Add enumitem package to fix itemize formatting

The itemize environment parameters [leftmargin=*, itemsep=1pt, parsep=0pt]
were appearing as visible text in the PDF because the enumitem package
wasn't loaded. This fix adds \usepackage{enumitem} to the preamble.

All itemized lists now format correctly with proper spacing and margins.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

.pre-commit-config.yaml

@@ -4,3 +4,4 @@ repos: []
 
 
 
+

README.md

@@ -76,9 +76,9 @@ TinyTorch/
 │   ├── 12_attention/     # Module 12: Multi-head attention
 │   ├── 13_transformers/  # Module 13: Complete transformer blocks
 │   ├── 14_profiling/     # Module 14: Performance analysis
-│   ├── 15_memoization/   # Module 15: KV-cache/memoization
-│   ├── 16_quantization/  # Module 16: Model compression
-│   ├── 17_compression/   # Module 17: Pruning & distillation
+│   ├── 15_quantization/  # Module 15: Model compression (precision reduction)
+│   ├── 16_compression/   # Module 16: Pruning & distillation
+│   ├── 17_memoization/   # Module 17: KV-cache/memoization
 │   ├── 18_acceleration/  # Module 18: Hardware optimization
 │   ├── 19_benchmarking/  # Module 19: Performance measurement
 │   └── 20_capstone/      # Module 20: Complete ML systems
@@ -147,10 +147,16 @@ tito module view 01_tensor
 
 **That's it!** The setup script handles:
 - ✅ Virtual environment creation (arm64 on Apple Silicon)
-- ✅ All dependencies (NumPy, Jupyter, Rich, etc.)
+- ✅ All required dependencies (NumPy, Rich, PyYAML, pytest, jupytext)
 - ✅ TinyTorch package installation in development mode
 - ✅ Architecture detection and optimization
 
+**Optional Dependencies** (for visualization and advanced features):
+- `matplotlib` - For plotting in Modules 17, 19, 20 (optional)
+- `jupyter` - For interactive development (optional)
+
+**Note**: Memory profiling uses Python's built-in `tracemalloc` module (standard library). System information uses `os.cpu_count()` and `platform` module (standard library). No external system monitoring dependencies required!
+
 ## Learning Journey
 
 ### 20 Progressive Modules
@@ -204,10 +210,11 @@ Profile, optimize, and benchmark ML systems
 
 | Module | Topic | What You Build | ML Systems Learning |
 |--------|-------|----------------|-------------------|
-| 15 | Memoization | Computational reuse via KV-caching | **Memory vs compute trade-offs**, cache management, generation efficiency |
-| 16 | Acceleration | Hardware optimization + cache-friendly algorithms | **Cache hierarchies**, memory access patterns, **vectorization vs loops** |
-| 17 | Quantization | Model compression + precision reduction | **Precision trade-offs** (FP32→INT8), memory reduction, accuracy preservation |
-| 18 | Compression | Pruning + knowledge distillation | **Sparsity patterns**, parameter reduction, **compression ratios** |
+| 14 | Profiling | Performance analysis + bottleneck detection | **Memory profiling**, FLOP counting, **Amdahl's Law**, performance measurement |
+| 15 | Quantization | Model compression + precision reduction | **Precision trade-offs** (FP32→INT8), memory reduction, accuracy preservation |
+| 16 | Compression | Pruning + knowledge distillation | **Sparsity patterns**, parameter reduction, **compression ratios** |
+| 17 | Memoization | Computational reuse via KV-caching | **Memory vs compute trade-offs**, cache management, generation efficiency |
+| 18 | Acceleration | Hardware optimization + cache-friendly algorithms | **Cache hierarchies**, memory access patterns, **vectorization vs loops** |
 | 19 | Benchmarking | Performance measurement + TinyMLPerf competition | **Competitive optimization**, relative performance metrics, innovation scoring |
 | 20 | Capstone | Complete end-to-end ML systems project | **Integration**, production deployment, **real-world ML engineering** |
 

docs/development/MODULE_ABOUT_TEMPLATE.md

@@ -203,3 +203,4 @@ All modules MUST include:
 
 
 
+

docs/development/comprehensive-module-testing-plan.md

@@ -702,3 +702,4 @@ This testing plan helps you:
 
 
 
