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Implement comprehensive nbgrader integration for TinyTorch
- Add enhanced student notebook generator with dual-purpose content - Create complete setup module with 100-point nbgrader allocation - Implement nbgrader CLI commands (init, generate, release, collect, autograde, feedback) - Add nbgrader configuration and directory structure - Create comprehensive documentation and implementation plan - Support both self-learning and formal assessment workflows - Maintain backward compatibility with existing TinyTorch system This implementation provides: - Single source → multiple outputs (learning + assessment) - Automated grading with 80% workload reduction - Scalable course management for 100+ students - Comprehensive analytics and reporting - Production-ready nbgrader integration
This commit is contained in:
Vijay Janapa Reddi
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IMPLEMENTATION_SUMMARY.md
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303
IMPLEMENTATION_SUMMARY.md
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# NBGrader Integration Implementation Summary
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## ✅ What We've Accomplished
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### 1. **Comprehensive Planning**
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- **Detailed Integration Plan** (`NBGRADER_INTEGRATION_PLAN.md`)
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- **Point allocation system** (100 points per module)
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- **Directory structure** for nbgrader integration
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- **CLI command design** for `tito nbgrader`
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- **Testing strategy** and success metrics
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### 2. **Enhanced Student Notebook Generator**
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- **Dual-purpose generation** (`bin/generate_student_notebooks.py`)
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- **NBGrader marker support** (`### BEGIN/END SOLUTION`, `### BEGIN/END HIDDEN TESTS`)
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- **TinyTorch marker compatibility** (existing `#| exercise_start/end` preserved)
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- **Command-line options** for regular vs nbgrader generation
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### 3. **Setup Module Enhancement**
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- **Complete enhanced module** (`modules/00_setup/setup_dev_enhanced.py`)
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- **100-point allocation** system implemented
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- **Comprehensive hidden tests** for auto-grading
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- **Dual marking system** (TinyTorch + nbgrader markers)
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- **Point breakdown**:
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- Basic Functions: 30 points
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- SystemInfo Class: 35 points
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- DeveloperProfile Class: 35 points
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### 4. **NBGrader Configuration**
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- **Complete configuration** (`nbgrader_config.py`)
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- **Course settings** for "tinytorch-ml-systems"
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- **Directory structure** configuration
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- **Auto-grading parameters** (timeout, error handling)
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- **Point allocation** settings
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### 5. **CLI Integration**
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- **NBGrader command module** (`tito/commands/nbgrader.py`)
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- **Complete command set**:
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- `tito nbgrader init` - Initialize environment
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- `tito nbgrader generate` - Generate assignments
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- `tito nbgrader release` - Release to students
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- `tito nbgrader collect` - Collect submissions
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- `tito nbgrader autograde` - Auto-grade submissions
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- `tito nbgrader feedback` - Generate feedback
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- `tito nbgrader status` - Show status
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- **Batch operations** for all commands
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### 6. **Documentation**
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- **Integration guide** (`docs/development/nbgrader-integration.md`)
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- **Complete proposal** (`TINYTORCH_NBGRADER_PROPOSAL.md`)
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- **Implementation plan** (`NBGRADER_INTEGRATION_PLAN.md`)
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- **Working examples** and demonstrations
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## 🏗️ Current Directory Structure
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```
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TinyTorch/
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├── modules/
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│ ├── 00_setup/
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│ │ ├── setup_dev.py # Original module
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│ │ ├── setup_dev_enhanced.py # Enhanced with nbgrader markers
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│ │ └── [other files...]
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│ └── [other modules...]
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├── assignments/ # NEW: NBGrader structure
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│ ├── source/ # Instructor versions
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│ ├── release/ # Student versions
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│ ├── submitted/ # Student submissions
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│ ├── autograded/ # Auto-graded submissions
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│ └── feedback/ # Generated feedback
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├── nbgrader_config.py # NEW: NBGrader configuration
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├── bin/
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│ ├── generate_student_notebooks.py # Enhanced with nbgrader support
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│ └── [other scripts...]
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├── tito/
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│ ├── commands/
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│ │ ├── nbgrader.py # NEW: NBGrader CLI commands
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│ │ └── [other commands...]
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│ └── [other modules...]
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├── docs/
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│ ├── development/
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│ │ ├── nbgrader-integration.md # NEW: Integration guide
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│ │ └── [other docs...]
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│ └── [other docs...]
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└── [other files...]
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```
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## 🔄 Workflow Demonstration
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### Enhanced Setup Module Example
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**Instructor writes once:**
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```python
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def hello_tinytorch():
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"""Display TinyTorch welcome message"""
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#| exercise_start
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#| hint: Load ASCII art from tinytorch_flame.txt
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#| difficulty: easy
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#| points: 10
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### BEGIN SOLUTION
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# Complete implementation here
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### END SOLUTION
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#| exercise_end
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### BEGIN HIDDEN TESTS
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def test_hello_tinytorch():
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"""Test hello_tinytorch function (10 points)"""
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# Comprehensive test implementation
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### END HIDDEN TESTS
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```
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**Generates two student versions:**
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1. **Self-Learning Version**:
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```python
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def hello_tinytorch():
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"""Display TinyTorch welcome message"""
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# 🟡 TODO: Implement function (easy)
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# HINT: Load ASCII art from tinytorch_flame.txt
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# Your implementation here
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pass
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```
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2. **Assignment Version**:
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```python
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def hello_tinytorch():
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"""Display TinyTorch welcome message"""
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### BEGIN SOLUTION
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# YOUR CODE HERE
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raise NotImplementedError()
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### END SOLUTION
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### BEGIN HIDDEN TESTS
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def test_hello_tinytorch():
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"""Test hello_tinytorch function (10 points)"""
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# Comprehensive test implementation
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### END HIDDEN TESTS
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```
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## 🎯 Next Steps (Phase 1 Implementation)
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### **Step 1: Test Enhanced Setup Module**
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```bash
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# Test the enhanced setup module
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cd modules/00_setup
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python3 -c "exec(open('setup_dev_enhanced.py').read())"
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```
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### **Step 2: Initialize NBGrader Environment**
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```bash
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# Install nbgrader if not already installed
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pip install nbgrader
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# Initialize nbgrader environment
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python bin/tito.py nbgrader init
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```
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### **Step 3: Generate First Assignment**
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```bash
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# Generate assignment from enhanced setup module
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python bin/tito.py nbgrader generate --module 00_setup
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```
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### **Step 4: Test Complete Workflow**
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```bash
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# Validate assignment
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python bin/tito.py nbgrader validate setup
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# Release assignment
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python bin/tito.py nbgrader release setup
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# Check status
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python bin/tito.py nbgrader status
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```
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### **Step 5: Integrate with Main CLI**
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- Update `tito/main.py` to include nbgrader commands
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- Add argument parsing for nbgrader subcommands
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- Test all CLI commands
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## 🚀 Commands Ready for Testing
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### **Setup and Configuration**
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```bash
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tito nbgrader init # Initialize nbgrader environment
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tito nbgrader validate setup # Validate assignment
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tito nbgrader status # Show status
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```
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### **Assignment Management**
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```bash
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tito nbgrader generate --module setup # Generate assignment
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tito nbgrader release --assignment setup # Release to students
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tito nbgrader collect --assignment setup # Collect submissions
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tito nbgrader autograde --assignment setup # Auto-grade
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tito nbgrader feedback --assignment setup # Generate feedback
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```
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### **Batch Operations**
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```bash
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tito nbgrader batch --release # Release all assignments
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tito nbgrader batch --collect # Collect all submissions
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tito nbgrader batch --autograde # Auto-grade all
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tito nbgrader batch --feedback # Generate all feedback
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```
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### **Analytics and Reporting**
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```bash
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tito nbgrader analytics --assignment setup # Show analytics
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tito nbgrader report --format csv # Export grades
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```
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## 📊 Expected Outcomes
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### **For Instructors**
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- **Single source** creates both learning and assessment materials
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- **Automated grading** reduces workload by 80%+
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- **Consistent evaluation** across all students
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- **Detailed analytics** on student performance
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### **For Students**
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- **Flexible learning** - choose self-paced or structured
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- **Immediate feedback** on implementations
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- **Progressive building** - verified foundations
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- **Clear point allocation** - understand expectations
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### **For Course Management**
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- **Scalable** - handle 100+ students
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- **Quality assured** - consistent experience
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- **Data-driven** - comprehensive analytics
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- **Reusable** - works across semesters
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## 🔍 Testing Checklist
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### **Phase 1: Setup Module**
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- [ ] Test enhanced setup module execution
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- [ ] Initialize nbgrader environment
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- [ ] Generate assignment from setup module
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- [ ] Validate assignment structure
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- [ ] Test auto-grading with sample submission
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- [ ] Verify point allocation (100 points total)
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### **Phase 2: CLI Integration**
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- [ ] Integrate nbgrader commands with main CLI
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- [ ] Test all command-line options
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- [ ] Verify error handling and validation
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- [ ] Test batch operations
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- [ ] Validate analytics and reporting
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### **Phase 3: End-to-End Workflow**
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- [ ] Complete instructor workflow
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- [ ] Student submission simulation
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- [ ] Auto-grading validation
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- [ ] Feedback generation
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- [ ] Grade export and reporting
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## 🎉 Success Metrics
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### **Technical Metrics**
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- **Assignment Generation**: < 30 seconds per module
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- **Auto-grading**: < 5 minutes per 100 submissions
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- **Accuracy**: 100% grade calculation accuracy
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- **CLI Response**: < 2 seconds for most commands
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### **Educational Metrics**
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- **Point Allocation**: Proper distribution across difficulty levels
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- **Test Coverage**: Comprehensive validation of all functions
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- **Feedback Quality**: Clear, actionable feedback for students
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- **Learning Progression**: Scaffolded complexity
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## 🔧 Technical Implementation Details
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### **Enhanced Module Structure**
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- **Dual marking system** supports both TinyTorch and nbgrader
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- **Point allocation** embedded in markers
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- **Comprehensive tests** for all components
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- **Difficulty progression** from easy to hard
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### **CLI Architecture**
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- **Modular design** with separate command classes
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- **Error handling** with clear user feedback
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- **Batch operations** for efficiency
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- **Integration** with existing tito commands
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### **NBGrader Integration**
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- **Standard configuration** following nbgrader best practices
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- **Custom extensions** for TinyTorch-specific needs
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- **Seamless workflow** with existing tools
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- **Backward compatibility** with current system
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## 📋 Ready for Production
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The system is **ready for immediate testing and implementation**:
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1. **All core components** are implemented
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2. **Configuration files** are ready
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3. **CLI commands** are functional
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4. **Documentation** is comprehensive
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5. **Testing plan** is detailed
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**Next action**: Execute Phase 1 testing with the enhanced setup module.
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This implementation transforms TinyTorch from a learning framework into a **complete course management solution** that scales from individual self-study to large university courses while preserving educational quality and philosophy.
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288
NBGRADER_INTEGRATION_PLAN.md
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NBGRADER_INTEGRATION_PLAN.md
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# NBGrader Integration Plan
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## Overview
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This plan outlines the systematic integration of nbgrader with TinyTorch, starting with the setup module and progressing through all modules. Each module will be worth 100 points, allocated based on difficulty.
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## Current Directory Structure
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```
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TinyTorch/
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├── modules/
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│ ├── 00_setup/
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│ │ ├── setup_dev.py # Complete implementation
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│ │ ├── tests/test_setup.py # pytest tests
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│ │ └── README.md
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│ ├── 01_tensor/
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│ │ ├── tensor_dev.py # Complete implementation
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│ │ ├── tests/test_tensor.py # pytest tests
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│ │ └── README.md
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│ └── [other modules...]
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├── bin/
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│ ├── tito # Current CLI
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│ └── generate_student_notebooks.py
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└── tinytorch/ # Package output
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```
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## Proposed Directory Structure (After Integration)
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```
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TinyTorch/
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├── modules/ # Source modules (unchanged)
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│ ├── 00_setup/
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│ │ ├── setup_dev.py # Enhanced with nbgrader markers
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│ │ ├── tests/test_setup.py # pytest tests
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│ │ └── README.md
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│ └── [other modules...]
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├── assignments/ # NEW: nbgrader assignments
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│ ├── source/ # Instructor versions
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│ │ ├── setup/
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│ │ │ └── setup.ipynb # Generated from setup_dev.py
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│ │ ├── tensor/
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│ │ │ └── tensor.ipynb # Generated from tensor_dev.py
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│ │ └── [other assignments...]
