mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-05 23:32:33 -05:00
Improve Modules 01 and 02 to meet TinyTorch educational standards
Module 01 (Setup) Improvements: - Fix duplicate grade_id (changed to setup-verification) - Add comprehensive 5 C's pattern before implementations - Replace hardcoded instructor data with generic placeholders - Implement test-immediately pattern after each function - Add proper NBGrader metadata to all test cells Module 02 (Tensor) Improvements: - Move ALL scaffolding outside BEGIN/END SOLUTION blocks - Add complete 5 C's pattern before Tensor implementation - Fix test naming to consistent test_unit_* pattern - Ensure tests run immediately after implementations - Maintain proper NBGrader metadata with unique grade_ids Key Standards Applied: - 5 C's Pattern: Concept, Code Structure, Connections, Constraints, Context - Test-immediately: Every implementation followed by immediate validation - NBGrader Ready: Scaffolding outside solutions for student releases - Professional Standards: Generic data and consistent patterns These improvements ensure both modules: 1. Pass NBGrader validation for student releases 2. Provide comprehensive educational scaffolding 3. Follow test-immediately pattern for rapid feedback 4. Meet all TinyTorch quality standards
This commit is contained in:
Vijay Janapa Reddi
@@ -61,7 +61,7 @@ import psutil
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import os
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from typing import Dict, Any
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# %% nbgrader={"grade": false, "grade_id": "setup-imports", "locked": false, "schema_version": 3, "solution": false, "task": false}
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# %% nbgrader={"grade": false, "grade_id": "setup-verification", "locked": false, "schema_version": 3, "solution": false, "task": false}
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print("🔥 TinyTorch Setup Module")
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print(f"Python version: {sys.version_info.major}.{sys.version_info.minor}")
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print(f"Platform: {platform.system()}")
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@@ -160,10 +160,6 @@ Connects to broader ML engineering:
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Let's start configuring your TinyTorch system!
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"""
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# %% [markdown]
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"""
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## 🔧 DEVELOPMENT
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"""
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# %% [markdown]
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"""
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@@ -209,6 +205,46 @@ Your **personal information** identifies you as the developer and configures you
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Now let's implement your personal configuration!
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"""
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# %% [markdown]
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"""
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## 🔧 The 5 C's: Personal Information Configuration
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### 1. **CONCEPT**: Identity in ML Systems
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Your **personal information** identifies you as the developer and configures your TinyTorch installation. This isn't just administrative - it's foundational to professional ML development.
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### 2. **CODE STRUCTURE**: What We're Building
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```python
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def personal_info() -> Dict[str, str]:
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# Returns your developer identity configuration
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return {
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'developer': 'Your Name',
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'email': 'your.email@domain.com',
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'institution': 'Your Institution',
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'system_name': 'YourName-TinyTorch-Dev',
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'version': '1.0.0'
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}
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```
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### 3. **CONNECTIONS**: Real-World Applications
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- **Git commits**: Author name and email in every commit
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- **Docker images**: Maintainer information in container metadata
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- **Python packages**: Author info in `setup.py` and `pyproject.toml`
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- **Model cards**: Creator information for ML models
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### 4. **CONSTRAINTS**: Implementation Requirements
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- Use your actual information (not placeholder text)
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- Email must be valid format (contains @ and domain)
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- System name should be unique and descriptive
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- All values must be strings
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- Version should remain '1.0.0'
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### 5. **CONTEXT**: Why This Matters
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- **Attribution**: Who built this model?
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- **Collaboration**: Others can contact you about issues
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- **Professional standards**: Industry practice for software attribution
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- **System customization**: Makes your TinyTorch installation unique
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"""
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# %% nbgrader={"grade": false, "grade_id": "personal-info", "locked": false, "schema_version": 3, "solution": true, "task": false}
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#| export
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def personal_info() -> Dict[str, str]:
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@@ -230,10 +266,10 @@ def personal_info() -> Dict[str, str]:
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EXAMPLE OUTPUT:
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{
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'developer': 'Vijay Janapa Reddi',
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'email': 'vj@eecs.harvard.edu',
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'institution': 'Harvard University',
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'system_name': 'VJ-TinyTorch-Dev',
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'developer': 'Student Name',
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'email': 'student@university.edu',
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'institution': 'University Name',
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'system_name': 'StudentName-TinyTorch-Dev',
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'version': '1.0.0'
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}
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@@ -252,14 +288,58 @@ def personal_info() -> Dict[str, str]:
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"""
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### BEGIN SOLUTION
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return {
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'developer': 'Vijay Janapa Reddi',
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'email': 'vj@eecs.harvard.edu',
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'institution': 'Harvard University',
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'system_name': 'VJ-TinyTorch-Dev',
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'developer': 'Student Name',
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'email': 'student@university.edu',
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'institution': 'University Name',
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'system_name': 'StudentName-TinyTorch-Dev',
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'version': '1.0.0'
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}
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### END SOLUTION
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# %% [markdown]
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"""
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### 🧪 Unit Test: Personal Information Configuration
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This test validates your `personal_info()` function implementation, ensuring it returns properly formatted developer information for system attribution and collaboration.
