Enhance setup assignment with two-part system configuration

- Split into personal_info() and system_info() functions (50 pts each) - personal_info(): Student's personal configuration (name, email, institution, etc.) - system_info(): System queries using sys, platform, psutil modules - Teaches students to query real system information (Python version, platform, CPU, memory) - Comprehensive comments outside SOLUTION blocks guide implementation - Proper NBGrader syntax with solution blocks and hidden tests - Both functions become part of tinytorch package after export - Creates useful personalized TinyTorch installation with system diagnostics
2026-04-28 22:27:30 -05:00 · 2025-07-12 14:48:54 -04:00
parent 8d20b97869
commit 873539b552
1 changed files with 158 additions and 21 deletions
--- a/assignments/source/00_setup/setup_dev.py
+++ b/assignments/source/00_setup/setup_dev.py
@@ -15,24 +15,27 @@ Welcome to TinyTorch! This assignment configures your personal TinyTorch install
 ## 🎯 Learning Objectives
 By the end of this assignment, you will:
 - ✅ Configure your personal TinyTorch installation
+- ✅ Learn to query system information using Python
 - ✅ Learn the NBGrader workflow: implement → test → export
- ✅ Create a function that will be part of your tinytorch package
+- ✅ Create functions that will be part of your tinytorch package
 - ✅ Understand solution blocks and hidden tests

 ## 📋 Assignment Overview
-You'll implement **1 simple function** that configures your TinyTorch installation:
+You'll implement **2 functions** that configure your TinyTorch installation:

 | Problem | Points | Description |
 |---------|--------|-------------|
-| System Info Function | 100 | Return your personal TinyTorch configuration |
+| Personal Info Function | 50 | Return your personal configuration |
+| System Info Function | 50 | Query and return system information |
 | **Total** | **100** | **Complete Setup** |

 ## 🚀 The Goal
 After completing this assignment and running `tito module export 00_setup`, you'll be able to:

 ```python
-from tinytorch.core.setup import system_info
-print(system_info())
+from tinytorch.core.setup import personal_info, system_info
+print(personal_info())  # Your personal details
+print(system_info())    # System information
 ```

 And see your personalized TinyTorch configuration!
@@ -45,14 +48,22 @@ Let's configure your TinyTorch installation! 🔥
 # %%
 #| default_exp core.setup

+# %%
+#| export
+# Required imports for system information
+import sys
+import platform
+import psutil
+import os
+
 # %% [markdown]
 """
-## Problem 1: System Info Function (100 points) ⚙️
+## Problem 1: Personal Info Function (50 points) 👤

-**Goal**: Create a function that returns your personal TinyTorch system configuration.
+**Goal**: Create a function that returns your personal TinyTorch configuration.

 ### 📝 Requirements
- Function name: `system_info()`
+- Function name: `personal_info()`
 - Return a dictionary with your information
 - Use your actual details (not the example)
 - Include: developer, email, institution, system_name, version
@@ -75,28 +86,28 @@ Let's configure your TinyTorch installation! 🔥
 - Keep version as '1.0.0' for now

 ### 🎯 This Will Become Part of Your TinyTorch Package
-After export, you'll be able to call `tinytorch.system_info()` and see your personalized configuration!
+After export, you'll be able to call `tinytorch.personal_info()` and see your personalized configuration!
 """

 # %%
 # === BEGIN MARK SCHEME ===
-# Award 100 points if:
+# Award 50 points if:
 # - Function returns a dictionary
 # - All required keys are present: developer, email, institution, system_name, version
 # - All values are non-empty strings
 # - Information appears to be real (not placeholder text)
 # - Dictionary structure is correct
 # 
-# Deduct 20 points per missing/invalid field.
+# Deduct 10 points per missing/invalid field.
 # === END MARK SCHEME ===

 #| export
-def system_info():
+def personal_info():
    """
-    Return system information for this TinyTorch installation.
+    Return personal information for this TinyTorch installation.
    
    Returns:
-        dict: System configuration with developer info, email, institution, 
+        dict: Personal configuration with developer info, email, institution, 
              system name, and version
    """
    ### BEGIN SOLUTION
@@ -111,11 +122,11 @@ def system_info():

 # %%
 ### BEGIN HIDDEN TESTS
-# Test system_info function
-info = system_info()
+# Test personal_info function
+info = personal_info()

 # Test return type
-assert isinstance(info, dict), "system_info should return a dictionary"
+assert isinstance(info, dict), "personal_info should return a dictionary"

 # Test required keys
 required_keys = ['developer', 'email', 'institution', 'system_name', 'version']
@@ -137,11 +148,133 @@ assert info['version'] == '1.0.0', "Version should be '1.0.0'"
 # Test system name (should be unique/personalized)
 assert len(info['system_name']) > 5, "System name should be descriptive"

-print("✅ System info function tests passed!")
+print("✅ Personal info function tests passed!")
 print(f"✅ TinyTorch configured for: {info['developer']}")
 print(f"✅ System: {info['system_name']}")
 ### END HIDDEN TESTS