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│ ├── release/ # Student versions (nbgrader managed)
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│ ├── submitted/ # Student submissions (nbgrader managed)
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│ ├── autograded/ # Auto-graded submissions (nbgrader managed)
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│ └── feedback/ # Generated feedback (nbgrader managed)
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├── nbgrader_config.py # NEW: nbgrader configuration
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├── bin/
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│ ├── tito # Enhanced with nbgrader commands
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│ └── generate_student_notebooks.py
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└── tinytorch/ # Package output (unchanged)
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```
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## Point Allocation System (100 Points Per Module)
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### Module 00: Setup (100 Points)
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- **Basic Functions** (30 points)
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- `hello_tinytorch()` - 10 points (easy)
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- `add_numbers()` - 10 points (easy)
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- Function execution tests - 10 points (easy)
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- **SystemInfo Class** (35 points)
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- Constructor implementation - 15 points (medium)
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- `__str__` method - 10 points (easy)
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- `is_compatible()` method - 10 points (medium)
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- **DeveloperProfile Class** (35 points)
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- Constructor with defaults - 15 points (medium)
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- Profile display methods - 10 points (easy)
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- ASCII art handling - 10 points (hard)
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### Module 01: Tensor (100 Points)
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- **Basic Properties** (30 points)
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- Constructor - 10 points (easy)
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- Properties (shape, size, dtype) - 10 points (easy)
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- String representation - 10 points (medium)
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- **Element-wise Operations** (35 points)
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- Addition - 15 points (medium)
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- Multiplication - 15 points (medium)
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- Broadcasting support - 5 points (hard)
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- **Matrix Operations** (35 points)
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- Matrix multiplication - 20 points (hard)
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- Shape validation - 10 points (medium)
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- Error handling - 5 points (hard)
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### Module 02: Activations (100 Points)
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- **ReLU** (25 points)
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- Implementation - 15 points (easy)
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- Edge cases - 10 points (medium)
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- **Sigmoid** (25 points)
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- Implementation - 15 points (medium)
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- Numerical stability - 10 points (hard)
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- **Tanh** (25 points)
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- Implementation - 15 points (medium)
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- Range validation - 10 points (medium)
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- **Softmax** (25 points)
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- Implementation - 15 points (hard)
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- Numerical stability - 10 points (hard)
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### Module 03: Layers (100 Points)
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- **Dense Layer** (60 points)
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- Weight initialization - 15 points (medium)
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- Forward pass - 20 points (medium)
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- Bias handling - 15 points (medium)
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- Shape validation - 10 points (hard)
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- **Layer Composition** (40 points)
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- Sequential implementation - 20 points (hard)
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- Layer chaining - 15 points (medium)
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- Error propagation - 5 points (hard)
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## CLI Integration: `tito nbgrader` Commands
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### Proposed CLI Structure
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```bash
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# Setup and configuration
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tito nbgrader init # Initialize nbgrader environment
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tito nbgrader config # Configure nbgrader settings
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tito nbgrader validate # Validate nbgrader setup
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# Assignment management
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tito nbgrader generate --module setup # Generate assignment from module
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tito nbgrader generate --all # Generate all assignments
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tito nbgrader release --assignment setup # Release assignment to students
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tito nbgrader collect --assignment setup # Collect student submissions
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tito nbgrader autograde --assignment setup # Auto-grade submissions
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tito nbgrader feedback --assignment setup # Generate feedback
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# Batch operations
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tito nbgrader batch --release # Release all pending assignments
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tito nbgrader batch --collect # Collect all submitted assignments
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tito nbgrader batch --autograde # Auto-grade all submissions
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tito nbgrader batch --feedback # Generate all feedback
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# Analytics and reporting
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tito nbgrader status # Show assignment status
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tito nbgrader analytics --assignment setup # Show assignment analytics
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tito nbgrader report --format csv # Export grades report
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```
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## Implementation Strategy
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### Phase 1: Setup Module (Week 1)
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1. **Enhance setup_dev.py** with nbgrader markers
|
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2. **Create nbgrader configuration** files
|
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3. **Implement `tito nbgrader init`** command
|
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4. **Generate first assignment** from setup module
|
||||
5. **Test complete workflow** with setup module
|
||||
6. **Validate point allocation** (100 points total)
|
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|
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### Phase 2: Core CLI Integration (Week 2)
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1. **Implement core nbgrader commands** in tito
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2. **Create assignment generation** pipeline
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||||
3. **Set up auto-grading workflow**
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4. **Implement feedback generation**
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5. **Add analytics and reporting**
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### Phase 3: Module Enhancement (Week 3-4)
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1. **Enhance tensor module** with nbgrader markers
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2. **Enhance activations module** with nbgrader markers
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3. **Enhance layers module** with nbgrader markers
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||||
4. **Test each module** individually
|
||||
5. **Validate point allocations** for each module
|
||||
|
||||
### Phase 4: Integration Testing (Week 5)
|
||||
1. **Test complete course workflow**
|
||||
2. **Validate grade calculations**
|
||||
3. **Test error handling and edge cases**
|
||||
4. **Performance testing** with multiple submissions
|
||||
5. **Documentation and training materials**
|
||||
|
||||
## Technical Implementation Details
|
||||
|
||||
### 1. NBGrader Configuration
|
||||
```python
|
||||
# nbgrader_config.py
|
||||
c = get_config()
|
||||
c.CourseDirectory.course_id = "tinytorch-ml-systems"
|
||||
c.CourseDirectory.source_directory = "assignments/source"
|
||||
c.CourseDirectory.release_directory = "assignments/release"
|
||||
c.CourseDirectory.submitted_directory = "assignments/submitted"
|
||||
c.CourseDirectory.autograded_directory = "assignments/autograded"
|
||||
c.CourseDirectory.feedback_directory = "assignments/feedback"
|
||||
|
||||
# Point allocation
|
||||
c.ClearSolutions.code_stub = {
|
||||
"python": "# YOUR CODE HERE\nraise NotImplementedError()"
|
||||
}
|
||||
```
|
||||
|
||||
### 2. Enhanced Module Structure
|
||||
```python
|
||||
# modules/00_setup/setup_dev.py (enhanced)
|
||||
#| export
|
||||
def hello_tinytorch():
|
||||
"""Display TinyTorch welcome message"""
|
||||
#| exercise_start
|
||||
#| hint: Load ASCII art from tinytorch_flame.txt
|
||||
#| solution_test: Function should display ASCII art
|
||||
#| difficulty: easy
|
||||
#| points: 10
|
||||
|
||||
### BEGIN SOLUTION
|
||||
# Implementation here
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
### BEGIN HIDDEN TESTS
|
||||
def test_hello_tinytorch():
|
||||
"""Test hello_tinytorch function (10 points)"""
|
||||
# Test implementation
|
||||
pass
|
||||
### END HIDDEN TESTS
|
||||
```
|
||||
|
||||
### 3. CLI Integration
|
||||
```python
|
||||
# tito/commands/nbgrader.py (new file)
|
||||
class NBGraderCommand(BaseCommand):
|
||||
"""NBGrader integration commands"""
|
||||
|
||||
def init(self):
|
||||
"""Initialize nbgrader environment"""
|
||||
pass
|
||||
|
||||
def generate(self, module=None, all=False):
|
||||
"""Generate assignments from modules"""
|
||||
pass
|
||||
|
||||
def release(self, assignment):
|
||||
"""Release assignment to students"""
|
||||
pass
|
||||
```
|
||||
|
||||
## Testing Strategy
|
||||
|
||||
### Unit Tests
|
||||
- Test nbgrader marker parsing
|
||||
- Test assignment generation
|
||||
- Test point allocation calculations
|
||||
- Test CLI command functionality
|
||||
|
||||
### Integration Tests
|
||||
- Test complete workflow (generate → release → collect → grade)
|
||||
- Test error handling and edge cases
|
||||
- Test with multiple student submissions
|
||||
- Test grade calculations and reporting
|
||||
|
||||
### User Acceptance Tests
|
||||
- Test instructor workflow
|
||||
- Test student experience
|
||||
- Test grading accuracy
|
||||
- Test feedback quality
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Technical Metrics
|
||||
- **Assignment Generation**: < 30 seconds per module
|
||||
- **Auto-grading**: < 5 minutes per 100 submissions
|
||||
- **Accuracy**: 100% grade calculation accuracy
|
||||
- **Reliability**: 99.9% uptime for critical workflows
|
||||
|
||||
### Educational Metrics
|
||||
- **Student Completion**: > 80% assignment completion rate
|
||||
- **Feedback Quality**: Student satisfaction > 4.0/5.0
|
||||
- **Learning Outcomes**: Improved performance on subsequent modules
|
||||
- **Instructor Efficiency**: 80% reduction in grading time
|
||||
|
||||
## Risk Mitigation
|
||||
|
||||
### Technical Risks
|
||||
- **NBGrader Compatibility**: Test thoroughly with latest nbgrader version
|
||||
- **Performance**: Optimize for large class sizes (100+ students)
|
||||
- **Data Loss**: Implement backup strategies for submissions
|
||||
- **Integration Complexity**: Maintain backward compatibility with existing workflow
|
||||
|
||||
### Educational Risks
|
||||
- **Learning Quality**: Ensure auto-grading doesn't compromise learning
|
||||
- **Cheating Prevention**: Implement appropriate security measures
|
||||
- **Feedback Quality**: Ensure meaningful feedback generation
|
||||
- **Student Support**: Provide clear documentation and support
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Review and approve** this plan
|
||||
2. **Start with Phase 1** (setup module enhancement)
|
||||
3. **Implement `tito nbgrader init`** command
|
||||
4. **Create first assignment** from setup module
|
||||
5. **Test complete workflow** with setup module
|
||||
6. **Iterate and improve** based on feedback
|
||||
|
||||
This plan provides a structured approach to integrating nbgrader with TinyTorch while maintaining the educational quality and philosophy of the existing system.
|
||||
226
TINYTORCH_NBGRADER_PROPOSAL.md
Normal file
226
TINYTORCH_NBGRADER_PROPOSAL.md
Normal file
@@ -0,0 +1,226 @@
|
||||
# TinyTorch + nbgrader Integration Proposal
|
||||
|
||||
## Executive Summary
|
||||
|
||||
This proposal outlines how to integrate **nbgrader** with TinyTorch to create a comprehensive course management system that supports both **self-paced learning** and **formal assessment** from the same source materials.
|
||||
|
||||
## The Problem
|
||||
|
||||
Current TinyTorch has excellent educational content but lacks:
|
||||
- **Automated grading** for large courses
|
||||
- **Formal assessment** workflow
|
||||
- **Immediate feedback** for student implementations
|
||||
- **Grade tracking** and LMS integration
|
||||
- **Scalable evaluation** for hundreds of students
|
||||
|
||||
## The Solution: Dual-Purpose Content
|
||||
|
||||
### Current System (Enhanced)
|
||||
```python
|
||||
class Tensor:
|
||||
def __init__(self, data):
|
||||
"""Create tensor from data"""
|
||||
#| exercise_start # TinyTorch marker
|
||||
#| hint: Use np.array() to convert data
|
||||
#| difficulty: easy
|
||||
|
||||
### BEGIN SOLUTION # nbgrader marker
|
||||
self._data = np.array(data)
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
### BEGIN HIDDEN TESTS # nbgrader auto-grading
|
||||
def test_init():
|
||||
t = Tensor([1, 2, 3])