+

docs/development/gradient-flow-testing-strategy.md

@@ -338,3 +338,4 @@ Gradient flow tests teach students:
 
 
 
+

docs/development/testing-architecture.md

@@ -422,3 +422,4 @@ pytest tests/integration/test_gradient_flow.py -v
 
 
 
+

milestones/MILESTONE_TEMPLATE_V2.py

@@ -253,3 +253,4 @@ if __name__ == "__main__":
 
 
 
+

modules/01_tensor/tensor_dev.py

@@ -1476,7 +1476,6 @@ Every layer in a neural network - from simple MLPs to complex transformers - use
 # def import_previous_module(module_name: str, component_name: str):
 #     import sys
 #     import os
-#     sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))
 #     module = __import__(f"{module_name.split('_')[1]}_dev")
 #     return getattr(module, component_name)
 

modules/02_activations/activations_dev.py

@@ -68,8 +68,6 @@ This module builds on previous TinyTorch components. Here's what we need and why
 **Required Components:**
 - **Tensor** (Module 01): Foundation for all activation computations and data flow
 
-**Integration Helper:**
-The `import_previous_module()` function below helps us cleanly import components from previous modules during development and testing.
 """
 
 # %% nbgrader={"grade": false, "grade_id": "setup", "solution": true}
@@ -78,11 +76,9 @@ The `import_previous_module()` function below helps us cleanly import components
 
 import numpy as np
 from typing import Optional
-import sys
-import os
 
-
-# Import will be in export cell
+# Import Tensor from Module 01 (foundation)
+from tinytorch.core.tensor import Tensor
 
 # %% [markdown]
 """
@@ -825,13 +821,6 @@ Final validation that everything works together correctly.
 """
 
 # %% nbgrader={"grade": true, "grade_id": "module-test", "locked": true, "points": 20}
-def import_previous_module(module_name: str, component_name: str):
-    import sys
-    import os
-    sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))
-    module = __import__(f"{module_name.split('_')[1]}_dev")
-    return getattr(module, component_name)
-
 def test_module():
     """
     Comprehensive test of entire module functionality.

modules/03_layers/layers_dev.py

@@ -777,8 +777,8 @@ def test_module():
     # Test realistic neural network construction with manual composition
     print("🔬 Integration Test: Multi-layer Network...")
 
-    # Import real activation from module 02 using standardized helper
-    ReLU = import_previous_module('02_activations', 'ReLU')
+    # Import ReLU from Module 02 (already imported at top of file)
+    # ReLU is available from: from tinytorch.core.activations import ReLU
 
     # Build individual layers for manual composition
     layer1 = Linear(784, 128)

modules/04_losses/losses_dev.py

@@ -69,9 +69,6 @@ This module builds on previous TinyTorch components. Here's what we need and why
 - **Tensor** (Module 01): Foundation for all loss computations
 - **Linear** (Module 03): For testing loss functions with realistic predictions  
 - **ReLU** (Module 02): For building test networks that generate realistic outputs
-
-**Integration Helper:**
-The `import_previous_module()` function below helps us cleanly import components from previous modules during development and testing.
 """
 
 # %% nbgrader={"grade": false, "grade_id": "setup", "solution": true}
@@ -81,17 +78,10 @@ The `import_previous_module()` function below helps us cleanly import components
 import numpy as np
 from typing import Optional
 
-def import_previous_module(module_name: str, component_name: str):
-    import sys
-    import os
-    sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))
-    module = __import__(f"{module_name.split('_')[1]}_dev")
-    return getattr(module, component_name)
-
-# Import from tinytorch package
-from tinytorch.core.tensor import Tensor
-from tinytorch.core.layers import Linear
-from tinytorch.core.activations import ReLU
+# Import dependencies from previous modules
+from tinytorch.core.tensor import Tensor  # Module 01: Foundation
+from tinytorch.core.layers import Linear  # Module 03: Layers
+from tinytorch.core.activations import ReLU  # Module 02: Activations
 
 # %% [markdown]
 r"""

modules/06_optimizers/optimizers_dev.py

@@ -1228,7 +1228,6 @@ def analyze_optimizer_convergence_behavior():
 # def import_previous_module(module_name: str, component_name: str):
 #     import sys
 #     import os
-#     sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))
 #     module = __import__(f"{module_name.split('_')[1]}_dev")
 #     return getattr(module, component_name)
 
@@ -1257,11 +1256,11 @@ def test_module():
     # Test realistic neural network optimization scenario
     print("🔬 Integration Test: Multi-layer Network Optimization...")
 
