MiniTorch

from minitorch page

• Docs: https://minitorch.github.io/

This project is an re-implemented version of PyTorch at the Machine Learning Engineering Course project minitorch.

Instructed by Professor Sasha Rush at Cornell Tech.

Descriptions

• Constructed a deep learning system using Python, including auto-differentiation, backpropagation, and tensor matrix operations.

• Implemented parallel computing with Numba and CUDA.

• Visualized with Streamlit and tested functions using pytest and Flake8.

Overview

This module requires fast_ops.py, cuda_ops.py, scalar.py, tensor_functions.py, tensor_data.py, tensor_ops.py, operators.py, module.py, and autodiff.py from Module 3.

Additionally you will need to install and download the MNist library.

(On Mac, this may require installing the wget command)

pip install python-mnist
mnist_get_data.sh

• Tests:

python run_tests.py

This assignment requires the following files from the previous assignments. You can get these by running

python sync_previous_module.py previous-module-dir current-module-dir