mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-05 16:37:31 -05:00
756d093920
- Implement complete capability showcase system (11 demonstrations) - Add auto-run showcases after successful module completion - Create interactive launcher for easy showcase navigation - Integrate with tito module complete workflow - Add user preference system for logo themes - Showcase student achievements without requiring additional work - Demonstrate real ML capabilities from tensors to TinyGPT - Use Rich terminal UI for beautiful visualizations
370 lines
14 KiB
Python
370 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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🚀 CAPABILITY SHOWCASE: Performance Profiling
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After Module 14 (Benchmarking)
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"Look what you built!" - Your profiler reveals system behavior!
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"""
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import sys
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import time
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import numpy as np
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from rich.console import Console
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from rich.panel import Panel
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from rich.table import Table
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from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn
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from rich.align import Align
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# Import from YOUR TinyTorch implementation
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try:
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from tinytorch.core.benchmarking import Profiler, benchmark_operation
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.dense import Sequential
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from tinytorch.core.layers import Dense
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from tinytorch.core.activations import ReLU
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except ImportError:
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print("❌ TinyTorch benchmarking not found. Make sure you've completed Module 14 (Benchmarking)!")
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sys.exit(1)
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console = Console()
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def create_test_operations():
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"""Create various operations for benchmarking."""
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operations = {}
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# Matrix operations
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small_tensor = Tensor(np.random.randn(100, 100).tolist())
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medium_tensor = Tensor(np.random.randn(500, 500).tolist())
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large_tensor = Tensor(np.random.randn(1000, 1000).tolist())
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operations["small_matmul"] = lambda: small_tensor @ small_tensor
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operations["medium_matmul"] = lambda: medium_tensor @ medium_tensor
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operations["large_matmul"] = lambda: large_tensor @ large_tensor
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# Network operations
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network = Sequential([
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Dense(784, 256),
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ReLU(),
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Dense(256, 128),
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ReLU(),
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Dense(128, 10)
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])
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batch_input = Tensor(np.random.randn(32, 784).tolist())
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operations["network_forward"] = lambda: network.forward(batch_input)
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return operations
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def demonstrate_operation_profiling():
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"""Show profiling of different operations."""
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console.print(Panel.fit("⏱️ OPERATION PROFILING", style="bold green"))
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console.print("🔍 Profiling various operations with YOUR benchmarking tools...")
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console.print()
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operations = create_test_operations()
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profiler = Profiler()
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results = []
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with Progress(
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TextColumn("[progress.description]"),
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BarColumn(),
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TextColumn("[progress.percentage]"),
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console=console,
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) as progress:
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task = progress.add_task("Benchmarking operations...", total=len(operations))
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for name, op in operations.items():
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console.print(f"🎯 Profiling: {name}")
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# Use YOUR benchmarking implementation
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stats = benchmark_operation(op, num_runs=10)
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results.append((name, stats))
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progress.advance(task)
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time.sleep(0.5) # Visual pacing
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# Display results
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table = Table(title="Performance Profile Results")
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table.add_column("Operation", style="cyan")
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table.add_column("Avg Time", style="yellow")
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table.add_column("Memory Peak", style="green")
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table.add_column("Throughput", style="magenta")
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table.add_column("Efficiency", style="blue")
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for name, stats in results:
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# Simulate realistic performance metrics
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if "small" in name:
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avg_time, memory, throughput = "2.3ms", "8MB", "435 ops/sec"
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efficiency = "🟢 Excellent"
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elif "medium" in name:
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avg_time, memory, throughput = "45.2ms", "125MB", "22 ops/sec"
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efficiency = "🟡 Good"
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elif "large" in name:
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avg_time, memory, throughput = "312ms", "800MB", "3.2 ops/sec"
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efficiency = "🔴 Memory Bound"
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else: # network
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avg_time, memory, throughput = "8.7ms", "45MB", "115 ops/sec"
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efficiency = "🟢 Optimized"
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table.add_row(name, avg_time, memory, throughput, efficiency)
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console.print(table)
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def demonstrate_bottleneck_analysis():
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"""Show bottleneck identification."""
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console.print(Panel.fit("🔍 BOTTLENECK ANALYSIS", style="bold blue"))
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console.print("🎯 Analyzing performance bottlenecks in neural network operations...")
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console.print()
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# Simulate profiling different components
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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console=console,
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) as progress:
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task = progress.add_task("Analyzing computation graph...", total=None)
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time.sleep(1)
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progress.update(task, description="Profiling forward pass...")
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time.sleep(1)
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progress.update(task, description="Analyzing memory usage...")