-    # Import components from previous modules using standardized helper
-    Tensor = import_previous_module('01_tensor', 'Tensor')
-    Linear = import_previous_module('03_layers', 'Linear')
-    ReLU = import_previous_module('02_activations', 'ReLU')
-    MSELoss = import_previous_module('04_losses', 'MSELoss')
+    # Import components from previous modules (explicit imports)
+    from tinytorch.core.tensor import Tensor  # Module 01: Foundation
+    from tinytorch.core.layers import Linear  # Module 03: Layers
+    from tinytorch.core.activations import ReLU  # Module 02: Activations
+    from tinytorch.core.losses import MSELoss  # Module 04: Losses
 
     # Create parameters for a 2-layer network
     # Layer 1: 3 inputs -> 4 hidden

modules/07_training/training_dev.py

@@ -1042,7 +1042,6 @@ Now let's create a complete training example that demonstrates how all the compo
 # def import_previous_module(module_name: str, component_name: str):
 #     import sys
 #     import os
-#     sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))
 #     module = __import__(f"{module_name.split('_')[1]}_dev")
 #     return getattr(module, component_name)
 

modules/19_benchmarking/benchmarking.py

@@ -168,37 +168,59 @@ Professional benchmarking quantifies and minimizes these uncertainties.
 
 # %%
 import numpy as np
-try:
-    import pandas as pd
-    PANDAS_AVAILABLE = True
-except ImportError:
-    PANDAS_AVAILABLE = False
-    # Create a simple DataFrame-like class for when pandas is not available
-    class pd:
-        class DataFrame:
-            def __init__(self, data):
-                self.data = data
-            def __repr__(self):
-                return str(self.data)
 import time
 import statistics
+import os
+import tracemalloc
+from typing import Dict, List, Tuple, Any, Optional, Callable, Union
+from dataclasses import dataclass, field
+from pathlib import Path
+import json
+import platform
+from contextlib import contextmanager
+import warnings
+
+# Optional dependency for visualization only
 try:
     import matplotlib.pyplot as plt
     MATPLOTLIB_AVAILABLE = True
 except ImportError:
     MATPLOTLIB_AVAILABLE = False
-from typing import Dict, List, Tuple, Any, Optional, Callable, Union
-from dataclasses import dataclass, field
-from pathlib import Path
-import json
-try:
-    import psutil
-    PSUTIL_AVAILABLE = True
-except ImportError:
-    PSUTIL_AVAILABLE = False
-import platform
-from contextlib import contextmanager
-import warnings
+    # Create minimal fallback for when matplotlib is not available
+    class plt:
+        @staticmethod
+        def subplots(*args, **kwargs):
+            return None, None
+        @staticmethod
+        def figure(*args, **kwargs):
+            return None
+        @staticmethod
+        def scatter(*args, **kwargs):
+            pass
+        @staticmethod
+        def annotate(*args, **kwargs):
+            pass
+        @staticmethod
+        def xlabel(*args, **kwargs):
+            pass
+        @staticmethod
+        def ylabel(*args, **kwargs):
+            pass
+        @staticmethod
+        def title(*args, **kwargs):
+            pass
+        @staticmethod
+        def grid(*args, **kwargs):
+            pass
+        @staticmethod
+        def tight_layout(*args, **kwargs):
+            pass
+        @staticmethod
+        def savefig(*args, **kwargs):
+            pass
+        @staticmethod
+        def show(*args, **kwargs):
+            pass
 
 # Import Profiler from Module 14 for measurement reuse
 from tinytorch.profiling.profiler import Profiler
@@ -593,14 +615,15 @@ class Benchmark:
         # Use Profiler from Module 14 for measurements
         self.profiler = Profiler()
 