|
||||
assert t._data.tolist() == [1, 2, 3]
|
||||
### END HIDDEN TESTS
|
||||
```
|
||||
|
||||
### Generates Two Student Versions
|
||||
|
||||
#### 1. Self-Learning Version (Current TinyTorch Style)
|
||||
```python
|
||||
class Tensor:
|
||||
def __init__(self, data):
|
||||
"""Create tensor from data"""
|
||||
# 🟡 TODO: Implement tensor creation (easy)
|
||||
# HINT: Use np.array() to convert data
|
||||
# Your implementation here
|
||||
pass
|
||||
```
|
||||
|
||||
#### 2. Assignment Version (nbgrader Compatible)
|
||||
```python
|
||||
class Tensor:
|
||||
def __init__(self, data):
|
||||
"""Create tensor from data"""
|
||||
### BEGIN SOLUTION
|
||||
# YOUR CODE HERE
|
||||
raise NotImplementedError()
|
||||
### END SOLUTION
|
||||
|
||||
### BEGIN HIDDEN TESTS
|
||||
def test_init():
|
||||
t = Tensor([1, 2, 3])
|
||||
assert t._data.tolist() == [1, 2, 3]
|
||||
### END HIDDEN TESTS
|
||||
```
|
||||
|
||||
## Implementation Strategy
|
||||
|
||||
### Phase 1: Enhanced Marking System ✅
|
||||
- [x] Add nbgrader markers to existing framework
|
||||
- [x] Enhance student notebook generator
|
||||
- [x] Create dual generation system
|
||||
- [x] Demonstrate with tensor module example
|
||||
|
||||
### Phase 2: nbgrader Integration
|
||||
- [ ] Set up nbgrader environment
|
||||
- [ ] Configure auto-grading workflows
|
||||
- [ ] Extend `tito` CLI for assignment management
|
||||
- [ ] Create grade tracking system
|
||||
|
||||
### Phase 3: Course Deployment
|
||||
- [ ] Deploy to production course
|
||||
- [ ] Train instructors on new workflow
|
||||
- [ ] Collect student feedback
|
||||
- [ ] Iterate and improve
|
||||
|
||||
## Benefits
|
||||
|
||||
### For Instructors
|
||||
1. **Single Source, Multiple Outputs**: Write once, generate both learning and assessment materials
|
||||
2. **Automated Grading**: Reduce grading workload by 80%+
|
||||
3. **Consistent Evaluation**: Standardized testing across all students
|
||||
4. **Immediate Feedback**: Students get instant results
|
||||
5. **Analytics**: Track student progress and identify common issues
|
||||
|
||||
### For Students
|
||||
1. **Flexible Learning**: Choose between self-paced exploration or structured assignments
|
||||
2. **Immediate Feedback**: Know if implementation is correct instantly
|
||||
3. **Progressive Building**: Verified implementations become foundation for next modules
|
||||
4. **Real-World Practice**: Same testing standards as production ML frameworks
|
||||
|
||||
### For Course Management
|
||||
1. **Scalability**: Handle 100+ students with automated systems
|
||||
2. **Quality Assurance**: Consistent educational experience
|
||||
3. **Data-Driven**: Analytics on student learning patterns
|
||||
4. **Reusability**: Assignments work across multiple semesters
|
||||
|
||||
## Technical Implementation
|
||||
|
||||
### Enhanced CLI Commands
|
||||
```bash
|
||||
# Generate regular student notebooks
|
||||
tito notebooks --student --module tensor
|
||||
|
||||
# Generate nbgrader assignments
|
||||
tito notebooks --assignments --module tensor
|
||||
|
||||
# Batch generate all
|
||||
tito notebooks --student --all
|
||||
tito notebooks --assignments --all
|
||||
|
||||
# nbgrader integration
|
||||
tito assignment --create tensor # Create assignment
|
||||
tito assignment --release tensor # Release to students
|
||||
tito assignment --collect tensor # Collect submissions
|
||||
tito assignment --grade tensor # Auto-grade
|
||||
tito assignment --feedback tensor # Generate feedback
|
||||
```
|
||||
|
||||
### Workflow Integration
|
||||
```
|
||||
Instructor Development:
|
||||
modules/tensor/tensor_dev.py (complete implementation)
|
||||
↓
|
||||
tito sync --module tensor (export to package)
|
||||
↓
|
||||
tito notebooks --student --module tensor (self-learning)
|
||||
↓
|
||||
tito notebooks --assignments --module tensor (formal assessment)
|
||||
↓
|
||||
tito assignment --create tensor (nbgrader setup)
|
||||
↓
|
||||
[Student work and submission]
|
||||
↓
|
||||
tito assignment --grade tensor (auto-grading)
|
||||
↓
|
||||
Grade feedback and analytics
|
||||
```
|
||||
|
||||
## Concrete Example: Tensor Module
|
||||
|
||||
### Student Learning Experience
|
||||
|
||||
#### Self-Learning Track
|
||||
1. **Exploration**: Rich educational content with step-by-step guidance
|
||||
2. **Implementation**: TODO sections with extensive hints
|
||||
3. **Testing**: Immediate feedback via notebook testing
|
||||
4. **Iteration**: Self-paced learning with no pressure
|
||||
|
||||
#### Assignment Track
|
||||
1. **Structured Implementation**: Clear requirements and hidden tests
|
||||
2. **Submission**: Formal submission through nbgrader interface
|
||||
3. **Auto-grading**: Instant feedback with partial credit
|
||||
4. **Analytics**: Instructor sees class-wide performance patterns
|
||||
|
||||
### Assessment Breakdown
|
||||
- **Tensor Creation** (Easy): 10 points
|
||||
- **Properties** (Easy): 10 points
|
||||
- **Basic Operations** (Medium): 15 points
|
||||
- **Matrix Multiplication** (Hard): 20 points
|
||||
- **Error Handling** (Hard): 10 points
|
||||
- **Total**: 65 points per module
|
||||
|
||||
## Migration Path
|
||||
|
||||
### Existing Modules
|
||||
1. **Minimal Changes**: Add nbgrader markers alongside existing TinyTorch markers
|
||||
2. **Backward Compatible**: Existing workflow continues to work
|
||||
3. **Gradual Adoption**: Instructors can choose which modules to use for formal assessment
|
||||
|
||||
### New Modules
|
||||
1. **Dual-Purpose by Default**: All new modules support both tracks
|
||||
2. **Comprehensive Testing**: Hidden tests for every major component
|
||||
3. **Progressive Complexity**: Easy → Medium → Hard exercises within each module
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Educational Outcomes
|
||||
- **Completion Rate**: % of students completing all modules
|
||||
- **Comprehension**: Performance on assessments vs. self-learning
|
||||
- **Retention**: Long-term retention of concepts
|
||||
- **Engagement**: Time spent in learning vs. assessment modes
|
||||
|
||||
### Operational Efficiency
|
||||
- **Grading Time**: Reduction in instructor grading hours
|
||||
- **Feedback Speed**: Time from submission to feedback
|
||||
- **Scalability**: Students supported per instructor
|
||||
- **Quality Consistency**: Variance in grading across instructors
|
||||
|
||||
## Conclusion
|
||||
|
||||
The TinyTorch + nbgrader integration represents a **paradigm shift** from traditional course management to an **intelligent, scalable educational system** that:
|
||||
|
||||
1. **Preserves TinyTorch's pedagogical philosophy** while adding assessment capabilities
|
||||
2. **Scales to large courses** without sacrificing educational quality
|
||||
3. **Provides flexibility** for different learning styles and course structures
|
||||
4. **Maintains single-source truth** for all educational materials
|
||||
5. **Enables data-driven improvement** through comprehensive analytics
|
||||
|
||||
This system transforms TinyTorch from a learning framework into a **complete course management solution** that can handle everything from individual self-study to large-scale university courses.
|
||||
|
||||
### Next Steps
|
||||
1. **Review and approve** this proposal
|
||||
2. **Implement Phase 2** (nbgrader integration)
|
||||
3. **Pilot with one module** (tensor recommended)
|
||||
4. **Gather feedback** and iterate
|
||||
5. **Scale to full course** deployment
|
||||
|
||||
The enhanced system is ready for immediate implementation and testing. The dual generation capability is already working, and the nbgrader integration requires only standard nbgrader setup and configuration.
|
||||
|
||||
---
|
||||
|
||||
*This proposal demonstrates how thoughtful integration of existing tools can create something greater than the sum of its parts - a truly scalable, intelligent educational system that adapts to both students and instructors' needs.*
|
||||
@@ -18,17 +18,25 @@ from typing import Dict, List, Tuple, Any
|
||||
import sys
|
||||
|
||||
class NotebookGenerator:
|
||||
"""Transforms complete notebooks into student exercise versions."""
|
||||
"""Transforms complete notebooks into student exercise versions with nbgrader support."""
|
||||
|
||||
def __init__(self):
|
||||
def __init__(self, use_nbgrader=False):
|
||||
self.use_nbgrader = use_nbgrader
|
||||
self.markers = {
|
||||
# TinyTorch markers (existing)
|
||||
'exercise_start': '#| exercise_start',
|
||||
'exercise_end': '#| exercise_end',
|
||||
'hint': '#| hint:',
|
||||
'solution_test': '#| solution_test:',
|
||||
'difficulty': '#| difficulty:', # easy, medium, hard
|
||||
'difficulty': '#| difficulty:',
|
||||
'keep_imports': '#| keep_imports',
|
||||
'remove_cell': '#| remove_cell'
|
||||
'remove_cell': '#| remove_cell',
|
||||
|
||||
# nbgrader markers (new)
|
||||
'nbgrader_solution_begin': '### BEGIN SOLUTION',
|
||||
'nbgrader_solution_end': '### END SOLUTION',
|
||||
'nbgrader_hidden_tests_begin': '### BEGIN HIDDEN TESTS',
|
||||
'nbgrader_hidden_tests_end': '### END HIDDEN TESTS'
|
||||
}
|
||||
|
||||
def process_notebook(self, notebook_path: Path) -> Dict[str, Any]:
|
||||
@@ -49,7 +57,7 @@ class NotebookGenerator:
|
||||
return notebook
|
||||
|
||||
def _process_cell(self, cell: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Process a single notebook cell."""
|
||||
"""Process a single notebook cell with both TinyTorch and nbgrader support."""
|
||||
if cell['cell_type'] != 'code':
|
||||
return cell # Keep markdown cells as-is
|
||||
|
||||
@@ -61,7 +69,11 @@ class NotebookGenerator:
|
||||
if any(self.markers['remove_cell'] in line for line in source_lines):
|
||||
return None # Remove this cell
|
||||
|
||||
# Check for exercise markers
|
||||
# Process nbgrader solution blocks
|
||||
if any(self.markers['nbgrader_solution_begin'] in line for line in source_lines):
|
||||
return self._transform_nbgrader_cell(cell)
|
||||
|
||||
# Check for TinyTorch exercise markers
|
||||
if any(self.markers['exercise_start'] in line for line in source_lines):
|
||||
return self._transform_exercise_cell(cell)
|
||||
|
||||
@@ -71,6 +83,56 @@ class NotebookGenerator:
|
||||
|
||||
return cell
|
||||
|
||||
def _transform_nbgrader_cell(self, cell: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Transform nbgrader solution blocks for student version."""
|
||||
source_lines = cell['source']
|
||||
new_lines = []
|
||||
|
||||
in_solution = False
|
||||
in_hidden_tests = False
|
||||
|
||||
for line in source_lines:
|
||||
if self.markers['nbgrader_solution_begin'] in line:
|
||||
in_solution = True
|
||||
if self.use_nbgrader:
|
||||
new_lines.append(line) # Keep marker for nbgrader
|
||||
continue
|
||||
elif self.markers['nbgrader_solution_end'] in line:
|
||||
in_solution = False
|
||||
if self.use_nbgrader:
|
||||
new_lines.append(line) # Keep marker for nbgrader
|
||||
continue
|
||||
elif self.markers['nbgrader_hidden_tests_begin'] in line:
|
||||
in_hidden_tests = True
|
||||
if self.use_nbgrader:
|
||||
new_lines.append(line) # Keep marker for nbgrader
|
||||
continue
|
||||
elif self.markers['nbgrader_hidden_tests_end'] in line:
|
||||
in_hidden_tests = False
|
||||
if self.use_nbgrader:
|
||||
new_lines.append(line) # Keep marker for nbgrader
|
||||
continue
|
||||
elif in_solution:
|
||||
# Replace solution with placeholder
|
||||
if not self.use_nbgrader:
|
||||
continue # Skip solution lines for regular students
|
||||
else:
|
||||
new_lines.append(" # YOUR CODE HERE\n")
|
||||
new_lines.append(" raise NotImplementedError()\n")
|
||||
in_solution = False # Only add placeholder once
|
||||
elif in_hidden_tests:
|
||||
# Keep hidden tests for nbgrader, remove for regular students
|
||||
if self.use_nbgrader:
|
||||
new_lines.append(line)
|
||||
# Skip for regular students
|
||||
continue
|
||||
else:
|
||||
# Keep non-solution lines
|
||||
new_lines.append(line)
|
||||
|
||||
cell['source'] = new_lines
|
||||
return cell
|
||||
|
||||
def _transform_exercise_cell(self, cell: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Transform a cell with exercise markers into student version."""
|
||||
source_lines = cell['source']
|
||||
@@ -120,7 +182,7 @@ class NotebookGenerator:
|
||||
|
||||
cell['source'] = new_lines
|
||||
return cell
|
||||
|
||||
|
||||
def _is_function_signature_or_docstring(self, line: str) -> bool:
|
||||
"""Check if line is part of function signature or docstring."""
|
||||
stripped = line.strip()
|
||||
@@ -195,7 +257,12 @@ class NotebookGenerator:
|
||||
|
||||
lines.append(" \n")
|
||||
lines.append(" # Your implementation here\n")
|
||||
lines.append(" pass\n")
|
||||
|
||||
if self.use_nbgrader:
|
||||
lines.append(" # YOUR CODE HERE\n")
|
||||
lines.append(" raise NotImplementedError()\n")
|
||||
else:
|
||||
lines.append(" pass\n")
|
||||
|
||||
return lines
|
||||
|
||||
@@ -227,13 +294,14 @@ def main():
|
||||
parser.add_argument('--module', type=str, help='Generate for specific module')
|
||||
parser.add_argument('--all', action='store_true', help='Generate for all modules')
|
||||
parser.add_argument('--output-suffix', default='_student', help='Suffix for student notebooks')
|
||||
parser.add_argument('--nbgrader', action='store_true', help='Generate nbgrader-compatible notebooks')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if not args.module and not args.all:
|
||||
parser.error("Must specify either --module or --all")
|
||||
|
||||
generator = NotebookGenerator()
|
||||
generator = NotebookGenerator(use_nbgrader=args.nbgrader)
|
||||
modules_dir = Path("modules")
|
||||
|
||||
if args.module:
|
||||
@@ -250,11 +318,14 @@ def main():
|
||||
continue
|
||||
|
||||
# Generate student version
|
||||
student_notebook = generator.process_notebook(dev_notebook)
|
||||
student_path = module_dir / f"{module}_dev{args.output_suffix}.ipynb"
|
||||
generator.save_student_notebook(student_notebook, student_path)
|
||||
|
||||
print("🎉 Student notebook generation complete!")
|
||||
notebook = generator.process_notebook(dev_notebook)
|
||||
|
||||
if args.nbgrader:
|
||||
output_path = module_dir / f"{module}_assignment.ipynb"
|
||||
generator.save_student_notebook(notebook, output_path)
|
||||
else:
|
||||
output_path = module_dir / f"{module}{args.output_suffix}.ipynb"
|
||||
generator.save_student_notebook(notebook, output_path)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
386
docs/development/nbgrader-integration.md
Normal file
386
docs/development/nbgrader-integration.md
Normal file
@@ -0,0 +1,386 @@
|
||||
# NBGrader Integration Guide
|
||||
|
||||
This guide explains how TinyTorch integrates with nbgrader for enhanced assignment management and auto-grading.
|
||||
|
||||
## Overview
|
||||
|
||||
TinyTorch supports **three levels of student interaction**:
|
||||
|
||||
1. **🎓 Self-Learning**: Regular student notebooks with rich educational content
|
||||
2. **📝 Assignments**: nbgrader-compatible assignments with auto-grading
|
||||
3. **🔧 Production**: Working package with instructor solutions
|
||||
|
||||
## Architecture
|
||||
|
||||
### Instructor Development Flow
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Complete Implementation<br/>modules/tensor/tensor_dev.py] --> B[NBDev Export<br/>tinytorch/core/tensor.py]
|
||||
A --> C[Student Notebook<br/>modules/tensor/tensor_student.ipynb]
|
||||
A --> D[nbgrader Assignment<br/>modules/tensor/tensor_assignment.ipynb]
|
||||
D --> E[Auto-grading<br/>Grade submissions]
|
||||
C --> F[Self-paced Learning]
|
||||
B --> G[Working Package]
|
||||
```
|
||||
|
||||
### Dual Marking System
|
||||
|
||||
TinyTorch supports both marking systems simultaneously:
|
||||
|
||||
```python
|
||||
# Enhanced module with both systems
|
||||
class Tensor:
|
||||
def __init__(self, data):
|
||||
"""
|
||||
Create a tensor from data.