+# %% [markdown]
+"""
+## Problem 2: System Info Function (50 points) 🖥️
+
+**Goal**: Create a function that queries and returns useful system information.
+
+### 📝 Requirements
+- Function name: `system_info()`
+- Query system information using Python modules
+- Return a dictionary with system details
+- Include: python_version, platform, memory_gb, cpu_count, architecture
+
+### 💡 System Information to Query
+You'll need to use these Python modules to get system information:
+- `sys.version_info` - Get Python version
+- `platform.system()` - Get operating system (Windows, Darwin, Linux)
+- `platform.machine()` - Get CPU architecture (x86_64, arm64, etc.)
+- `psutil.cpu_count()` - Get number of CPU cores
+- `psutil.virtual_memory().total` - Get total RAM in bytes (convert to GB)
+
+### 💡 Example Output
+```python
+{
+    'python_version': '3.9.7',
+    'platform': 'Darwin',
+    'architecture': 'arm64',
+    'cpu_count': 8,
+    'memory_gb': 16.0
+}
+```
+
+### 🚨 Important
+- Use the imported modules to query real system information
+- Convert memory from bytes to GB (divide by 1024^3)
+- Handle any potential errors gracefully
+- Make sure all values are the correct data types
+
+### 🎯 Learning System Queries
+This teaches you how to gather system information that's useful for ML development!
+"""
+
+# %%
+# === BEGIN MARK SCHEME ===
+# Award 50 points if:
+# - Function returns a dictionary with system information
+# - All required keys are present: python_version, platform, architecture, cpu_count, memory_gb
+# - Values are queried from actual system (not hardcoded)
+# - Memory is converted to GB correctly
+# - Data types are correct (strings for text, int for cpu_count, float for memory_gb)
+# 
+# Deduct 10 points per missing/incorrect field.
+# === END MARK SCHEME ===
+
+#| export
+def system_info():
+    """
+    Query and return system information for this TinyTorch installation.
+    
+    Returns:
+        dict: System information including Python version, platform, 
+              architecture, CPU count, and memory
+    """
+    ### BEGIN SOLUTION
+    # Get Python version
+    version_info = sys.version_info
+    python_version = f"{version_info.major}.{version_info.minor}.{version_info.micro}"
+    
+    # Get platform information
+    platform_name = platform.system()
+    architecture = platform.machine()
+    
+    # Get CPU information
+    cpu_count = psutil.cpu_count()
+    
+    # Get memory information (convert bytes to GB)
+    memory_bytes = psutil.virtual_memory().total
+    memory_gb = round(memory_bytes / (1024**3), 1)
+    
+    return {
+        'python_version': python_version,
+        'platform': platform_name,
+        'architecture': architecture,
+        'cpu_count': cpu_count,
+        'memory_gb': memory_gb
+    }
+    ### END SOLUTION
+
+# %%
+### BEGIN HIDDEN TESTS
+# Test system_info function
+sys_info = system_info()
+
+# Test return type
+assert isinstance(sys_info, dict), "system_info should return a dictionary"
+
+# Test required keys
+required_keys = ['python_version', 'platform', 'architecture', 'cpu_count', 'memory_gb']
+for key in required_keys:
+    assert key in sys_info, f"Dictionary should have '{key}' key"
+
+# Test data types
+assert isinstance(sys_info['python_version'], str), "python_version should be string"
+assert isinstance(sys_info['platform'], str), "platform should be string"
+assert isinstance(sys_info['architecture'], str), "architecture should be string"
+assert isinstance(sys_info['cpu_count'], int), "cpu_count should be integer"
+assert isinstance(sys_info['memory_gb'], (int, float)), "memory_gb should be number"
+
+# Test reasonable values
+assert sys_info['cpu_count'] > 0, "CPU count should be positive"
+assert sys_info['memory_gb'] > 0, "Memory should be positive"
+assert len(sys_info['python_version']) > 0, "Python version should not be empty"
+
+# Test that values are actually queried (not hardcoded)
+# These should match the actual system
+actual_version = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
+assert sys_info['python_version'] == actual_version, "Python version should match actual system"
+
+print("✅ System info function tests passed!")
+print(f"✅ Python: {sys_info['python_version']} on {sys_info['platform']}")
+print(f"✅ Hardware: {sys_info['cpu_count']} cores, {sys_info['memory_gb']} GB RAM")
+### END HIDDEN TESTS
+
 # %% [markdown]
 """
 ## 🎉 Setup Complete!
@@ -149,22 +282,26 @@ print(f"✅ System: {info['system_name']}")
 Congratulations! You've successfully configured your personal TinyTorch installation.

 ### What You've Accomplished:
-✅ **Created your system_info function** - This will be part of your tinytorch package  
+✅ **Created personal_info function** - Your personal TinyTorch configuration  
+✅ **Created system_info function** - System information queries  
 ✅ **Learned NBGrader workflow** - Solution blocks and hidden tests  
+✅ **Learned system queries** - How to gather useful system information  
 ✅ **Configured your personal installation** - Your TinyTorch is now personalized  

 ### Next Steps:
 1. **Export your code**: `tito module export 00_setup`
 2. **Test your installation**: 
   ```python
-   from tinytorch.core.setup import system_info
-   print(system_info())
+   from tinytorch.core.setup import personal_info, system_info
+   print(personal_info())  # Your personal details
+   print(system_info())    # System information
   ```
 3. **Move to Assignment 1**: Start building your first tensors!

 ### Key Takeaways:
 - **NBGrader workflow** - Write solutions between BEGIN/END SOLUTION markers
 - **Hidden tests** - Tests that verify your implementation automatically
+- **System queries** - How to gather useful system information for ML development
 - **Code export** - Your functions become part of the tinytorch package
 - **Personalization** - Your TinyTorch installation is now uniquely yours