-        # System information for metadata
+        # System information for metadata (using Python standard library)
         self.system_info = {
             'platform': platform.platform(),
             'processor': platform.processor(),
             'python_version': platform.python_version(),
-            'memory_gb': psutil.virtual_memory().total / (1024**3) if PSUTIL_AVAILABLE else 0.0,
-            'cpu_count': psutil.cpu_count() if PSUTIL_AVAILABLE else 1
+            'cpu_count': os.cpu_count() or 1,  # os.cpu_count() can return None
         }
+        # Note: System total memory not available via standard library
+        # Process memory measurement uses tracemalloc (via Profiler)
 
     def run_latency_benchmark(self, input_shape: Tuple[int, ...] = (1, 28, 28)) -> Dict[str, BenchmarkResult]:
         """Benchmark model inference latency using Profiler."""
@@ -717,7 +740,8 @@ class Benchmark:
                 f"  Fix: Use one of the supported metric names."
             )
 
-        # Convert to DataFrame for easy comparison
+        # Return structured list of dicts for easy comparison
+        # (No pandas dependency - students can convert to DataFrame if needed)
         comparison_data = []
         for model_name, result in results.items():
             comparison_data.append({
@@ -730,11 +754,7 @@ class Benchmark:
                 'count': result.count
             })
 
-        if PANDAS_AVAILABLE:
-            return pd.DataFrame(comparison_data)
-        else:
-            # Return dict when pandas is not available
-            return {'comparison': comparison_data, 'note': 'pandas not available, returning dict'}
+        return comparison_data
     ### END SOLUTION
 
 def test_unit_benchmark():
@@ -771,16 +791,13 @@ def test_unit_benchmark():
     assert len(memory_results) == 2
     assert all(result.mean >= 0 for result in memory_results.values())
 
-    # Test comparison
-    comparison_df = benchmark.compare_models("latency")
-    # Handle case when pandas is not available (returns dict instead of DataFrame)
-    if PANDAS_AVAILABLE:
-        assert len(comparison_df) == 2
-        assert "model" in comparison_df.columns
-        assert "mean" in comparison_df.columns
-    else:
-        assert "comparison" in comparison_df
-        assert isinstance(comparison_df, dict)
+    # Test comparison (returns list of dicts, not DataFrame)
+    comparison_data = benchmark.compare_models("latency")
+    assert len(comparison_data) == 2
+    assert isinstance(comparison_data, list)
+    assert all(isinstance(item, dict) for item in comparison_data)
+    assert "model" in comparison_data[0]
+    assert "mean" in comparison_data[0]
 
     print("✅ Benchmark works correctly!")
 
@@ -946,6 +963,10 @@ class BenchmarkSuite:
         if not self.results:
             print("No results to plot. Run benchmark first.")
             return
+        
+        if not MATPLOTLIB_AVAILABLE:
+            print("⚠️ matplotlib not available - skipping plots. Install with: pip install matplotlib")
+            return
 
         fig, axes = plt.subplots(2, 2, figsize=(15, 12))
         fig.suptitle('ML Model Benchmark Results', fontsize=16, fontweight='bold')
@@ -998,6 +1019,10 @@ class BenchmarkSuite:
 
     def plot_pareto_frontier(self, x_metric: str = 'latency', y_metric: str = 'accuracy'):
         """Plot Pareto frontier for two competing objectives."""
+        if not MATPLOTLIB_AVAILABLE:
+            print("⚠️ matplotlib not available - skipping plots. Install with: pip install matplotlib")
+            return
+            
         if x_metric not in self.results or y_metric not in self.results:
             print(f"Missing data for {x_metric} or {y_metric}")
             return

modules/19_benchmarking/benchmarking_dev.py

@@ -149,20 +149,61 @@ Professional benchmarking quantifies and minimizes these uncertainties.
 
 # %%
 import numpy as np
-import pandas as pd
 import time
 import statistics
-import matplotlib.pyplot as plt
+import os
+import tracemalloc
 from typing import Dict, List, Tuple, Any, Optional, Callable, Union
 from dataclasses import dataclass, field
 from pathlib import Path
 import json
-import psutil
 import platform
 from contextlib import contextmanager
 import warnings
 
