MiniTorch

• Docs: https://minitorch.github.io/

• Overview: https://minitorch.github.io/module4.html

MiniTorch is a diy teaching library for machine learning engineers who wish to learn about the internal concepts underlying deep learning systems. It is a pure Python re-implementation of the Torch API designed to be simple, easy-to-read, tested, and incremental. The final library can run Torch code.

The main repo for the course is available on (GitHub)[https://github.com/minitorch/minitorch}.

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

The files that will be synced are:

    minitorch/tensor_data.py minitorch/tensor_functions.py minitorch/tensor_ops.py minitorch/fast_ops.py minitorch/cuda_ops.py minitorch/operators.py minitorch/module.py minitorch/autodiff.py minitorch/module.py project/run_manual.py project/run_scalar.py project/run_tensor.py project/run_fast_tensor.py project/parallel_check.py tests/test_tensor_general.py