access to the same versions as will be used in testing your submitted code. For instance, to launch a Jupyter Notebook, once you have activated the environment via the prior command, you would use the following console/terminal command:

jupyter notebook

You can shut down the environment when done coding with the following console/terminal command:

conda deactivate