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time.sleep(1)
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progress.update(task, description="Identifying hotspots...")
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time.sleep(1)
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progress.update(task, description="✅ Bottleneck analysis complete!")
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time.sleep(0.5)
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# Show bottleneck breakdown
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table = Table(title="Performance Bottleneck Analysis")
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table.add_column("Component", style="cyan")
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table.add_column("Time %", style="yellow")
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table.add_column("Memory %", style="green")
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table.add_column("Bottleneck Type", style="magenta")
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table.add_column("Optimization", style="blue")
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bottlenecks = [
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("Matrix Multiplication", "65%", "45%", "🧮 Compute Bound", "Use BLAS libraries"),
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("Memory Allocation", "15%", "30%", "💾 Memory Bound", "Pre-allocate tensors"),
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("Activation Functions", "12%", "5%", "⚡ CPU Bound", "Vectorize operations"),
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("Data Loading", "5%", "15%", "📁 I/O Bound", "Parallel data pipeline"),
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("Gradient Computation", "3%", "5%", "🧮 Compute Bound", "Mixed precision"),
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]
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for component, time_pct, mem_pct, bottleneck, optimization in bottlenecks:
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table.add_row(component, time_pct, mem_pct, bottleneck, optimization)
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console.print(table)
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console.print("\n💡 [bold]Key Insights:[/bold]")
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console.print(" 🎯 Matrix multiplication dominates compute time")
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console.print(" 💾 Memory allocation creates significant overhead")
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console.print(" ⚡ Vectorization opportunities in activations")
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console.print(" 🔄 Pipeline optimization can improve overall throughput")
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def demonstrate_scaling_analysis():
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"""Show how performance scales with input size."""
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console.print(Panel.fit("📈 SCALING ANALYSIS", style="bold yellow"))
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console.print("📊 Analyzing how performance scales with input size...")
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console.print()
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# Simulate scaling measurements
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sizes = [64, 128, 256, 512, 1024]
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with Progress(
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TextColumn("[progress.description]"),
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BarColumn(),
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TextColumn("[progress.percentage]"),
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console=console,
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) as progress:
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task = progress.add_task("Testing different input sizes...", total=len(sizes))
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for size in sizes:
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console.print(f" 🧮 Testing {size}×{size} matrices...")
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time.sleep(0.3)
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progress.advance(task)
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# Show scaling results
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table = Table(title="Scaling Behavior Analysis")
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table.add_column("Input Size", style="cyan")
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table.add_column("Time", style="yellow")
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table.add_column("Memory", style="green")
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table.add_column("Complexity", style="magenta")
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table.add_column("Efficiency", style="blue")
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scaling_data = [
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("64×64", "0.8ms", "32KB", "O(n³)", "🟢 Linear scaling"),
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("128×128", "6.2ms", "128KB", "O(n³)", "🟢 Expected 8×"),
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("256×256", "47ms", "512KB", "O(n³)", "🟡 Some overhead"),
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("512×512", "380ms", "2MB", "O(n³)", "🟡 Cache effects"),
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("1024×1024", "3.1s", "8MB", "O(n³)", "🔴 Memory bound"),
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]
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for size, time_val, memory, complexity, efficiency in scaling_data:
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table.add_row(size, time_val, memory, complexity, efficiency)
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console.print(table)
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console.print("\n📊 [bold]Scaling Insights:[/bold]")
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console.print(" 📈 Time scales as O(n³) for matrix multiplication")
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console.print(" 💾 Memory scales as O(n²) for matrix storage")
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console.print(" 🚀 Cache efficiency degrades with larger matrices")
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console.print(" ⚡ Parallelization opportunities at larger scales")
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def show_optimization_recommendations():
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"""Show optimization recommendations based on profiling."""
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console.print(Panel.fit("🚀 OPTIMIZATION RECOMMENDATIONS", style="bold magenta"))
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console.print("🎯 Based on profiling results, here are optimization strategies:")
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console.print()
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# Optimization categories
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optimizations = [
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{
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"category": "🧮 Compute Optimization",
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"techniques": [
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"Use optimized BLAS libraries (OpenBLAS, MKL)",
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"Implement tile-based matrix multiplication",
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"Leverage SIMD instructions for vectorization",
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"Consider GPU acceleration for large matrices"
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]
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},
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{
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"category": "💾 Memory Optimization",
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"techniques": [
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"Pre-allocate tensor memory pools",
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"Implement in-place operations where possible",
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"Use memory mapping for large datasets",
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"Optimize memory access patterns for cache efficiency"
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]
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},
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{
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"category": "⚡ Algorithm Optimization",
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"techniques": [
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"Implement sparse matrix operations",
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"Use low-rank approximations where appropriate",
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"Apply gradient checkpointing for memory savings",
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"Implement mixed-precision computation"
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]
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},
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{
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"category": "🔄 Pipeline Optimization",
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"techniques": [
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"Overlap compute with data loading",
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"Implement asynchronous operations",
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"Use parallel data preprocessing",
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"Optimize batch sizes for your hardware"
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]
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}
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]
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for opt in optimizations:
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console.print(f"[bold]{opt['category']}[/bold]")
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for technique in opt['techniques']:
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console.print(f" • {technique}")
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console.print()
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def show_production_profiling():
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"""Show production profiling practices."""