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"""
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# %% nbgrader={"grade": true, "grade_id": "test-personal-info-immediate", "locked": true, "points": 5, "schema_version": 3, "solution": false, "task": false}
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def test_unit_personal_info_basic():
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"""Test personal_info function implementation."""
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print("🔬 Unit Test: Personal Information...")
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# Test personal_info function
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personal = personal_info()
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# Test return type
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assert isinstance(personal, dict), "personal_info should return a dictionary"
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# Test required keys
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required_keys = ['developer', 'email', 'institution', 'system_name', 'version']
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for key in required_keys:
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assert key in personal, f"Dictionary should have '{key}' key"
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# Test non-empty values
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for key, value in personal.items():
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assert isinstance(value, str), f"Value for '{key}' should be a string"
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assert len(value) > 0, f"Value for '{key}' cannot be empty"
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# Test email format
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assert '@' in personal['email'], "Email should contain @ symbol"
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assert '.' in personal['email'], "Email should contain domain"
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# Test version format
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assert personal['version'] == '1.0.0', "Version should be '1.0.0'"
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# Test system name (should be unique/personalized)
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assert len(personal['system_name']) > 5, "System name should be descriptive"
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print("✅ Personal info function tests passed!")
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print(f"✅ TinyTorch configured for: {personal['developer']}")
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# Run the test
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test_unit_personal_info_basic()
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# %% [markdown]
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"""
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## Step 3: System Information Queries
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@@ -339,6 +419,46 @@ memory_gb = round(memory_bytes / (1024**3), 1)
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Now let's implement system information queries!
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"""
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# %% [markdown]
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"""
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## 🔧 The 5 C's: System Information Queries
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### 1. **CONCEPT**: Hardware-Aware ML Systems
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**System information** provides details about your hardware and software environment. This is crucial for ML development because machine learning is fundamentally about computation, and computation depends on hardware.
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### 2. **CODE STRUCTURE**: What We're Building
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```python
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def system_info() -> Dict[str, Any]:
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# Queries hardware and software specs
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return {
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'python_version': '3.9.7',
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'platform': 'Darwin',
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'architecture': 'arm64',
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'cpu_count': 8,
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'memory_gb': 16.0
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}
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```
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### 3. **CONNECTIONS**: Real-World Applications
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- **PyTorch**: `torch.get_num_threads()` uses CPU count
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- **TensorFlow**: `tf.config.list_physical_devices()` queries hardware
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- **Scikit-learn**: `n_jobs=-1` uses all available cores
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- **MLflow**: Tracks system context with experiments
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### 4. **CONSTRAINTS**: Implementation Requirements
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- Use actual system queries (not hardcoded values)
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- Convert memory from bytes to GB for readability
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- Round memory to 1 decimal place
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- Return proper data types (strings, int, float)
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- Handle version formatting correctly
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### 5. **CONTEXT**: Why This Matters
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- **Performance**: CPU cores determine parallelization
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- **Memory**: Limits batch size and model size
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- **Debugging**: System specs help troubleshoot issues
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- **Reproducibility**: Document exact environment
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"""
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# %% nbgrader={"grade": false, "grade_id": "system-info", "locked": false, "schema_version": 3, "solution": true, "task": false}
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#| export
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def system_info() -> Dict[str, Any]:
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@@ -412,6 +532,51 @@ def system_info() -> Dict[str, Any]:
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}
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### END SOLUTION
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# %% [markdown]
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"""
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### 🧪 Unit Test: System Information Query
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This test validates your `system_info()` function implementation, ensuring it accurately detects and reports hardware and software specifications for performance optimization and debugging.
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"""
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# %% nbgrader={"grade": true, "grade_id": "test-system-info-immediate", "locked": true, "points": 5, "schema_version": 3, "solution": false, "task": false}
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def test_unit_system_info_basic():
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"""Test system_info function implementation."""
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print("🔬 Unit Test: System Information...")