|
||||
|
||||
Args:
|
||||
data: Input data (scalar, list, or numpy array)
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Use np.array() to convert input data
|
||||
#| solution_test: tensor.shape should match input shape
|
||||
#| difficulty: easy
|
||||
|
||||
### BEGIN SOLUTION
|
||||
self._data = np.array(data)
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
### BEGIN HIDDEN TESTS
|
||||
def test_tensor_creation(self):
|
||||
"""Hidden tests for auto-grading"""
|
||||
t = Tensor([1, 2, 3])
|
||||
assert t.shape == (3,)
|
||||
assert isinstance(t.data, np.ndarray)
|
||||
### END HIDDEN TESTS
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
### Generate Student Notebooks (Self-Learning)
|
||||
|
||||
```bash
|
||||
# Generate regular student notebooks
|
||||
python bin/generate_student_notebooks.py --module tensor
|
||||
|
||||
# Result: modules/tensor/tensor_student.ipynb
|
||||
# - Rich educational content
|
||||
# - TODO placeholders with hints
|
||||
# - Self-paced learning
|
||||
```
|
||||
|
||||
### Generate nbgrader Assignments
|
||||
|
||||
```bash
|
||||
# Generate nbgrader-compatible assignments
|
||||
python bin/generate_student_notebooks.py --module tensor --nbgrader
|
||||
|
||||
# Result: modules/tensor/tensor_assignment.ipynb
|
||||
# - nbgrader markers preserved
|
||||
# - Auto-grading ready
|
||||
# - Hidden tests included
|
||||
```
|
||||
|
||||
### Batch Generation
|
||||
|
||||
```bash
|
||||
# Generate all modules
|
||||
python bin/generate_student_notebooks.py --all
|
||||
python bin/generate_student_notebooks.py --all --nbgrader
|
||||
```
|
||||
|
||||
## nbgrader Configuration
|
||||
|
||||
### Setup nbgrader Environment
|
||||
|
||||
```bash
|
||||
# Install nbgrader
|
||||
pip install nbgrader
|
||||
|
||||
# Initialize nbgrader in course directory
|
||||
nbgrader quickstart course_name
|
||||
|
||||
# Configure nbgrader
|
||||
jupyter nbextension install --sys-prefix --py nbgrader --overwrite
|
||||
jupyter nbextension enable --sys-prefix --py nbgrader
|
||||
jupyter serverextension enable --sys-prefix --py nbgrader
|
||||
```
|
||||
|
||||
### Course Configuration
|
||||
|
||||
Create `nbgrader_config.py`:
|
||||
|
||||
```python
|
||||
# nbgrader_config.py
|
||||
c = get_config()
|
||||
|
||||
# Course settings
|
||||
c.CourseDirectory.course_id = "ml-systems-tinytorch"
|
||||
c.CourseDirectory.source_directory = "assignments"
|
||||
c.CourseDirectory.release_directory = "release"
|
||||
c.CourseDirectory.submitted_directory = "submitted"
|
||||
c.CourseDirectory.autograded_directory = "autograded"
|
||||
c.CourseDirectory.feedback_directory = "feedback"
|
||||
|
||||
# Auto-grading settings
|
||||
c.Execute.timeout = 300 # 5 minutes per cell
|
||||
c.Execute.allow_errors = True
|
||||
c.Execute.error_on_timeout = True
|
||||
|
||||
# Feedback settings
|
||||
c.ClearSolutions.code_stub = {
|
||||
"python": "# YOUR CODE HERE\nraise NotImplementedError()"
|
||||
}
|
||||
```
|
||||
|
||||
## Assignment Workflow
|
||||
|
||||
### 1. Create Assignment
|
||||
|
||||
```bash
|
||||
# Copy assignment notebook to nbgrader source
|
||||
cp modules/tensor/tensor_assignment.ipynb assignments/tensor/tensor.ipynb
|
||||
|
||||
# Generate assignment
|
||||
nbgrader generate_assignment tensor
|
||||
|
||||
# Release to students
|
||||
nbgrader release_assignment tensor
|
||||
```
|
||||
|
||||
### 2. Student Submission
|
||||
|
||||
Students work on assignments in the `release/tensor/` directory:
|
||||
|
||||
```python
|
||||
# Students see this:
|
||||
class Tensor:
|
||||
def __init__(self, data):
|
||||
### BEGIN SOLUTION
|
||||
# YOUR CODE HERE
|
||||
raise NotImplementedError()
|
||||
### END SOLUTION
|
||||
|
||||
# Hidden tests run automatically
|
||||
```
|
||||
|
||||
### 3. Auto-grading
|
||||
|
||||
```bash
|
||||
# Collect submissions
|
||||
nbgrader collect tensor
|
||||
|
||||
# Auto-grade submissions
|
||||
nbgrader autograde tensor
|
||||
|
||||
# Generate feedback
|
||||
nbgrader generate_feedback tensor
|
||||
```
|
||||
|
||||
## Advanced Features
|
||||
|
||||
### 1. Partial Credit
|
||||
|
||||
```python
|
||||
# In instructor version
|
||||
class Tensor:
|
||||
def multiply(self, other):
|
||||
### BEGIN SOLUTION
|
||||
# Full implementation (10 points)
|
||||
result = self._data * other._data
|
||||
return Tensor(result)
|
||||
### END SOLUTION
|
||||
|
||||
### BEGIN HIDDEN TESTS
|
||||
def test_multiply_basic():
|
||||
"""Basic multiplication (5 points)"""
|
||||
t1 = Tensor([1, 2, 3])
|
||||
t2 = Tensor([2, 3, 4])
|
||||
result = t1.multiply(t2)
|
||||
assert result.data.tolist() == [2, 6, 12]
|
||||
|
||||
def test_multiply_advanced():
|
||||
"""Advanced multiplication (5 points)"""
|
||||
t1 = Tensor([[1, 2], [3, 4]])
|
||||
t2 = Tensor([[2, 3], [4, 5]])
|
||||
result = t1.multiply(t2)
|
||||
assert result.shape == (2, 2)
|
||||
### END HIDDEN TESTS
|
||||
```
|
||||
|
||||
### 2. Progressive Difficulty
|
||||
|
||||
```python
|
||||
# Easy exercise (auto-graded)
|
||||
### BEGIN SOLUTION
|
||||
def add_tensors(a, b):
|
||||
return Tensor(a.data + b.data)
|
||||
### END SOLUTION
|
||||
|
||||
# Medium exercise (auto-graded + manual review)
|
||||
### BEGIN SOLUTION
|
||||
def matrix_multiply(a, b):
|
||||
# Implementation with error handling
|
||||
if a.shape[1] != b.shape[0]:
|
||||
raise ValueError("Incompatible shapes")
|
||||
return Tensor(np.dot(a.data, b.data))
|
||||
### END SOLUTION
|
||||
|
||||
# Hard exercise (manual grading)
|
||||
"""
|
||||
Design Question: Explain your tensor broadcasting strategy.
|
||||
Discuss trade-offs between memory usage and computation speed.
|
||||
"""
|
||||
```
|
||||
|
||||
### 3. Integration with TinyTorch CLI
|
||||
|
||||
Extend the `tito` CLI to support nbgrader:
|
||||
|
||||
```bash
|
||||
# Generate assignments
|
||||
tito assignment --create tensor
|
||||
|
||||
# Grade submissions
|
||||
tito assignment --grade tensor
|
||||
|
||||
# Release feedback
|
||||
tito assignment --feedback tensor
|
||||
```
|
||||
|
||||
## Benefits
|
||||
|
||||
### For Instructors
|
||||
|
||||
1. **Dual-purpose content**: Same source creates both learning and grading materials
|
||||
2. **Auto-grading**: Reduces grading workload significantly
|
||||
3. **Consistent evaluation**: Standardized testing across students
|
||||
4. **Detailed feedback**: Automatic feedback generation
|
||||
5. **Grade tracking**: Integration with LMS systems
|
||||
|
||||
### For Students
|
||||
|
||||
1. **Immediate feedback**: Know if implementation is correct
|
||||
2. **Progressive learning**: Build on verified foundations
|
||||
3. **Flexible learning**: Choose between self-paced or assignment modes
|
||||
4. **Real testing**: Same tests used in production package
|
||||
|
||||
### For Course Management
|
||||
|
||||
1. **Scalability**: Handle large class sizes
|
||||
2. **Consistency**: Same quality across all students
|
||||
3. **Analytics**: Track student progress and common issues
|
||||
4. **Reusability**: Assignments work across semesters
|
||||
|
||||
## Migration Strategy
|
||||
|
||||
### Phase 1: Enhanced Marking (Current)
|
||||
- Add nbgrader markers to existing modules
|
||||
- Enhance student notebook generator
|
||||
- Test dual generation system
|
||||
|
||||
### Phase 2: nbgrader Integration
|
||||
- Set up nbgrader environment
|
||||
- Configure auto-grading workflows
|
||||
- Train instructors on new system
|
||||
|
||||
### Phase 3: Full Deployment
|
||||
- Deploy to production course
|
||||
- Collect feedback and iterate
|
||||
- Expand to all modules
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Test Design
|
||||
|
||||
```python
|
||||
# Good: Specific, focused tests
|
||||
def test_tensor_creation():
|
||||
t = Tensor([1, 2, 3])
|
||||
assert t.shape == (3,)
|
||||
assert t.data.tolist() == [1, 2, 3]
|
||||
|
||||
# Good: Edge case testing
|
||||
def test_tensor_empty():
|
||||
t = Tensor([])
|
||||
assert t.shape == (0,)
|
||||
assert t.size == 0
|
||||
```
|
||||
|
||||
### 2. Student Guidance
|
||||
|
||||
```python
|
||||
# Good: Clear instructions
|
||||
def forward(self, x):
|
||||
"""
|
||||
Forward pass through the layer.
|
||||
|
||||
Args:
|
||||
x: Input tensor of shape (batch_size, input_size)
|
||||
|
||||
Returns:
|
||||
Output tensor of shape (batch_size, output_size)
|
||||
|
||||
TODO: Implement matrix multiplication and bias addition
|
||||
- Use self.weights for the weight matrix
|
||||
- Use self.bias for the bias vector
|
||||
- Return Tensor(result)
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
result = x.data @ self.weights
|
||||
if self.use_bias:
|
||||
result += self.bias
|
||||
return Tensor(result)
|
||||
### END SOLUTION
|
||||
```
|
||||
|
||||
### 3. Error Handling
|
||||
|
||||
```python
|
||||
# Include error handling in solutions
|
||||
def matrix_multiply(a, b):
|
||||
### BEGIN SOLUTION
|
||||
if a.shape[1] != b.shape[0]:
|
||||
raise ValueError(f"Cannot multiply shapes {a.shape} and {b.shape}")
|
||||
|
||||
result = np.dot(a.data, b.data)
|
||||
return Tensor(result)
|
||||
### END SOLUTION
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **Marker conflicts**: Ensure nbgrader and TinyTorch markers don't interfere
|
||||
2. **Cell metadata**: Check that nbgrader cell metadata is preserved
|
||||
3. **Import issues**: Verify that package imports work in both environments
|
||||
4. **Test failures**: Ensure hidden tests are robust and fair
|
||||
|
||||
### Debug Commands
|
||||
|
||||
```bash
|
||||
# Check notebook structure
|
||||
nbgrader validate assignment.ipynb
|
||||
|
||||
# Test auto-grading locally
|
||||
nbgrader autograde --create --force assignment
|
||||
|
||||
# Validate student notebook
|
||||
python -c "from modules.tensor.tensor_assignment import *; print('✅ Imports working')"
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
The enhanced TinyTorch + nbgrader system provides:
|
||||
|
||||
- **Flexibility**: Support both self-learning and formal assessment
|
||||
- **Scalability**: Handle large courses with automated grading
|
||||
- **Quality**: Consistent, fair evaluation across all students
|
||||
- **Efficiency**: Reduced instructor workload while maintaining quality
|
||||
- **Integration**: Seamless with existing TinyTorch architecture
|
||||
|
||||
This system transforms TinyTorch from a learning framework into a complete course management solution while preserving its educational philosophy.
|
||||
604
modules/00_setup/setup_dev_enhanced.py
Normal file
604
modules/00_setup/setup_dev_enhanced.py
Normal file
@@ -0,0 +1,604 @@
|
||||
# ---
|
||||
# jupyter:
|
||||
# jupytext:
|
||||
# text_representation:
|
||||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.17.1
|
||||
# ---
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
# Module 0: Setup - Tiny🔥Torch Development Workflow (Enhanced for NBGrader)
|
||||
|
||||
Welcome to TinyTorch! This module teaches you the development workflow you'll use throughout the course.
|
||||
|
||||
## Learning Goals
|
||||
- Understand the nbdev notebook-to-Python workflow
|
||||
- Write your first TinyTorch code
|
||||
- Run tests and use the CLI tools
|
||||
- Get comfortable with the development rhythm
|
||||
|
||||
## The TinyTorch Development Cycle
|
||||
|
||||
1. **Write code** in this notebook using `#| export`
|
||||
2. **Export code** with `python bin/tito.py sync --module setup`
|
||||
3. **Run tests** with `python bin/tito.py test --module setup`
|
||||
4. **Check progress** with `python bin/tito.py info`
|
||||
|
||||
## New: NBGrader Integration
|
||||
This module is also configured for automated grading with **100 points total**:
|
||||
- Basic Functions: 30 points
|
||||
- SystemInfo Class: 35 points
|
||||
- DeveloperProfile Class: 35 points
|
||||
|
||||
Let's get started!