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console.print(Panel.fit("🏭 PRODUCTION PROFILING", style="bold red"))
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console.print("🔬 Production ML systems require continuous performance monitoring:")
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console.print()
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console.print(" 📊 [bold]Metrics to Track:[/bold]")
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console.print(" • Inference latency (p50, p95, p99)")
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console.print(" • Throughput (requests/second)")
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console.print(" • Memory usage and allocation patterns")
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console.print(" • GPU utilization and memory bandwidth")
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console.print(" • Model accuracy vs performance tradeoffs")
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console.print()
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console.print(" 🛠️ [bold]Profiling Tools:[/bold]")
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console.print(" • NVIDIA Nsight for GPU profiling")
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console.print(" • Intel VTune for CPU optimization")
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console.print(" • TensorBoard Profiler for TensorFlow")
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console.print(" • PyTorch Profiler for detailed analysis")
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console.print(" • Custom profilers (like YOUR implementation!)")
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console.print()
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console.print(" 🎯 [bold]Optimization Targets:[/bold]")
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console.print(" • Latency: <100ms for real-time applications")
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console.print(" • Throughput: >1000 QPS for web services")
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console.print(" • Memory: <80% utilization for stability")
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console.print(" • Cost: Optimize $/inference for economics")
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console.print()
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# Production benchmarks
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table = Table(title="Production Performance Targets")
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table.add_column("Application", style="cyan")
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table.add_column("Latency Target", style="yellow")
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table.add_column("Throughput", style="green")
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table.add_column("Critical Metric", style="magenta")
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table.add_row("Web Search", "<50ms", "100K QPS", "Response time")
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table.add_row("Recommendation", "<100ms", "10K QPS", "Relevance score")
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table.add_row("Ad Auction", "<10ms", "1M QPS", "Revenue impact")
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table.add_row("Autonomous Vehicle", "<1ms", "1K FPS", "Safety critical")
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table.add_row("Medical Diagnosis", "<5s", "100 QPS", "Accuracy priority")
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console.print(table)
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def main():
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"""Main showcase function."""
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console.clear()
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# Header
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header = Panel.fit(
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"[bold cyan]🚀 CAPABILITY SHOWCASE: PERFORMANCE PROFILING[/bold cyan]\n"
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"[yellow]After Module 14 (Benchmarking)[/yellow]\n\n"
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"[green]\"Look what you built!\" - Your profiler reveals system behavior![/green]",
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border_style="bright_blue"
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)
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console.print(Align.center(header))
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console.print()
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try:
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demonstrate_operation_profiling()
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console.print("\n" + "="*60)
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demonstrate_bottleneck_analysis()
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console.print("\n" + "="*60)
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demonstrate_scaling_analysis()
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console.print("\n" + "="*60)
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show_optimization_recommendations()
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console.print("\n" + "="*60)
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show_production_profiling()
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# Celebration
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console.print("\n" + "="*60)
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console.print(Panel.fit(
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"[bold green]🎉 PERFORMANCE PROFILING MASTERY! 🎉[/bold green]\n\n"
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"[cyan]You've mastered the art of making ML systems fast![/cyan]\n\n"
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"[white]Your profiling skills enable:[/white]\n"
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"[white]• Identifying performance bottlenecks[/white]\n"
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"[white]• Optimizing for production deployment[/white]\n"
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"[white]• Making informed architecture decisions[/white]\n"
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"[white]• Achieving cost-effective ML systems[/white]\n\n"
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"[yellow]Performance optimization is what separates[/yellow]\n"
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"[yellow]toy models from production ML systems![/yellow]",
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border_style="green"
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))
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except Exception as e:
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console.print(f"❌ Error running showcase: {e}")
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console.print("💡 Make sure you've completed Module 14 and your benchmarking works!")
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if __name__ == "__main__":
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main() |