installation.md

MiniTorch Module 2 Installation

MiniTorch requires Python 3.8 or higher. To check your version of Python, run:

>>> python --version

We recommend creating a global MiniTorch workspace directory that you will use for all modules:

>>> mkdir workspace; cd workspace

Environment Setup

We highly recommend setting up a virtual environment. The virtual environment lets you install packages that are only used for your assignments and do not impact the rest of the system.

Option 1: Anaconda (Recommended)

>>> conda create --name minitorch python    # Run only once
>>> conda activate minitorch
>>> conda install llvmlite                  # For optimization

Option 2: Venv

>>> python -m venv venv          # Run only once
>>> source venv/bin/activate

The first line should be run only once, whereas the second needs to be run whenever you open a new terminal to get started for the class. You can tell if it works by checking if your terminal starts with (minitorch) or (venv).

Getting the Code

Each assignment is distributed through a Git repo. Once you accept the assignment from GitHub Classroom, a personal repository under Cornell-Tech-ML will be created for you. You can then clone this repository to start working on your assignment.

>>> git clone {{ASSIGNMENT}}
>>> cd {{ASSIGNMENT}}

Syncing Previous Module Files

Module 2 requires files from Module 0 and Module 1. Sync them using:

>>> python sync_previous_module.py <path-to-module-1> <path-to-current-module>

Example:

>>> python sync_previous_module.py ../Module-1 .

Replace <path-to-module-1> with the path to your Module 1 directory and <path-to-current-module> with . for the current directory.

This will copy the following required files:

• minitorch/operators.py
• minitorch/module.py
• minitorch/autodiff.py
• minitorch/scalar.py
• tests/test_module.py
• tests/test_operators.py
• tests/test_autodiff.py
• tests/test_scalar.py
• project/run_manual.py
• project/run_scalar.py

Installation

Install all packages in your virtual environment:

>>> python -m pip install -e ".[dev,extra]"

Verification

Make sure everything is installed by running:

>>> python -c "import minitorch; print('Success!')"

Verify that the tensor functionality is available:

>>> python -c "from minitorch import tensor; print('Module 2 ready!')"

You're ready to start Module 2!