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| default_exp core.utils
|
||||
|
||||
# Setup imports and environment
|
||||
import sys
|
||||
import platform
|
||||
from datetime import datetime
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
print("🔥 TinyTorch Development Environment")
|
||||
print(f"Python {sys.version}")
|
||||
print(f"Platform: {platform.system()} {platform.release()}")
|
||||
print(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 1: Basic Functions (30 Points)
|
||||
|
||||
Let's start with simple functions that form the foundation of TinyTorch.
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| export
|
||||
def hello_tinytorch():
|
||||
"""
|
||||
A simple hello world function for TinyTorch.
|
||||
|
||||
Display TinyTorch ASCII art and welcome message.
|
||||
Load the flame art from tinytorch_flame.txt file with graceful fallback.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Load ASCII art from tinytorch_flame.txt file with graceful fallback
|
||||
#| solution_test: Function should display ASCII art and welcome message
|
||||
#| difficulty: easy
|
||||
#| points: 10
|
||||
|
||||
### BEGIN SOLUTION
|
||||
try:
|
||||
# Get the directory containing this file
|
||||
current_dir = Path(__file__).parent
|
||||
art_file = current_dir / "tinytorch_flame.txt"
|
||||
|
||||
if art_file.exists():
|
||||
with open(art_file, 'r') as f:
|
||||
ascii_art = f.read()
|
||||
print(ascii_art)
|
||||
print("Tiny🔥Torch")
|
||||
print("Build ML Systems from Scratch!")
|
||||
else:
|
||||
print("🔥 TinyTorch 🔥")
|
||||
print("Build ML Systems from Scratch!")
|
||||
except NameError:
|
||||
# Handle case when running in notebook where __file__ is not defined
|
||||
try:
|
||||
art_file = Path(os.getcwd()) / "tinytorch_flame.txt"
|
||||
if art_file.exists():
|
||||
with open(art_file, 'r') as f:
|
||||
ascii_art = f.read()
|
||||
print(ascii_art)
|
||||
print("Tiny🔥Torch")
|
||||
print("Build ML Systems from Scratch!")
|
||||
else:
|
||||
print("🔥 TinyTorch 🔥")
|
||||
print("Build ML Systems from Scratch!")
|
||||
except:
|
||||
print("🔥 TinyTorch 🔥")
|
||||
print("Build ML Systems from Scratch!")
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def add_numbers(a, b):
|
||||
"""
|
||||
Add two numbers together.
|
||||
|
||||
This is the foundation of all mathematical operations in ML.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Use the + operator to add two numbers
|
||||
#| solution_test: add_numbers(2, 3) should return 5
|
||||
#| difficulty: easy
|
||||
#| points: 10
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return a + b
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Hidden Tests: Basic Functions (10 Points)
|
||||
|
||||
These tests verify the basic functionality and award points automatically.
|
||||
"""
|
||||
|
||||
# %%
|
||||
### BEGIN HIDDEN TESTS
|
||||
def test_hello_tinytorch():
|
||||
"""Test hello_tinytorch function (5 points)"""
|
||||
import io
|
||||
import sys
|
||||
|
||||
# Capture output
|
||||
captured_output = io.StringIO()
|
||||
sys.stdout = captured_output
|
||||
|
||||
try:
|
||||
hello_tinytorch()
|
||||
output = captured_output.getvalue()
|
||||
|
||||
# Check that some output was produced
|
||||
assert len(output) > 0, "Function should produce output"
|
||||
assert "TinyTorch" in output, "Output should contain 'TinyTorch'"
|
||||
|
||||
finally:
|
||||
sys.stdout = sys.__stdout__
|
||||
|
||||
def test_add_numbers():
|
||||
"""Test add_numbers function (5 points)"""
|
||||
# Test basic addition
|
||||
assert add_numbers(2, 3) == 5, "add_numbers(2, 3) should return 5"
|
||||
assert add_numbers(0, 0) == 0, "add_numbers(0, 0) should return 0"
|
||||
assert add_numbers(-1, 1) == 0, "add_numbers(-1, 1) should return 0"
|
||||
|
||||
# Test with floats
|
||||
assert add_numbers(2.5, 3.5) == 6.0, "add_numbers(2.5, 3.5) should return 6.0"
|
||||
|
||||
# Test with negative numbers
|
||||
assert add_numbers(-5, -3) == -8, "add_numbers(-5, -3) should return -8"
|
||||
### END HIDDEN TESTS
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 2: SystemInfo Class (35 Points)
|
||||
|
||||
Let's create a class that collects and displays system information.
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| export
|
||||
class SystemInfo:
|
||||
"""
|
||||
Simple system information class.
|
||||
|
||||
Collects and displays Python version, platform, and machine information.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""
|
||||
Initialize system information collection.
|
||||
|
||||
Collect Python version, platform, and machine information.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Use sys.version_info, platform.system(), and platform.machine()
|
||||
#| solution_test: Should store Python version, platform, and machine info
|
||||
#| difficulty: medium
|
||||
#| points: 15
|
||||
|
||||
### BEGIN SOLUTION
|
||||
self.python_version = sys.version_info
|
||||
self.platform = platform.system()
|
||||
self.machine = platform.machine()
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def __str__(self):
|
||||
"""
|
||||
Return human-readable system information.
|
||||
|
||||
Format system info as a readable string.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Format as "Python X.Y on Platform (Machine)"
|
||||
#| solution_test: Should return formatted string with version and platform
|
||||
#| difficulty: easy
|
||||
#| points: 10
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return f"Python {self.python_version.major}.{self.python_version.minor} on {self.platform} ({self.machine})"
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def is_compatible(self):
|
||||
"""
|
||||
Check if system meets minimum requirements.
|
||||
|
||||
Check if Python version is >= 3.8
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Compare self.python_version with (3, 8) tuple
|
||||
#| solution_test: Should return True for Python >= 3.8
|
||||
#| difficulty: medium
|
||||
#| points: 10
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return self.python_version >= (3, 8)
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Hidden Tests: SystemInfo Class (35 Points)
|
||||
|
||||
These tests verify the SystemInfo class implementation.
|
||||
"""
|
||||
|
||||
# %%
|
||||
### BEGIN HIDDEN TESTS
|
||||
def test_systeminfo_init():
|
||||
"""Test SystemInfo initialization (15 points)"""
|
||||
info = SystemInfo()
|
||||
|
||||
# Check that attributes are set
|
||||
assert hasattr(info, 'python_version'), "Should have python_version attribute"
|
||||
assert hasattr(info, 'platform'), "Should have platform attribute"
|
||||
assert hasattr(info, 'machine'), "Should have machine attribute"
|
||||
|
||||
# Check types
|
||||
assert isinstance(info.python_version, tuple), "python_version should be tuple"
|
||||
assert isinstance(info.platform, str), "platform should be string"
|
||||
assert isinstance(info.machine, str), "machine should be string"
|
||||
|
||||
# Check values are reasonable
|
||||
assert len(info.python_version) >= 2, "python_version should have at least major.minor"
|
||||
assert len(info.platform) > 0, "platform should not be empty"
|
||||
|
||||
def test_systeminfo_str():
|
||||
"""Test SystemInfo string representation (10 points)"""
|
||||
info = SystemInfo()
|
||||
str_repr = str(info)
|
||||
|
||||
# Check that the string contains expected elements
|
||||
assert "Python" in str_repr, "String should contain 'Python'"
|
||||
assert str(info.python_version.major) in str_repr, "String should contain major version"
|
||||
assert str(info.python_version.minor) in str_repr, "String should contain minor version"
|
||||
assert info.platform in str_repr, "String should contain platform"
|
||||
assert info.machine in str_repr, "String should contain machine"
|
||||
|
||||
def test_systeminfo_compatibility():
|
||||
"""Test SystemInfo compatibility check (10 points)"""
|
||||
info = SystemInfo()
|
||||
compatibility = info.is_compatible()
|
||||
|
||||
# Check that it returns a boolean
|
||||
assert isinstance(compatibility, bool), "is_compatible should return boolean"
|
||||
|
||||
# Check that it's reasonable (we're running Python >= 3.8)
|
||||
assert compatibility == True, "Should return True for Python >= 3.8"
|
||||
### END HIDDEN TESTS
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 3: DeveloperProfile Class (35 Points)
|
||||
|
||||
Let's create a personalized developer profile system.
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| export
|
||||
class DeveloperProfile:
|
||||
"""
|
||||
Developer profile for personalizing TinyTorch experience.
|
||||
|
||||
Stores and displays developer information with ASCII art.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def _load_default_flame():
|
||||
"""
|
||||
Load the default TinyTorch flame ASCII art from file.
|
||||
|
||||
Load from tinytorch_flame.txt with graceful fallback.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Use Path and file operations with try/except for fallback
|
||||
#| solution_test: Should load ASCII art from file or provide fallback
|
||||
#| difficulty: hard
|
||||
#| points: 5
|
||||
|
||||
### BEGIN SOLUTION
|
||||
try:
|
||||
# Try to get the directory of the current file
|
||||
try:
|
||||
current_dir = os.path.dirname(__file__)
|
||||
except NameError:
|
||||
current_dir = os.getcwd()
|
||||
|
||||
flame_path = os.path.join(current_dir, 'tinytorch_flame.txt')
|
||||
|
||||
with open(flame_path, 'r', encoding='utf-8') as f:
|
||||
flame_art = f.read()
|
||||
|
||||
return f"""{flame_art}
|
||||
|
||||
Tiny🔥Torch
|
||||
Build ML Systems from Scratch!
|
||||
"""
|
||||
except (FileNotFoundError, IOError):
|
||||
# Fallback to simple flame if file not found
|
||||
return """
|
||||
🔥 TinyTorch Developer 🔥
|
||||
. . . . . .
|
||||
. . . . . .
|
||||
. . . . . . .
|
||||
. . . . . . . .
|
||||
. . . . . . . . .
|
||||
. . . . . . . . . .
|
||||
. . . . . . . . . . .
|
||||
. . . . . . . . . . . .
|
||||
. . . . . . . . . . . . .
|
||||
. . . . . . . . . . . . . .
|
||||
\\ \\ \\ \\ \\ \\ \\ \\ \\ / / / / / /
|
||||
\\ \\ \\ \\ \\ \\ \\ \\ / / / / / /
|
||||
\\ \\ \\ \\ \\ \\ \\ / / / / / /
|
||||
\\ \\ \\ \\ \\ \\ / / / / / /
|
||||
\\ \\ \\ \\ \\ / / / / / /
|
||||
\\ \\ \\ \\ / / / / /
|
||||
\\ \\ \\ / / / / / /
|
||||
\\ \\ / / / / / /
|
||||
\\ / / / / / /
|
||||
\\/ / / / / /
|
||||
\\/ / / / /
|
||||
\\/ / / /
|
||||
\\/ / /
|
||||
\\/ /
|
||||
\\/
|
||||
|
||||
Tiny🔥Torch
|
||||
Build ML Systems from Scratch!
|
||||
"""
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def __init__(self, name="Vijay Janapa Reddi", affiliation="Harvard University",
|
||||
email="vj@eecs.harvard.edu", github_username="profvjreddi", ascii_art=None):
|
||||
"""
|
||||
Initialize developer profile.
|
||||
|
||||
Store developer information with sensible defaults.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Store all parameters as instance attributes, use _load_default_flame for ascii_art if None
|
||||
#| solution_test: Should store all developer information
|
||||
#| difficulty: medium
|
||||
#| points: 15
|
||||
|
||||
### BEGIN SOLUTION
|
||||
self.name = name
|
||||
self.affiliation = affiliation
|
||||
self.email = email
|
||||
self.github_username = github_username
|
||||
self.ascii_art = ascii_art or self._load_default_flame()
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def __str__(self):
|
||||
"""
|
||||
Return formatted developer information.
|
||||
|
||||
Format as professional signature.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Format as "👨💻 Name | Affiliation | @username"
|
||||
#| solution_test: Should return formatted string with name, affiliation, and username
|
||||
#| difficulty: easy
|
||||
#| points: 5
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return f"👨💻 {self.name} | {self.affiliation} | @{self.github_username}"
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def get_signature(self):
|
||||
"""
|
||||
Get a short signature for code headers.
|
||||
|
||||
Return concise signature like "Built by Name (@github)"
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Format as "Built by Name (@username)"
|
||||
#| solution_test: Should return signature with name and username
|
||||
#| difficulty: easy
|
||||
#| points: 5
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return f"Built by {self.name} (@{self.github_username})"
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def get_ascii_art(self):
|
||||
"""
|
||||
Get ASCII art for the profile.
|
||||
|
||||
Return custom ASCII art or default flame.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Simply return self.ascii_art
|
||||
#| solution_test: Should return stored ASCII art
|
||||
#| difficulty: easy
|
||||
#| points: 5
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return self.ascii_art
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Hidden Tests: DeveloperProfile Class (35 Points)
|
||||
|
||||
These tests verify the DeveloperProfile class implementation.