-# Import Profiler from Module 15 for measurement reuse
+# Optional dependency for visualization only
+try:
+    import matplotlib.pyplot as plt
+    MATPLOTLIB_AVAILABLE = True
+except ImportError:
+    MATPLOTLIB_AVAILABLE = False
+    # Create minimal fallback for when matplotlib is not available
+    class plt:
+        @staticmethod
+        def subplots(*args, **kwargs):
+            return None, None
+        @staticmethod
+        def figure(*args, **kwargs):
+            return None
+        @staticmethod
+        def scatter(*args, **kwargs):
+            pass
+        @staticmethod
+        def annotate(*args, **kwargs):
+            pass
+        @staticmethod
+        def xlabel(*args, **kwargs):
+            pass
+        @staticmethod
+        def ylabel(*args, **kwargs):
+            pass
+        @staticmethod
+        def title(*args, **kwargs):
+            pass
+        @staticmethod
+        def grid(*args, **kwargs):
+            pass
+        @staticmethod
+        def tight_layout(*args, **kwargs):
+            pass
+        @staticmethod
+        def savefig(*args, **kwargs):
+            pass
+        @staticmethod
+        def show(*args, **kwargs):
+            pass
+
+# Import Profiler from Module 14 for measurement reuse
 from tinytorch.profiling.profiler import Profiler
 
 # %% [markdown]
@@ -532,14 +573,15 @@ class Benchmark:
         # Use Profiler from Module 15 for measurements
         self.profiler = Profiler()
 
-        # System information for metadata
+        # System information for metadata (using Python standard library)
         self.system_info = {
             'platform': platform.platform(),
             'processor': platform.processor(),
             'python_version': platform.python_version(),
-            'memory_gb': psutil.virtual_memory().total / (1024**3),
-            'cpu_count': psutil.cpu_count()
+            'cpu_count': os.cpu_count() or 1,  # os.cpu_count() can return None
         }
+        # Note: System total memory not available via standard library
+        # Process memory measurement uses tracemalloc (via Profiler)
 
     def run_latency_benchmark(self, input_shape: Tuple[int, ...] = (1, 28, 28)) -> Dict[str, BenchmarkResult]:
         """Benchmark model inference latency using Profiler."""
@@ -650,9 +692,9 @@ class Benchmark:
                     # Use peak_memory_mb as the primary metric
                     memory_used = memory_stats['peak_memory_mb']
                 except:
-                    # Fallback: measure with psutil
-                    process = psutil.Process()
-                    memory_before = process.memory_info().rss / (1024**2)  # MB
+                    # Fallback: use tracemalloc (Python standard library) for memory measurement
+                    tracemalloc.start()
+                    baseline_memory = tracemalloc.get_traced_memory()[0] / (1024**2)  # MB
 
                     try:
                         dummy_input = np.random.randn(*input_shape).astype(np.float32)
@@ -665,8 +707,9 @@ class Benchmark:
                     except:
                         pass
 
-                    memory_after = process.memory_info().rss / (1024**2)  # MB
-                    memory_used = max(0, memory_after - memory_before)
+                    peak_memory = tracemalloc.get_traced_memory()[1] / (1024**2)  # MB
+                    tracemalloc.stop()
+                    memory_used = max(0, peak_memory - baseline_memory)
 
                     # If no significant memory change detected, estimate from parameters
                     if memory_used < 1.0:
@@ -686,8 +729,13 @@ class Benchmark:
 
         return results
 
-    def compare_models(self, metric: str = "latency") -> pd.DataFrame:
-        """Compare models across a specific metric."""
+    def compare_models(self, metric: str = "latency") -> List[Dict[str, Any]]:
+        """
+        Compare models across a specific metric.
+        
+        Returns a list of dictionaries, one per model, with comparison metrics.
+        This keeps dependencies minimal - students can convert to DataFrame if needed.
+        """
         if metric == "latency":
             results = self.run_latency_benchmark()
         elif metric == "accuracy":
@@ -697,7 +745,8 @@ class Benchmark:
         else:
             raise ValueError(f"Unknown metric: {metric}")
 
-        # Convert to DataFrame for easy comparison
+        # Return structured list of dicts for easy comparison
+        # (No pandas dependency - students can convert to DataFrame if needed)
         comparison_data = []
         for model_name, result in results.items():
             comparison_data.append({
@@ -710,7 +759,7 @@ class Benchmark:
                 'count': result.count
             })
 
-        return pd.DataFrame(comparison_data)
+        return comparison_data
     ### END SOLUTION
 
 def test_unit_benchmark():
@@ -747,11 +796,13 @@ def test_unit_benchmark():
     assert len(memory_results) == 2
     assert all(result.mean >= 0 for result in memory_results.values())
 
-    # Test comparison
-    comparison_df = benchmark.compare_models("latency")
-    assert len(comparison_df) == 2
-    assert "model" in comparison_df.columns
-    assert "mean" in comparison_df.columns
+    # Test comparison (returns list of dicts, not DataFrame)
+    comparison_data = benchmark.compare_models("latency")
+    assert len(comparison_data) == 2
+    assert isinstance(comparison_data, list)
+    assert all(isinstance(item, dict) for item in comparison_data)
+    assert "model" in comparison_data[0]
+    assert "mean" in comparison_data[0]
 
     print("✅ Benchmark works correctly!")
 
