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MiniTorch Module 3 Installation

MiniTorch requires Python 3.11. To check your version of Python, run:

>>> python --version

If you don't have Python 3.11, install it before proceeding:

  • Mac: brew install python@3.11
  • Ubuntu/Debian: sudo apt install python3.11
  • Windows: Download from python.org

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=3.11    # Run only once
>>> conda activate minitorch
>>> conda install llvmlite                       # For optimization

Option 2: Venv

>>> python3.11 -m venv venv      # Run only once (requires Python 3.11)
>>> 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}}

Installation

Install all packages in your virtual environment:

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

Syncing Previous Module Files

Module 3 requires files from Module 0, Module 1, and Module 2. Sync them using:

>>> python sync_previous_module.py <path-to-module-2> <path-to-current-module>

Example:

>>> python sync_previous_module.py ../Module-2 .

Replace <path-to-module-2> with the path to your Module 2 directory and <path-to-current-module> with . for the current directory.

This will copy the following required files:

  • minitorch/tensor_data.py
  • minitorch/tensor_functions.py
  • minitorch/tensor_ops.py
  • minitorch/operators.py
  • minitorch/scalar.py
  • minitorch/scalar_functions.py
  • minitorch/module.py
  • minitorch/autodiff.py
  • minitorch/tensor.py
  • minitorch/datasets.py
  • minitorch/testing.py
  • minitorch/optim.py
  • project/run_manual.py
  • project/run_scalar.py
  • project/run_tensor.py

Installation

Install all packages in your virtual environment:

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

GPU Setup (Required for Tasks 3.3 and 3.4)

Tasks 3.3 and 3.4 require GPU support. Use Google Colab for GPU access (Sign up for student version).

Follow this Google Colab link, save the file to your drive, select T4 GPU runtime, and follow the instructions in the notebook.