COSC 410LA F25 Lab 0: Python Environment

This lab is due Tuesday, September 2, 2025 at 11:59PM.

Note: Unlike other labs, this should be done individually to ensure your setup is working properly.

Introduction

The following guides you through installing python packages in an environment and the basic of running jupyter notebooks.

Provided Files

• Lab0.ipynb
• environment.yml: A yaml file with the packages for the course

Notebooks Overview

Programming in lab will make use of Python notebooks (also called Jupyter notebooks). Python notebooks are a cell-based Python environment, meaning that your code is divided into cells that can be run in any order. The notebooks are backed by a Python kernel that maintains state between cell executions. This means after executing a cell, the variables and other similar things remain active throughout the duration of your interaction with the notebook. Python notebooks are a very common programming environment for machine learning and data science. The cell-based execution and in-place plotting makes data exploration particularly convenient.

Local Environment

Install

You will need Python 3 and a bunch of Python libraries. In particular, download and install Miniforge for your OS.

Create the ml Environment

Next, make sure you’re in the lab00 directory using Terminal (or Miniforge Prompt if you are on Windows) and run the following command. It will create a new conda environment containing libraries you will need for this class:

$ conda env create -f environment.yml

Next, activate the new environment:

$ conda activate ml

Start Jupyter

You’re almost there! You just need to register the ml conda environment to Jupyter. From the Terminal (or Miniforge Prompt):

$ python3 -m ipykernel install --user --name=ml

If you do not do this, you must select the kernel in the “Kernel > Change kernel…” menu in Jupyter every time you open a notebook, so that the correct packages are loaded.

That’s it! You can now start Jupyter from Terminal (or Miniforge Prompt) using:

$ jupyter lab

This should open up your browser to Jupyter lab with the contents of the current directory. If your browser does not open automatically, visit localhost:8888.

You should be able to open Lab0.ipynb from within your browser Jupyter window. Please read it and run the code cells to ensure everything is working.

Every time you want to work on materials for this class, you will need to open a Terminal, and run:

$ cd PATH # Navigate to your project folder 
$ conda activate ml 
$ jupyter lab

VS Code

There exists some way to integrate this with VS Code. I don’t know how to do this. Please ask your classmates on discord if you are taking this route.

Submission

Upload the following files to Gradescope (with the output of your code cells saved):

• Lab0.ipynb with the code block at the end run

Acknowledgments

The instructions for setting up a local environment and accompanying environment specification (environment.yml) are adapted from the github repo (link) for Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition).