Upgrade libraries

.github/classroom/autograding.json

@@ -0,0 +1,54 @@
+{
+  "tests": [
+    {
+      "name": "Style",
+      "setup": "python -m venv .venv; . .venv/bin/activate; pip3 install -qe .[dev]; pip3 install -q pre-commit",
+      "run": ". .venv/bin/activate && pre-commit run --all",
+      "input": "",
+      "output": "",
+      "comparison": "included",
+      "timeout": 10,
+      "points": 10
+    },
+    {
+      "name": "Task 2.1",
+      "setup": "sudo -H pip3 install -qe .",
+      "run": "pytest -m task2_1",
+      "input": "",
+      "output": "",
+      "comparison": "included",
+      "timeout": 10,
+      "points": 10
+    },
+    {
+      "name": "Task 2.2",
+      "setup": "sudo -H pip3 install -qe .",
+      "run": "pytest -m task2_2",
+      "input": "",
+      "output": "",
+      "comparison": "included",
+      "timeout": 10,
+      "points": 10
+    },
+    {
+      "name": "Task 2.3",
+      "setup": "sudo -H pip3 install -qe .",
+      "run": "pytest -m task2_3",
+      "input": "",
+      "output": "",
+      "comparison": "included",
+      "timeout": 10,
+      "points": 10
+    },
+    {
+      "name": "Task 2.4",
+      "setup": "sudo -H pip3 install -qe .",
+      "run": "pytest -m task2_4",
+      "input": "",
+      "output": "",
+      "comparison": "included",
+      "timeout": 10,
+      "points": 10
+    }
+  ]
+}

.github/workflows/classroom.yaml

@@ -0,0 +1,16 @@
+name: GitHub Classroom Workflow
+
+on: [push]
+
+permissions:
+  checks: write
+  actions: read
+  contents: read
+
+jobs:
+  build:
+    name: Autograding
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: education/autograding@v1

.gitignore

@@ -129,4 +129,5 @@ dmypy.json
 .pyre/
 *.\#*
 data/
-pyodide
+pyodideModule2.md
+module2-modernization.md

.pre-commit-config.yaml

@@ -15,7 +15,7 @@
 repos:
 # Standard hooks
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.3.0
+  rev: v5.0.0
   hooks:
   - id: check-added-large-files
   - id: check-case-conflict
@@ -25,38 +25,21 @@ repos:
   - id: check-toml
   - id: debug-statements
   - id: mixed-line-ending
-  - id: requirements-txt-fixer
   - id: trailing-whitespace
 
-- repo: https://github.com/timothycrosley/isort
-  rev: 5.10.1
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.12.9
   hooks:
-  - id: isort
+    # Run the linter.
+    - id: ruff-check
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format
 
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.971
+- repo: https://github.com/RobertCraigie/pyright-python
+  rev: v1.1.404
   hooks:
-  - id: mypy
-    exclude: ^(docs/)|(project/)|(assignments/)|(project/interface/)
-
-
-# Black, the code formatter, natively supports pre-commit
-- repo: https://github.com/psf/black
-  rev: 22.6.0
-  hooks:
-  - id: black
-
-# Flake8 also supports pre-commit natively (same author)
-- repo: https://github.com/PyCQA/flake8
-  rev: 5.0.4
-  hooks:
-  - id: flake8
-    additional_dependencies:
-    - pep8-naming
-    exclude: ^(docs/)|(assignments/)|(project/interface/)
-
-# Doc linters
-- repo: https://github.com/terrencepreilly/darglint
-  rev: v1.8.1
-  hooks:
-  - id: darglint
+    - id: pyright
+      additional_dependencies:
+        - pytest
+        - hypothesis

README.md

@@ -2,17 +2,66 @@
 
 <img src="https://minitorch.github.io/minitorch.svg" width="50%">
 
+**Tensors** - Extending Autodifferentiation to Multidimensional Arrays
 
 * Docs: https://minitorch.github.io/
-
 * Overview: https://minitorch.github.io/module2/module2/
 
-This assignment requires the following files from the previous assignments. You can get these by running
+## Overview
+
+Module 2 introduces **Tensors** - multidimensional arrays that extend the scalar autodifferentiation system from Module 1. While the scalar system is correct, it's inefficient due to Python overhead. Tensors solve this by grouping operations together and enabling faster implementations.
+
+## Installation
+
+See [installation.md](installation.md) for detailed setup instructions.
+
+## Quick Start
+
+```bash
+# Install dependencies
+pip install -e ".[dev,extra]"
+
+# Sync files from Module 1
+python sync_previous_module.py ../Module-1 .
+
+# Verify installation
+python -c "import minitorch; print('Success!')"
+
+# Run tests
+pytest -m task2_1  # Tensor data and indexing
+pytest -m task2_2  # Tensor broadcasting
+pytest -m task2_3  # Tensor operations
+pytest -m task2_4  # Tensor autodifferentiation
+
+# Train tensor-based model
+python project/run_tensor.py
+```
+
+## Tasks
+
+- **Task 2.1**: Implement tensor data structures with indexing and strides
+- **Task 2.2**: Implement tensor broadcasting for operations between different shapes
+- **Task 2.3**: Implement tensor operations (map, zip, reduce) and mathematical functions
+- **Task 2.4**: Extend autodifferentiation to work with tensors and broadcasting
+- **Task 2.5**: Create tensor-based neural network training
+
+## Testing
+
+See [testing.md](testing.md) for detailed testing instructions.
+
+## Files
+
+This assignment requires the following files from Module 1. You can get these by running:
 
 ```bash
-python sync_previous_module.py previous-module-dir current-module-dir
+python sync_previous_module.py ../Module-1 .
 ```
 
 The files that will be synced are:
 
-        minitorch/operators.py minitorch/module.py minitorch/autodiff.py minitorch/scalar.py minitorch/module.py project/run_manual.py project/run_scalar.py
+- `minitorch/operators.py`
+- `minitorch/module.py`
+- `minitorch/autodiff.py`
+- `minitorch/scalar.py`
+- `project/run_manual.py`
+- `project/run_scalar.py`

installation.md

@@ -0,0 +1,98 @@
+---
+hide:
+  - navigation
+---
+
+# MiniTorch Module 2 Installation
+
+MiniTorch requires Python 3.8 or higher. To check your version of Python, run:
+
+```bash
+>>> python --version
+```
+
+We recommend creating a global MiniTorch workspace directory that you will use
+for all modules:
+
+```bash
+>>> 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)**
+```bash
+>>> conda create --name minitorch python    # Run only once
+>>> conda activate minitorch
+>>> conda install llvmlite                  # For optimization
+```
+
+**Option 2: Venv**
+```bash
+>>> 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.
+
+```bash
+>>> git clone {{ASSIGNMENT}}
+>>> cd {{ASSIGNMENT}}
+```
+
+## Syncing Previous Module Files
+
+Module 2 requires files from Module 0 and Module 1. Sync them using:
+
+```bash
+>>> python sync_previous_module.py <path-to-module-1> <path-to-current-module>
+```
+
+Example:
+```bash
+>>> 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:
+
+```bash
+>>> python -m pip install -e ".[dev,extra]"
+```
+
+## Verification
+
+Make sure everything is installed by running:
+
+```bash
+>>> python -c "import minitorch; print('Success!')"
+```
+
+Verify that the tensor functionality is available:
+
+```bash
+>>> python -c "from minitorch import tensor; print('Module 2 ready!')"
+```
+
+You're ready to start Module 2!