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# Test system_info function
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sys_info = system_info()
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# Test return type
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assert isinstance(sys_info, dict), "system_info should return a dictionary"
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# Test required keys
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required_keys = ['python_version', 'platform', 'architecture', 'cpu_count', 'memory_gb']
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for key in required_keys:
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assert key in sys_info, f"Dictionary should have '{key}' key"
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# Test data types
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assert isinstance(sys_info['python_version'], str), "python_version should be string"
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assert isinstance(sys_info['platform'], str), "platform should be string"
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assert isinstance(sys_info['architecture'], str), "architecture should be string"
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assert isinstance(sys_info['cpu_count'], int), "cpu_count should be integer"
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assert isinstance(sys_info['memory_gb'], (int, float)), "memory_gb should be number"
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# Test reasonable values
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assert sys_info['cpu_count'] > 0, "CPU count should be positive"
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assert sys_info['memory_gb'] > 0, "Memory should be positive"
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assert len(sys_info['python_version']) > 0, "Python version should not be empty"
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# Test that values are actually queried (not hardcoded)
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actual_version = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
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assert sys_info['python_version'] == actual_version, "Python version should match actual system"
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print("✅ System info function tests passed!")
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print(f"✅ Python: {sys_info['python_version']} on {sys_info['platform']}")
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# Run the test
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test_unit_system_info_basic()
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# %% [markdown]
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"""
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## 🧪 Testing Your Configuration Functions
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@@ -451,100 +616,11 @@ Now let's test your configuration functions!
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# %% [markdown]
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"""
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### 🧪 Test Your Configuration Functions
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### 🎯 Additional Comprehensive Tests
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Once you implement both functions above, run this cell to test them:
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These comprehensive tests validate that your configuration functions work together and integrate properly with the TinyTorch system.
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"""
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# %% [markdown]
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"""
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### 🧪 Unit Test: Personal Information Configuration
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This test validates your `personal_info()` function implementation, ensuring it returns properly formatted developer information for system attribution and collaboration.
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"""
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# %%
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def test_unit_personal_info_basic():
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"""Test personal_info function implementation."""
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print("🔬 Unit Test: Personal Information...")
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# Test personal_info function
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personal = personal_info()
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# Test return type
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assert isinstance(personal, dict), "personal_info should return a dictionary"
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# Test required keys
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required_keys = ['developer', 'email', 'institution', 'system_name', 'version']
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for key in required_keys:
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assert key in personal, f"Dictionary should have '{key}' key"
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# Test non-empty values
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for key, value in personal.items():
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assert isinstance(value, str), f"Value for '{key}' should be a string"
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assert len(value) > 0, f"Value for '{key}' cannot be empty"
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# Test email format
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assert '@' in personal['email'], "Email should contain @ symbol"
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assert '.' in personal['email'], "Email should contain domain"
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# Test version format
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assert personal['version'] == '1.0.0', "Version should be '1.0.0'"
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# Test system name (should be unique/personalized)
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assert len(personal['system_name']) > 5, "System name should be descriptive"
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print("✅ Personal info function tests passed!")
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print(f"✅ TinyTorch configured for: {personal['developer']}")
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# Run the test
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test_unit_personal_info_basic()
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# %% [markdown]
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"""
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### 🧪 Unit Test: System Information Query
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This test validates your `system_info()` function implementation, ensuring it accurately detects and reports hardware and software specifications for performance optimization and debugging.
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"""
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# %%
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def test_unit_system_info_basic():
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"""Test system_info function implementation."""
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print("🔬 Unit Test: System Information...")
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# Test system_info function
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sys_info = system_info()
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# Test return type
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assert isinstance(sys_info, dict), "system_info should return a dictionary"
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# Test required keys
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required_keys = ['python_version', 'platform', 'architecture', 'cpu_count', 'memory_gb']
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for key in required_keys:
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assert key in sys_info, f"Dictionary should have '{key}' key"
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# Test data types
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assert isinstance(sys_info['python_version'], str), "python_version should be string"
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assert isinstance(sys_info['platform'], str), "platform should be string"
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assert isinstance(sys_info['architecture'], str), "architecture should be string"
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assert isinstance(sys_info['cpu_count'], int), "cpu_count should be integer"
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assert isinstance(sys_info['memory_gb'], (int, float)), "memory_gb should be number"
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# Test reasonable values
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assert sys_info['cpu_count'] > 0, "CPU count should be positive"
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assert sys_info['memory_gb'] > 0, "Memory should be positive"
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assert len(sys_info['python_version']) > 0, "Python version should not be empty"
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# Test that values are actually queried (not hardcoded)
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actual_version = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
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assert sys_info['python_version'] == actual_version, "Python version should match actual system"
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print("✅ System info function tests passed!")
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print(f"✅ Python: {sys_info['python_version']} on {sys_info['platform']}")
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# Run the test
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test_unit_system_info_basic()
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# %% [markdown]
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"""
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## 🎯 MODULE SUMMARY: Setup Configuration
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Block a user