Text Analysis in Python: Setup

Clone the git project

There are a number of files we need to conduct the workshop. By cloning the git repository, you will be able to have a copy of all of the necessary files and functions required to run through the exercises.

1. Click the link below to open the Github page.
2. Click the “Download” button.
3. Unzip the directory to your desktop or working directory.

Choose Colab or Local Install

You can either run this workshop on your local machine or Google Colab. Colab was chosen to ensure all learners have similar processing power (using Google’s servers), and to streamline the setup required for the workshop. If you prefer to setup a local environment and download all necessary packages, please review the next section. However, unless your local machine has at least 12 GB of RAM AND a graphics card with CUDA enabled, your code may run slower than the rest of the learners.

Google Colab Setup

We will be using Google Colab to run Python code in our browsers.

1. Visit the Google Colab website and click “New notebook” from the pop-up that shows up
2. Visit Google Drive and find a newly created “Colab Notebooks” folder stored under MyDrive, /My Drive/Colab Notebooks
3. Create a folder named text-analysis in the Colab Notebooks folder on Google Drive. The path should look something like this: /My Drive/Colab Notebooks/text-analysis/.
4. Upload contents of the text-analysis-setup.zip file to Google Drive.
5. Note for instructors: At the start of each section, create a new Colab file within the text-analysis folder by navigating to /My Drive/Colab Notebooks/text-analysis/ within Google Drive, followed by clicking New -> More -> Google Colaboratory

Anaconda Local Setup (Alternative to Colab)

Software dependencies for data science can be complicated and easily conflict with each other, therefore we will be using a self-contained ‘virtual environment’ to manage their installation.

1. Install Anaconda. Anaconda is a software manager that will help manage dependencies and properly configure use of your graphics card (if you have one). Follow the installation instructions specified for your operating system.
2. Find the appropriate environment.yml file from the directory you unzipped the setup file. Go into the “conda-setup” subdirectory.
   1. CPU environment configures an environment for CPU only. If you are not sure what environment to use, use this one.
   2. CUDA 11.8 environment configures an environment for a CUDA 11.8 enabled graphics card. Other versions of CUDA are not supported.
3. Launch Anaconda Navigator. Select Environments -> Import. Select the environment file you downloaded for the import. For the name of the environment, type in text-analysis. WARNING: This environment will likely take at least 10 minutes to configure and may take up to an hour. Give the installer time to configure the environment.
4. Once installed, click the play button next to the text-analysis environment and select Open with Python to open a python terminal. You can close and relaunch this python terminal as needed.