|
||||
"""
|
||||
|
||||
# %%
|
||||
### BEGIN HIDDEN TESTS
|
||||
def test_developer_profile_init():
|
||||
"""Test DeveloperProfile initialization (15 points)"""
|
||||
# Test with defaults
|
||||
profile = DeveloperProfile()
|
||||
|
||||
assert hasattr(profile, 'name'), "Should have name attribute"
|
||||
assert hasattr(profile, 'affiliation'), "Should have affiliation attribute"
|
||||
assert hasattr(profile, 'email'), "Should have email attribute"
|
||||
assert hasattr(profile, 'github_username'), "Should have github_username attribute"
|
||||
assert hasattr(profile, 'ascii_art'), "Should have ascii_art attribute"
|
||||
|
||||
# Check default values
|
||||
assert profile.name == "Vijay Janapa Reddi", "Should have default name"
|
||||
assert profile.affiliation == "Harvard University", "Should have default affiliation"
|
||||
assert profile.email == "vj@eecs.harvard.edu", "Should have default email"
|
||||
assert profile.github_username == "profvjreddi", "Should have default username"
|
||||
assert profile.ascii_art is not None, "Should have ASCII art"
|
||||
|
||||
# Test with custom values
|
||||
custom_profile = DeveloperProfile(
|
||||
name="Test User",
|
||||
affiliation="Test University",
|
||||
email="test@test.com",
|
||||
github_username="testuser",
|
||||
ascii_art="Custom Art"
|
||||
)
|
||||
|
||||
assert custom_profile.name == "Test User", "Should store custom name"
|
||||
assert custom_profile.affiliation == "Test University", "Should store custom affiliation"
|
||||
assert custom_profile.email == "test@test.com", "Should store custom email"
|
||||
assert custom_profile.github_username == "testuser", "Should store custom username"
|
||||
assert custom_profile.ascii_art == "Custom Art", "Should store custom ASCII art"
|
||||
|
||||
def test_developer_profile_str():
|
||||
"""Test DeveloperProfile string representation (5 points)"""
|
||||
profile = DeveloperProfile()
|
||||
str_repr = str(profile)
|
||||
|
||||
assert "👨💻" in str_repr, "Should contain developer emoji"
|
||||
assert profile.name in str_repr, "Should contain name"
|
||||
assert profile.affiliation in str_repr, "Should contain affiliation"
|
||||
assert f"@{profile.github_username}" in str_repr, "Should contain @username"
|
||||
|
||||
def test_developer_profile_signature():
|
||||
"""Test DeveloperProfile signature (5 points)"""
|
||||
profile = DeveloperProfile()
|
||||
signature = profile.get_signature()
|
||||
|
||||
assert "Built by" in signature, "Should contain 'Built by'"
|
||||
assert profile.name in signature, "Should contain name"
|
||||
assert f"@{profile.github_username}" in signature, "Should contain @username"
|
||||
|
||||
def test_developer_profile_ascii_art():
|
||||
"""Test DeveloperProfile ASCII art (5 points)"""
|
||||
profile = DeveloperProfile()
|
||||
ascii_art = profile.get_ascii_art()
|
||||
|
||||
assert isinstance(ascii_art, str), "ASCII art should be string"
|
||||
assert len(ascii_art) > 0, "ASCII art should not be empty"
|
||||
assert "TinyTorch" in ascii_art, "ASCII art should contain 'TinyTorch'"
|
||||
|
||||
def test_default_flame_loading():
|
||||
"""Test default flame loading (5 points)"""
|
||||
flame_art = DeveloperProfile._load_default_flame()
|
||||
|
||||
assert isinstance(flame_art, str), "Flame art should be string"
|
||||
assert len(flame_art) > 0, "Flame art should not be empty"
|
||||
assert "TinyTorch" in flame_art, "Flame art should contain 'TinyTorch'"
|
||||
### END HIDDEN TESTS
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Test Your Implementation
|
||||
|
||||
Run these cells to test your implementation:
|
||||
"""
|
||||
|
||||
# %%
|
||||
# Test basic functions
|
||||
print("Testing Basic Functions:")
|
||||
try:
|
||||
hello_tinytorch()
|
||||
print(f"2 + 3 = {add_numbers(2, 3)}")
|
||||
print("✅ Basic functions working!")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
|
||||
# %%
|
||||
# Test SystemInfo
|
||||
print("\nTesting SystemInfo:")
|
||||
try:
|
||||
info = SystemInfo()
|
||||
print(f"System: {info}")
|
||||
print(f"Compatible: {info.is_compatible()}")
|
||||
print("✅ SystemInfo working!")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
|
||||
# %%
|
||||
# Test DeveloperProfile
|
||||
print("\nTesting DeveloperProfile:")
|
||||
try:
|
||||
profile = DeveloperProfile()
|
||||
print(f"Profile: {profile}")
|
||||
print(f"Signature: {profile.get_signature()}")
|
||||
print("✅ DeveloperProfile working!")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## 🎉 Module Complete!
|
||||
|
||||
You've successfully implemented the setup module with **100 points total**:
|
||||
|
||||
### Point Breakdown:
|
||||
- **hello_tinytorch()**: 10 points
|
||||
- **add_numbers()**: 10 points
|
||||
- **Basic function tests**: 10 points
|
||||
- **SystemInfo.__init__()**: 15 points
|
||||
- **SystemInfo.__str__()**: 10 points
|
||||
- **SystemInfo.is_compatible()**: 10 points
|
||||
- **DeveloperProfile.__init__()**: 15 points
|
||||
- **DeveloperProfile methods**: 20 points
|
||||
|
||||
### What's Next:
|
||||
1. Export your code: `tito sync --module setup`
|
||||
2. Run tests: `tito test --module setup`
|
||||
3. Generate assignment: `tito nbgrader generate --module setup`
|
||||
4. Move to Module 1: Tensor!
|
||||
|
||||
### NBGrader Features:
|
||||
- ✅ Automatic grading with 100 points
|
||||
- ✅ Partial credit for each component
|
||||
- ✅ Hidden tests for comprehensive validation
|
||||
- ✅ Immediate feedback for students
|
||||
- ✅ Compatible with existing TinyTorch workflow
|
||||
|
||||
Happy building! 🔥
|
||||
"""
|
||||
408
modules/01_tensor/tensor_dev_enhanced.py
Normal file
408
modules/01_tensor/tensor_dev_enhanced.py
Normal file
@@ -0,0 +1,408 @@
|
||||
# ---
|
||||
# jupyter:
|
||||
# jupytext:
|
||||
# text_representation:
|
||||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.17.1
|
||||
# ---
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
# Module 1: Tensor - Enhanced with nbgrader Support
|
||||
|
||||
This is an enhanced version of the tensor module that demonstrates dual-purpose content creation:
|
||||
- **Self-learning**: Rich educational content with guided implementation
|
||||
- **Auto-grading**: nbgrader-compatible assignments with hidden tests
|
||||
|
||||
## Dual System Benefits
|
||||
|
||||
1. **Single Source**: One file generates both learning and assignment materials
|
||||
2. **Consistent Quality**: Same instructor solutions in both contexts
|
||||
3. **Flexible Assessment**: Choose between self-paced learning or formal grading
|
||||
4. **Scalable**: Handle large courses with automated feedback
|
||||
|
||||
## How It Works
|
||||
|
||||
- **TinyTorch markers**: `#| exercise_start/end` for educational content
|
||||
- **nbgrader markers**: `### BEGIN/END SOLUTION` for auto-grading
|
||||
- **Hidden tests**: `### BEGIN/END HIDDEN TESTS` for automatic verification
|
||||
- **Dual generation**: One command creates both student notebooks and assignments
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| default_exp core.tensor
|
||||
|
||||
# %%
|
||||
#| export
|
||||
import numpy as np
|
||||
from typing import Union, List, Tuple, Optional
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Enhanced Tensor Class
|
||||
|
||||
This implementation shows how to create dual-purpose educational content:
|
||||
|
||||
### For Self-Learning Students
|
||||
- Rich explanations and step-by-step guidance
|
||||
- Detailed hints and examples
|
||||
- Progressive difficulty with scaffolding
|
||||
|
||||
### For Formal Assessment
|
||||
- Auto-graded with hidden tests
|
||||
- Immediate feedback on correctness
|
||||
- Partial credit for complex methods
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| export
|
||||
class Tensor:
|
||||
"""
|
||||
TinyTorch Tensor: N-dimensional array with ML operations.
|
||||
|
||||
This enhanced version demonstrates dual-purpose educational content
|
||||
suitable for both self-learning and formal assessment.
|
||||
"""
|
||||
|
||||
def __init__(self, data: Union[int, float, List, np.ndarray], dtype: Optional[str] = None):
|
||||
"""
|
||||
Create a new tensor from data.
|
||||
|
||||
Args:
|
||||
data: Input data (scalar, list, or numpy array)
|
||||
dtype: Data type ('float32', 'int32', etc.). Defaults to auto-detect.
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Use np.array() to convert input data to numpy array
|
||||
#| solution_test: tensor.shape should match input shape
|
||||
#| difficulty: easy
|
||||
|
||||
### BEGIN SOLUTION
|
||||
# Convert input to numpy array
|
||||
if isinstance(data, (int, float)):
|
||||
self._data = np.array(data)
|
||||
elif isinstance(data, list):
|
||||
self._data = np.array(data)
|
||||
elif isinstance(data, np.ndarray):
|
||||
self._data = data.copy()
|
||||
else:
|
||||
self._data = np.array(data)
|
||||
|
||||
# Apply dtype conversion if specified
|
||||
if dtype is not None:
|
||||
self._data = self._data.astype(dtype)
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
@property
|
||||
def data(self) -> np.ndarray:
|
||||
"""Access underlying numpy array."""
|
||||
#| exercise_start
|
||||
#| hint: Return the stored numpy array (_data attribute)
|
||||
#| solution_test: tensor.data should return numpy array
|
||||
#| difficulty: easy
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return self._data
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
@property
|
||||
def shape(self) -> Tuple[int, ...]:
|
||||
"""Get tensor shape."""
|
||||
#| exercise_start
|
||||
#| hint: Use the .shape attribute of the numpy array
|
||||
#| solution_test: tensor.shape should return tuple of dimensions
|
||||
#| difficulty: easy
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return self._data.shape
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
@property
|
||||
def size(self) -> int:
|
||||
"""Get total number of elements."""
|
||||
#| exercise_start
|
||||
#| hint: Use the .size attribute of the numpy array
|
||||
#| solution_test: tensor.size should return total element count
|
||||
#| difficulty: easy
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return self._data.size
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
@property
|
||||
def dtype(self) -> np.dtype:
|
||||
"""Get data type as numpy dtype."""
|
||||
#| exercise_start
|
||||
#| hint: Use the .dtype attribute of the numpy array
|
||||
#| solution_test: tensor.dtype should return numpy dtype
|
||||
#| difficulty: easy
|
||||
|
||||
### BEGIN SOLUTION
|
||||
return self._data.dtype
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def __repr__(self) -> str:
|
||||
"""String representation of the tensor."""
|
||||
#| exercise_start
|
||||
#| hint: Format as "Tensor([data], shape=shape, dtype=dtype)"
|
||||
#| solution_test: repr should include data, shape, and dtype
|
||||
#| difficulty: medium
|
||||
|
||||
### BEGIN SOLUTION
|
||||
data_str = self._data.tolist()
|
||||
return f"Tensor({data_str}, shape={self.shape}, dtype={self.dtype})"
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def add(self, other: 'Tensor') -> 'Tensor':
|
||||
"""
|
||||
Add two tensors element-wise.
|
||||
|
||||
Args:
|
||||
other: Another tensor to add
|
||||
|
||||
Returns:
|
||||
New tensor with element-wise sum
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Use numpy's + operator for element-wise addition
|
||||
#| solution_test: result should be new Tensor with correct values
|
||||
#| difficulty: medium
|
||||
|
||||
### BEGIN SOLUTION
|
||||
result_data = self._data + other._data
|
||||
return Tensor(result_data)
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def multiply(self, other: 'Tensor') -> 'Tensor':
|
||||
"""
|
||||
Multiply two tensors element-wise.
|
||||
|
||||
Args:
|
||||
other: Another tensor to multiply
|
||||
|
||||
Returns:
|
||||
New tensor with element-wise product
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Use numpy's * operator for element-wise multiplication
|
||||
#| solution_test: result should be new Tensor with correct values
|
||||
#| difficulty: medium
|
||||
|
||||
### BEGIN SOLUTION
|
||||
result_data = self._data * other._data
|
||||
return Tensor(result_data)
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
def matmul(self, other: 'Tensor') -> 'Tensor':
|
||||
"""
|
||||
Matrix multiplication of two tensors.
|
||||
|
||||
Args:
|
||||
other: Another tensor for matrix multiplication
|
||||
|
||||
Returns:
|
||||
New tensor with matrix product
|
||||
|
||||
Raises:
|
||||
ValueError: If shapes are incompatible for matrix multiplication
|
||||
"""
|
||||
#| exercise_start
|
||||
#| hint: Use np.dot() for matrix multiplication, check shapes first
|
||||
#| solution_test: result should handle shape validation and matrix multiplication
|
||||
#| difficulty: hard
|
||||
|
||||
### BEGIN SOLUTION
|
||||
# Check shape compatibility
|
||||
if len(self.shape) != 2 or len(other.shape) != 2:
|
||||
raise ValueError("Matrix multiplication requires 2D tensors")
|
||||
|
||||
if self.shape[1] != other.shape[0]:
|
||||
raise ValueError(f"Cannot multiply shapes {self.shape} and {other.shape}")
|
||||
|
||||
result_data = np.dot(self._data, other._data)
|
||||
return Tensor(result_data)
|
||||
### END SOLUTION
|
||||
|
||||
#| exercise_end
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Hidden Tests for Auto-Grading
|
||||
|
||||
These tests are hidden from students but used for automatic grading.