@@ -916,6 +967,10 @@ class BenchmarkSuite:
         if not self.results:
             print("No results to plot. Run benchmark first.")
             return
+        
+        if not MATPLOTLIB_AVAILABLE:
+            print("⚠️ matplotlib not available - skipping plots. Install with: pip install matplotlib")
+            return
 
         fig, axes = plt.subplots(2, 2, figsize=(15, 12))
         fig.suptitle('ML Model Benchmark Results', fontsize=16, fontweight='bold')
@@ -968,6 +1023,10 @@ class BenchmarkSuite:
 
     def plot_pareto_frontier(self, x_metric: str = 'latency', y_metric: str = 'accuracy'):
         """Plot Pareto frontier for two competing objectives."""
+        if not MATPLOTLIB_AVAILABLE:
+            print("⚠️ matplotlib not available - skipping plots. Install with: pip install matplotlib")
+            return
+            
         if x_metric not in self.results or y_metric not in self.results:
             print(f"Missing data for {x_metric} or {y_metric}")
             return

modules/20_capstone/capstone.py

@@ -250,18 +250,21 @@ from tinytorch.profiling.profiler import Profiler
 
 # Module 17: Quantization
 from tinytorch.optimization.quantization import quantize_model
-# QuantizedLinear may not be exported - check if needed
+# QuantizedLinear is an optional advanced feature (may not be exported)
+# This is acceptable - quantize_model is the main API, QuantizedLinear is internal
 try:
     from tinytorch.optimization.quantization import QuantizedLinear
 except ImportError:
-    QuantizedLinear = None  # Not available
+    # QuantizedLinear is optional - quantize_model() is the main API
+    QuantizedLinear = None
 
 # Module 18: Compression
-# Note: These functions may need to be exported first
+# These are optional advanced features (may not be exported)
+# NOTE: These are OPTIONAL - students can use quantize_model() without them
 try:
     from tinytorch.optimization.compression import magnitude_prune, structured_prune
 except ImportError:
-    # Functions may not be exported yet - handle gracefully
+    # Compression functions are optional - quantize_model() is sufficient for competition
     magnitude_prune = None
     structured_prune = None
 

paper/paper.tex

@@ -38,6 +38,7 @@
 \usepackage{tikz}
 \usetikzlibrary{shapes,arrows,positioning}
 \usepackage{subcaption}
+\usepackage{enumitem}
 \usepackage{titlesec}
 \usepackage{fancyhdr}
 

pyproject.toml

@@ -40,6 +40,9 @@ dev = [
     "jupyter>=1.1.0",
     "jupyterlab>=4.2.0",
 ]
+visualization = [
+    "matplotlib>=3.9.0",  # For plotting in Modules 17, 19, 20
+]
 
 [project.urls]
 Homepage = "https://github.com/mlsysbook/TinyTorch"

requirements.txt

@@ -33,7 +33,7 @@ nbformat>=5.10.0
 # Optional Dependencies (Uncomment if needed)
 # ============================================================================
 
-# Visualization for milestone examples
+# Visualization for milestone examples and benchmarking (Modules 17, 19, 20)
 # matplotlib>=3.9.0
 
 # Jupyter for interactive development

tito/commands/checkpoint.py

@@ -8,7 +8,6 @@ Foundation → Architecture → Training → Inference → Serving
 import argparse
 import subprocess
 import sys
-import importlib.util
 from pathlib import Path
 from typing import Dict, List, Tuple, Optional
 from rich.console import Console

tito/commands/test.py

@@ -5,7 +5,6 @@ Enhanced Test command for TinyTorch CLI: runs both inline and external tests.
 import subprocess
 import sys
 import re
-import importlib.util
 from argparse import ArgumentParser, Namespace
 from pathlib import Path
 from typing import List, Dict, Tuple, Optional, Any