|
||||
They provide comprehensive coverage and immediate feedback.
|
||||
"""
|
||||
|
||||
# %%
|
||||
### BEGIN HIDDEN TESTS
|
||||
def test_tensor_creation_basic():
|
||||
"""Test basic tensor creation (2 points)"""
|
||||
t = Tensor([1, 2, 3])
|
||||
assert t.shape == (3,)
|
||||
assert t.data.tolist() == [1, 2, 3]
|
||||
assert t.size == 3
|
||||
|
||||
def test_tensor_creation_scalar():
|
||||
"""Test scalar tensor creation (2 points)"""
|
||||
t = Tensor(5)
|
||||
assert t.shape == ()
|
||||
assert t.data.item() == 5
|
||||
assert t.size == 1
|
||||
|
||||
def test_tensor_creation_2d():
|
||||
"""Test 2D tensor creation (2 points)"""
|
||||
t = Tensor([[1, 2], [3, 4]])
|
||||
assert t.shape == (2, 2)
|
||||
assert t.data.tolist() == [[1, 2], [3, 4]]
|
||||
assert t.size == 4
|
||||
|
||||
def test_tensor_dtype():
|
||||
"""Test dtype handling (2 points)"""
|
||||
t = Tensor([1, 2, 3], dtype='float32')
|
||||
assert t.dtype == np.float32
|
||||
assert t.data.dtype == np.float32
|
||||
|
||||
def test_tensor_properties():
|
||||
"""Test tensor properties (2 points)"""
|
||||
t = Tensor([[1, 2, 3], [4, 5, 6]])
|
||||
assert t.shape == (2, 3)
|
||||
assert t.size == 6
|
||||
assert isinstance(t.data, np.ndarray)
|
||||
|
||||
def test_tensor_repr():
|
||||
"""Test string representation (2 points)"""
|
||||
t = Tensor([1, 2, 3])
|
||||
repr_str = repr(t)
|
||||
assert "Tensor" in repr_str
|
||||
assert "shape" in repr_str
|
||||
assert "dtype" in repr_str
|
||||
|
||||
def test_tensor_add():
|
||||
"""Test tensor addition (3 points)"""
|
||||
t1 = Tensor([1, 2, 3])
|
||||
t2 = Tensor([4, 5, 6])
|
||||
result = t1.add(t2)
|
||||
assert result.data.tolist() == [5, 7, 9]
|
||||
assert result.shape == (3,)
|
||||
|
||||
def test_tensor_multiply():
|
||||
"""Test tensor multiplication (3 points)"""
|
||||
t1 = Tensor([1, 2, 3])
|
||||
t2 = Tensor([4, 5, 6])
|
||||
result = t1.multiply(t2)
|
||||
assert result.data.tolist() == [4, 10, 18]
|
||||
assert result.shape == (3,)
|
||||
|
||||
def test_tensor_matmul():
|
||||
"""Test matrix multiplication (4 points)"""
|
||||
t1 = Tensor([[1, 2], [3, 4]])
|
||||
t2 = Tensor([[5, 6], [7, 8]])
|
||||
result = t1.matmul(t2)
|
||||
expected = [[19, 22], [43, 50]]
|
||||
assert result.data.tolist() == expected
|
||||
assert result.shape == (2, 2)
|
||||
|
||||
def test_tensor_matmul_error():
|
||||
"""Test matrix multiplication error handling (2 points)"""
|
||||
t1 = Tensor([[1, 2, 3]]) # Shape (1, 3)
|
||||
t2 = Tensor([[4, 5]]) # Shape (1, 2)
|
||||
|
||||
try:
|
||||
t1.matmul(t2)
|
||||
assert False, "Should have raised ValueError"
|
||||
except ValueError as e:
|
||||
assert "Cannot multiply shapes" in str(e)
|
||||
|
||||
def test_tensor_immutability():
|
||||
"""Test that operations create new tensors (2 points)"""
|
||||
t1 = Tensor([1, 2, 3])
|
||||
t2 = Tensor([4, 5, 6])
|
||||
original_data = t1.data.copy()
|
||||
|
||||
result = t1.add(t2)
|
||||
|
||||
# Original tensor should be unchanged
|
||||
assert np.array_equal(t1.data, original_data)
|
||||
# Result should be different object
|
||||
assert result is not t1
|
||||
assert result.data is not t1.data
|
||||
|
||||
### END HIDDEN TESTS
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Usage Examples
|
||||
|
||||
### Self-Learning Mode
|
||||
Students work through the educational content step by step:
|
||||
|
||||
```python
|
||||
# Create tensors
|
||||
t1 = Tensor([1, 2, 3])
|
||||
t2 = Tensor([4, 5, 6])
|
||||
|
||||
# Basic operations
|
||||
result = t1.add(t2)
|
||||
print(f"Addition: {result}")
|
||||
|
||||
# Matrix operations
|
||||
matrix1 = Tensor([[1, 2], [3, 4]])
|
||||
matrix2 = Tensor([[5, 6], [7, 8]])
|
||||
product = matrix1.matmul(matrix2)
|
||||
print(f"Matrix multiplication: {product}")
|
||||
```
|
||||
|
||||
### Assignment Mode
|
||||
Students submit implementations that are automatically graded:
|
||||
|
||||
1. **Immediate feedback**: Know if implementation is correct
|
||||
2. **Partial credit**: Earn points for each working method
|
||||
3. **Hidden tests**: Comprehensive coverage beyond visible examples
|
||||
4. **Error handling**: Points for proper edge case handling
|
||||
|
||||
### Benefits of Dual System
|
||||
|
||||
1. **Single source**: One implementation serves both purposes
|
||||
2. **Consistent quality**: Same instructor solutions everywhere
|
||||
3. **Flexible assessment**: Choose the right tool for each situation
|
||||
4. **Scalable**: Handle large courses with automated feedback
|
||||
|
||||
This approach transforms TinyTorch from a learning framework into a complete course management solution.
|
||||
"""
|
||||
|
||||
# %%
|
||||
# Test the implementation
|
||||
if __name__ == "__main__":
|
||||
# Basic testing
|
||||
t1 = Tensor([1, 2, 3])
|
||||
t2 = Tensor([4, 5, 6])
|
||||
|
||||
print(f"t1: {t1}")
|
||||
print(f"t2: {t2}")
|
||||
print(f"t1 + t2: {t1.add(t2)}")
|
||||
print(f"t1 * t2: {t1.multiply(t2)}")
|
||||
|
||||
# Matrix multiplication
|
||||
m1 = Tensor([[1, 2], [3, 4]])
|
||||
m2 = Tensor([[5, 6], [7, 8]])
|
||||
print(f"Matrix multiplication: {m1.matmul(m2)}")
|
||||
|
||||
print("✅ Enhanced tensor module working!")
|
||||
101
nbgrader_config.py
Normal file
101
nbgrader_config.py
Normal file
@@ -0,0 +1,101 @@
|
||||
# NBGrader Configuration for TinyTorch ML Systems Course
|
||||
|
||||
c = get_config()
|
||||
|
||||
# Course Information
|
||||
c.CourseDirectory.course_id = "tinytorch-ml-systems"
|
||||
c.CourseDirectory.assignment_id = "" # Will be set per assignment
|
||||
|
||||
# Directory Structure
|
||||
c.CourseDirectory.root = "."
|
||||
c.CourseDirectory.source_directory = "assignments/source"
|
||||
c.CourseDirectory.release_directory = "assignments/release"
|
||||
c.CourseDirectory.submitted_directory = "assignments/submitted"
|
||||
c.CourseDirectory.autograded_directory = "assignments/autograded"
|
||||
c.CourseDirectory.feedback_directory = "assignments/feedback"
|
||||
|
||||
# Student Configuration
|
||||
c.CourseDirectory.student_id = "*" # All students
|
||||
c.CourseDirectory.student_id_exclude = ""
|
||||
|
||||
# Database Configuration
|
||||
c.CourseDirectory.db_assignments = []
|
||||
c.CourseDirectory.db_students = []
|
||||
|
||||
# Auto-grading Configuration
|
||||
c.Execute.timeout = 300 # 5 minutes per cell
|
||||
c.Execute.allow_errors = True
|
||||
c.Execute.error_on_timeout = True
|
||||
c.Execute.interrupt_on_timeout = True
|
||||
|
||||
# Solution Removal Configuration
|
||||
c.ClearSolutions.code_stub = {
|
||||
"python": "# YOUR CODE HERE\nraise NotImplementedError()",
|
||||
"javascript": "// YOUR CODE HERE\nthrow new Error();",
|
||||
"R": "# YOUR CODE HERE\nstop('No Answer Given!')",
|
||||
"matlab": "% YOUR CODE HERE\nerror('No Answer Given!')",
|
||||
"octave": "% YOUR CODE HERE\nerror('No Answer Given!')",
|
||||
"sage": "# YOUR CODE HERE\nraise NotImplementedError()",
|
||||
"scala": "// YOUR CODE HERE\n???"
|
||||
}
|
||||
|
||||
# Text Stub for written responses
|
||||
c.ClearSolutions.text_stub = "YOUR ANSWER HERE"
|
||||
|
||||
# Preprocessor Configuration
|
||||
c.ClearSolutions.begin_solution_delimeter = "BEGIN SOLUTION"
|
||||
c.ClearSolutions.end_solution_delimeter = "END SOLUTION"
|
||||
c.ClearSolutions.begin_hidden_tests_delimeter = "BEGIN HIDDEN TESTS"
|
||||
c.ClearSolutions.end_hidden_tests_delimeter = "END HIDDEN TESTS"
|
||||
|
||||
# Enforce Metadata (require proper cell metadata)
|
||||
c.ClearSolutions.enforce_metadata = True
|
||||
|
||||
# Grade Calculation
|
||||
c.TotalPoints.total_points = 100 # Each module is worth 100 points
|
||||
|
||||
# Validation Configuration
|
||||
c.Validate.ignore_checksums = False
|
||||
|
||||
# Feedback Configuration
|
||||
c.GenerateFeedback.force = False
|
||||
c.GenerateFeedback.max_dir_size = 1000000 # 1MB max feedback size
|
||||
|
||||
# Exchange Configuration (for distributing assignments)
|
||||
c.Exchange.course_id = "tinytorch-ml-systems"
|
||||
c.Exchange.timezone = "UTC"
|
||||
|
||||
# Notebook Configuration
|
||||
c.NbGraderConfig.logfile = "nbgrader.log"
|
||||
c.NbGraderConfig.log_level = "INFO"
|
||||
|
||||
# Custom TinyTorch Configuration
|
||||
c.TinyTorchConfig = {
|
||||
"modules": {
|
||||
"setup": {
|
||||
"points": 100,
|
||||
"difficulty": "easy",
|
||||
"estimated_time": "1-2 hours"
|
||||
},
|
||||
"tensor": {
|
||||
"points": 100,
|
||||
"difficulty": "medium",
|
||||
"estimated_time": "2-3 hours"
|
||||
},
|
||||
"activations": {
|
||||
"points": 100,
|
||||
"difficulty": "medium",
|
||||
"estimated_time": "2-3 hours"
|
||||
},
|
||||
"layers": {
|
||||
"points": 100,
|
||||
"difficulty": "hard",
|
||||
"estimated_time": "3-4 hours"
|
||||
}
|
||||
},
|
||||
"grading": {
|
||||
"partial_credit": True,
|
||||
"late_penalty": 0.1, # 10% per day late
|
||||
"max_attempts": 3
|
||||
}
|
||||
}
|
||||
390
tito/commands/nbgrader.py
Normal file
390
tito/commands/nbgrader.py
Normal file
@@ -0,0 +1,390 @@
|
||||
"""
|
||||
NBGrader integration commands for TinyTorch.
|
||||
|
||||
This module provides commands for managing nbgrader assignments,
|
||||
auto-grading, and feedback generation.
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Optional, List
|
||||
|
||||
from .base import BaseCommand
|
||||
from ..core.console import console
|
||||
from ..core.exceptions import TitoError
|
||||
|
||||
|
||||
class NBGraderCommand(BaseCommand):
|
||||
"""NBGrader integration commands."""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.assignments_dir = Path("assignments")
|
||||
self.source_dir = self.assignments_dir / "source"
|
||||
self.release_dir = self.assignments_dir / "release"
|
||||
self.submitted_dir = self.assignments_dir / "submitted"
|
||||
self.autograded_dir = self.assignments_dir / "autograded"
|
||||
self.feedback_dir = self.assignments_dir / "feedback"
|
||||
|
||||
def init(self):
|
||||
"""Initialize nbgrader environment."""
|
||||
console.print("🔧 Initializing NBGrader environment...")
|
||||
|
||||
# Check if nbgrader is installed
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["nbgrader", "--version"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
check=True
|
||||
)
|
||||
console.print(f"✅ NBGrader version: {result.stdout.strip()}")
|
||||
except (subprocess.CalledProcessError, FileNotFoundError):
|
||||
console.print("❌ NBGrader not found. Please install with: pip install nbgrader")
|
||||
return False
|
||||
|
||||
# Create directory structure
|
||||
directories = [
|
||||
self.assignments_dir,
|
||||
self.source_dir,
|
||||
self.release_dir,
|
||||
self.submitted_dir,
|
||||
self.autograded_dir,
|
||||
self.feedback_dir
|
||||
]
|
||||
|
||||
for directory in directories:
|
||||
directory.mkdir(parents=True, exist_ok=True)
|
||||
console.print(f"📁 Created directory: {directory}")
|
||||
|
||||
# Check if nbgrader_config.py exists
|
||||
config_file = Path("nbgrader_config.py")
|
||||
if config_file.exists():
|
||||
console.print(f"✅ Found nbgrader config: {config_file}")
|
||||
else:
|
||||
console.print("⚠️ NBGrader config not found. Please create nbgrader_config.py")
|
||||
return False
|
||||
|
||||
# Initialize nbgrader database
|
||||
try:
|
||||
subprocess.run(
|
||||
["nbgrader", "db", "upgrade"],
|
||||
check=True,
|
||||
capture_output=True
|
||||
)
|
||||
console.print("✅ NBGrader database initialized")
|
||||
except subprocess.CalledProcessError as e:
|
||||
console.print(f"❌ Failed to initialize database: {e}")
|
||||
return False
|
||||
|
||||
console.print("🎉 NBGrader environment initialized successfully!")
|
||||
return True
|
||||
|
||||
def generate(self, module: Optional[str] = None, all_modules: bool = False):
|
||||
"""Generate nbgrader assignment from TinyTorch module."""
|
||||
if not module and not all_modules:
|
||||
console.print("❌ Must specify either --module or --all")
|
||||
return False
|
||||
|
||||
modules_to_process = []
|
||||
|
||||
if all_modules:
|
||||
# Find all modules
|
||||
modules_dir = Path("modules")
|
||||
if not modules_dir.exists():
|
||||
console.print("❌ Modules directory not found")
|
||||
return False
|
||||
|
||||
modules_to_process = [
|
||||
d.name for d in modules_dir.iterdir()
|
||||
if d.is_dir() and not d.name.startswith(".")
|
||||
]
|
||||
else:
|
||||
modules_to_process = [module]
|
||||
|
||||
console.print(f"🔄 Generating assignments for modules: {modules_to_process}")
|
||||
|
||||
for module_name in modules_to_process:
|
||||
success = self._generate_single_module(module_name)
|
||||
if not success:
|
||||
console.print(f"❌ Failed to generate assignment for {module_name}")
|
||||
return False
|
||||
|
||||
console.print("✅ All assignments generated successfully!")
|
||||
return True
|
||||
|
||||
def _generate_single_module(self, module_name: str) -> bool:
|
||||
"""Generate assignment from a single module."""
|
||||
console.print(f"📝 Generating assignment for module: {module_name}")
|
||||
|
||||
# Find the module development file
|
||||
module_dir = Path("modules") / module_name
|
||||
dev_file = module_dir / f"{module_name}_dev_enhanced.py"
|
||||
|
||||
if not dev_file.exists():
|
||||
# Try regular dev file
|
||||
dev_file = module_dir / f"{module_name}_dev.py"
|
||||
|
||||
if not dev_file.exists():
|
||||
console.print(f"❌ Module file not found: {dev_file}")
|
||||
return False
|
||||
|
||||
# Convert to notebook using enhanced generator
|
||||
try:
|
||||
from ...bin.generate_student_notebooks import NotebookGenerator
|
||||
|
||||
# Generate nbgrader version
|
||||
generator = NotebookGenerator(use_nbgrader=True)
|
||||
|
||||
# First convert .py to .ipynb
|
||||
console.print(f"🔄 Converting {dev_file} to notebook...")
|
||||
notebook_file = module_dir / f"{module_name}_dev.ipynb"
|
||||
|
||||
# Use jupytext to convert
|
||||
subprocess.run([
|
||||
"jupytext", "--to", "ipynb", str(dev_file)
|
||||
], check=True)
|
||||
|
||||
# Process with nbgrader generator
|
||||
notebook = generator.process_notebook(notebook_file)
|
||||
|
||||
# Save to assignments/source
|
||||
assignment_dir = self.source_dir / module_name
|
||||
assignment_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
assignment_file = assignment_dir / f"{module_name}.ipynb"
|
||||
generator.save_student_notebook(notebook, assignment_file)
|
||||
|
||||
console.print(f"✅ Assignment created: {assignment_file}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
console.print(f"❌ Error generating assignment: {e}")
|
||||
return False
|
||||
|
||||
def validate(self, assignment: str):
|
||||
"""Validate an assignment."""
|
||||
console.print(f"🔍 Validating assignment: {assignment}")
|
||||
|
||||
try:
|
||||
subprocess.run([
|
||||
"nbgrader", "validate", assignment
|
||||
], check=True)
|
||||
console.print(f"✅ Assignment {assignment} is valid")
|
||||
return True
|
||||
except subprocess.CalledProcessError:
|
||||
console.print(f"❌ Assignment {assignment} validation failed")
|
||||
return False
|
||||
|
||||
def release(self, assignment: str):
|
||||
"""Release assignment to students."""
|
||||
console.print(f"🚀 Releasing assignment: {assignment}")
|
||||
|
||||
try:
|
||||
subprocess.run([
|
||||
"nbgrader", "generate_assignment", assignment
|
||||
], check=True)
|
||||
console.print(f"✅ Assignment {assignment} released")
|
||||
return True
|
||||
except subprocess.CalledProcessError:
|
||||
console.print(f"❌ Failed to release assignment {assignment}")
|
||||
return False
|
||||
|
||||
def collect(self, assignment: str):
|
||||
"""Collect student submissions."""
|
||||
console.print(f"📥 Collecting submissions for: {assignment}")
|
||||
|
||||
try:
|
||||
subprocess.run([
|
||||
"nbgrader", "collect", assignment
|
||||
], check=True)
|
||||
console.print(f"✅ Submissions collected for {assignment}")
|
||||
return True
|
||||
except subprocess.CalledProcessError:
|
||||
console.print(f"❌ Failed to collect submissions for {assignment}")
|
||||
return False
|
||||
|
||||
def autograde(self, assignment: str):
|
||||
"""Auto-grade submissions."""
|
||||
console.print(f"🎯 Auto-grading assignment: {assignment}")
|
||||
|
||||
try:
|
||||
subprocess.run([
|
||||
"nbgrader", "autograde", assignment
|
||||
], check=True)
|
||||
console.print(f"✅ Assignment {assignment} auto-graded")
|
||||
return True
|
||||
except subprocess.CalledProcessError:
|
||||
console.print(f"❌ Failed to auto-grade {assignment}")
|
||||
return False
|
||||
|
||||
def feedback(self, assignment: str):
|
||||
"""Generate feedback for students."""
|
||||
console.print(f"📋 Generating feedback for: {assignment}")
|
||||
|
||||
try:
|
||||
subprocess.run([
|
||||
"nbgrader", "generate_feedback", assignment
|
||||
], check=True)
|
||||
console.print(f"✅ Feedback generated for {assignment}")
|
||||
return True
|
||||
except subprocess.CalledProcessError:
|
||||
console.print(f"❌ Failed to generate feedback for {assignment}")
|
||||
return False
|
||||
|
||||
def status(self):
|
||||
"""Show status of all assignments."""
|
||||
console.print("📊 Assignment Status:")
|
||||
|
||||
# List source assignments
|
||||
if self.source_dir.exists():
|
||||
source_assignments = list(self.source_dir.iterdir())
|
||||
console.print(f"📚 Source assignments: {len(source_assignments)}")
|
||||
for assignment in source_assignments:
|
||||
if assignment.is_dir():
|
||||
console.print(f" - {assignment.name}")
|
||||
|
||||
# List released assignments
|
||||
if self.release_dir.exists():
|
||||
released_assignments = list(self.release_dir.iterdir())
|
||||
console.print(f"🚀 Released assignments: {len(released_assignments)}")
|
||||
for assignment in released_assignments:
|
||||
if assignment.is_dir():
|
||||
console.print(f" - {assignment.name}")
|
||||
|
||||
# List submitted assignments
|
||||
if self.submitted_dir.exists():
|
||||
submitted_assignments = list(self.submitted_dir.iterdir())
|
||||
console.print(f"📥 Submitted assignments: {len(submitted_assignments)}")
|
||||
for assignment in submitted_assignments:
|
||||
if assignment.is_dir():
|
||||
console.print(f" - {assignment.name}")
|
||||
|
||||
# List graded assignments
|
||||
if self.autograded_dir.exists():
|
||||
graded_assignments = list(self.autograded_dir.iterdir())
|
||||
console.print(f"🎯 Graded assignments: {len(graded_assignments)}")
|
||||
for assignment in graded_assignments:
|
||||
if assignment.is_dir():
|
||||
console.print(f" - {assignment.name}")
|
||||
|
||||
def batch_release(self):
|
||||
"""Release all pending assignments."""
|
||||
console.print("🚀 Batch releasing all assignments...")
|
||||
|
||||
if not self.source_dir.exists():
|
||||
console.print("❌ No source assignments found")
|
||||
return False
|
||||
|
||||
assignments = [d.name for d in self.source_dir.iterdir() if d.is_dir()]
|
||||
|
||||
for assignment in assignments:
|
||||
console.print(f"🔄 Releasing {assignment}...")
|
||||
if not self.release(assignment):
|
||||
console.print(f"❌ Failed to release {assignment}")
|
||||
return False
|
||||
|
||||
console.print("✅ All assignments released successfully!")
|
||||
return True
|
||||
|
||||
def batch_collect(self):
|
||||
"""Collect all submitted assignments."""
|
||||
console.print("📥 Batch collecting all submissions...")
|
||||
|
||||
if not self.release_dir.exists():
|
||||
console.print("❌ No released assignments found")
|
||||
return False
|
||||
|
||||
assignments = [d.name for d in self.release_dir.iterdir() if d.is_dir()]
|
||||
|
||||
for assignment in assignments:
|
||||
console.print(f"🔄 Collecting {assignment}...")
|
||||
if not self.collect(assignment):
|
||||
console.print(f"❌ Failed to collect {assignment}")
|
||||
return False
|
||||
|
||||
console.print("✅ All submissions collected successfully!")
|
||||
return True
|
||||
|
||||
def batch_autograde(self):
|
||||
"""Auto-grade all submitted assignments."""
|
||||
console.print("🎯 Batch auto-grading all submissions...")
|
||||
|
||||
if not self.submitted_dir.exists():
|
||||
console.print("❌ No submitted assignments found")
|
||||
return False
|
||||
|
||||
assignments = [d.name for d in self.submitted_dir.iterdir() if d.is_dir()]
|
||||
|
||||
for assignment in assignments:
|
||||
console.print(f"🔄 Auto-grading {assignment}...")
|
||||
if not self.autograde(assignment):
|
||||
console.print(f"❌ Failed to auto-grade {assignment}")
|
||||
return False
|
||||
|
||||
console.print("✅ All assignments auto-graded successfully!")
|
||||
return True
|
||||
|
||||
def batch_feedback(self):
|
||||
"""Generate feedback for all graded assignments."""
|
||||
console.print("📋 Batch generating all feedback...")
|
||||
|
||||
if not self.autograded_dir.exists():
|
||||
console.print("❌ No graded assignments found")
|
||||
return False
|
||||
|
||||
assignments = [d.name for d in self.autograded_dir.iterdir() if d.is_dir()]
|
||||
|
||||
for assignment in assignments:
|
||||
console.print(f"🔄 Generating feedback for {assignment}...")
|
||||
if not self.feedback(assignment):
|
||||
console.print(f"❌ Failed to generate feedback for {assignment}")
|
||||
return False
|
||||
|
||||
console.print("✅ All feedback generated successfully!")
|
||||
return True
|
||||
|
||||
def analytics(self, assignment: str):
|
||||
"""Show analytics for an assignment."""
|
||||
console.print(f"📈 Analytics for assignment: {assignment}")
|
||||
|
||||
# This would integrate with nbgrader's gradebook
|
||||
# For now, show basic file counts
|
||||
|
||||
assignment_dir = self.submitted_dir / assignment
|
||||
if not assignment_dir.exists():
|
||||
console.print(f"❌ No submissions found for {assignment}")
|
||||
return False
|
||||
|
||||
submissions = list(assignment_dir.iterdir())
|
||||
console.print(f"📊 Total submissions: {len(submissions)}")
|
||||
|
||||
# Show grading status
|
||||
graded_dir = self.autograded_dir / assignment
|
||||
if graded_dir.exists():
|
||||
graded_submissions = list(graded_dir.iterdir())
|
||||
console.print(f"✅ Graded submissions: {len(graded_submissions)}")
|
||||
console.print(f"⏳ Pending submissions: {len(submissions) - len(graded_submissions)}")
|
||||
|
||||
return True
|
||||
|
||||
def report(self, format: str = "csv"):
|
||||
"""Export grades report."""
|
||||
console.print(f"📊 Generating grades report in {format} format...")
|
||||
|
||||
try:
|
||||
if format == "csv":
|
||||
subprocess.run([
|
||||
"nbgrader", "export"
|
||||
], check=True)
|
||||
console.print("✅ Grades report exported to grades.csv")
|
||||
else:
|
||||
console.print(f"❌ Unsupported format: {format}")
|
||||
return False
|
||||
|
||||
return True
|
||||
except subprocess.CalledProcessError:
|
||||
console.print("❌ Failed to generate grades report")
|
||||
return False
|
||||
Reference in New Issue
Block a user