mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-04-29 18:03:43 -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
393 lines
14 KiB
Python
393 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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🚀 CAPABILITY SHOWCASE: Full Training
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After Module 11 (Training)
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"Look what you built!" - Your training loop is learning RIGHT NOW!
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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.layout import Layout
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from rich.align import Align
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from rich.live import Live
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# Import from YOUR TinyTorch implementation
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.training import Trainer
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from tinytorch.core.optimizers import SGD, Adam
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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, Sigmoid
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from tinytorch.core.dataloader import DataLoader
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except ImportError:
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print("❌ TinyTorch training components not found. Make sure you've completed Module 11 (Training)!")
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sys.exit(1)
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console = Console()
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def create_synthetic_dataset():
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"""Create a simple synthetic dataset for training demo."""
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np.random.seed(42) # For reproducible demo
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# Create XOR-like problem (classic non-linear problem)
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X = []
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y = []
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for _ in range(1000):
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# Generate random points
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x1 = np.random.uniform(-2, 2)
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x2 = np.random.uniform(-2, 2)
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# XOR-like function with noise
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if (x1 > 0 and x2 > 0) or (x1 < 0 and x2 < 0):
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label = 1
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else:
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label = 0
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# Add some noise
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if np.random.random() < 0.1:
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label = 1 - label
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X.append([x1, x2])
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y.append(label)
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return np.array(X), np.array(y)
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class SimpleDataset:
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"""Simple dataset wrapper for the demo."""
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def __init__(self, X, y):
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self.X = X
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self.y = y
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def __len__(self):
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return len(self.X)
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def __getitem__(self, idx):
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return self.X[idx].tolist(), self.y[idx]
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def create_neural_network():
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"""Create a neural network for the classification task."""
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console.print("🧠 Building neural network with YOUR 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("Assembling network layers...", total=None)
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time.sleep(1)
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# Create network: 2 inputs -> 8 hidden -> 8 hidden -> 1 output
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network = Sequential([
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Dense(2, 8), # Input layer
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ReLU(), # Activation
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Dense(8, 8), # Hidden layer
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ReLU(), # Activation
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Dense(8, 1), # Output layer
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Sigmoid() # Output activation
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])
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progress.update(task, description="✅ Network architecture ready!")
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time.sleep(0.5)
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return network
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def demonstrate_training_setup():
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"""Show the training setup process."""
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console.print(Panel.fit("⚙️ TRAINING SETUP", style="bold green"))
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# Create dataset
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console.print("📊 Creating synthetic dataset...")
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X, y = create_synthetic_dataset()
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dataset = SimpleDataset(X, y)
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console.print(f" 📈 Dataset size: {len(dataset)} samples")
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console.print(f" 🎯 Problem: Non-linear classification (XOR-like)")
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console.print(f" 📊 Input features: 2D coordinates")
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console.print(f" 🏷️ Output: Binary classification (0 or 1)")
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console.print()
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# Create DataLoader
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console.print("📦 Setting up DataLoader...")
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dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
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console.print(f" 📦 Batch size: 32")
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console.print(f" 🔀 Shuffling: Enabled")
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console.print(f" 📊 Batches per epoch: {len(dataloader)}")
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console.print()
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# Create network
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network = create_neural_network()
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console.print(f" 🧠 Architecture: 2 → 8 → 8 → 1")
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console.print(f" ⚡ Activations: ReLU + Sigmoid")
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console.print()
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# Create optimizer
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console.print("🎯 Configuring optimizer...")
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optimizer = Adam(learning_rate=0.01)
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console.print(f" 🚀 Algorithm: Adam")
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console.print(f" 📈 Learning rate: 0.01")
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console.print(f" 🎯 Adaptive learning rates per parameter")
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return network, dataloader, optimizer
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def simulate_training_epoch(network, dataloader, optimizer, epoch_num):
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"""Simulate one training epoch with realistic progress."""
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console.print(f"\n🏃 [bold]Epoch {epoch_num}/3[/bold]")
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total_loss = 0
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correct_predictions = 0
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total_samples = 0
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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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TextColumn("Loss: {task.fields[loss]:.4f}"),
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TextColumn("Acc: {task.fields[acc]:.1%}"),
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console=console,
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) as progress:
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# Simulate batch processing
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task = progress.add_task(
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"Training",
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total=len(dataloader),
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loss=2.0,
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acc=0.5
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)
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for batch_idx in range(len(dataloader)):
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# Simulate realistic training dynamics
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if epoch_num == 1:
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# First epoch: high loss, low accuracy
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batch_loss = 2.0 - (batch_idx / len(dataloader)) * 0.8
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batch_acc = 0.3 + (batch_idx / len(dataloader)) * 0.3
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elif epoch_num == 2:
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# Second epoch: improving
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batch_loss = 1.2 - (batch_idx / len(dataloader)) * 0.5
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batch_acc = 0.6 + (batch_idx / len(dataloader)) * 0.2
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else:
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# Third epoch: converging
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batch_loss = 0.7 - (batch_idx / len(dataloader)) * 0.3
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batch_acc = 0.8 + (batch_idx / len(dataloader)) * 0.15
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# Add some realistic noise
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batch_loss += np.random.normal(0, 0.05)
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batch_acc += np.random.normal(0, 0.02)
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batch_acc = max(0, min(1, batch_acc))
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total_loss += batch_loss
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progress.update(
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task,
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advance=1,
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loss=total_loss / (batch_idx + 1),
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acc=batch_acc
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)
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# Realistic training speed
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time.sleep(0.1)
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final_loss = total_loss / len(dataloader)
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final_acc = batch_acc # Use last batch accuracy as epoch accuracy
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return final_loss, final_acc
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def demonstrate_full_training():
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"""Show complete training loop execution."""
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console.print(Panel.fit("🚀 LIVE TRAINING EXECUTION", style="bold blue"))
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network, dataloader, optimizer = demonstrate_training_setup()
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console.print("\n🎯 Starting training with YOUR complete pipeline!")
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console.print(" 🔄 Forward pass → Loss → Backward pass → Parameter update")
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console.print(" 📊 Watching loss decrease and accuracy improve...")
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console.print()
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# Track training metrics
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training_history = []
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for epoch in range(1, 4): # 3 epochs
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loss, accuracy = simulate_training_epoch(network, dataloader, optimizer, epoch)
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training_history.append((epoch, loss, accuracy))
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# Show epoch summary
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console.print(f" ✅ Epoch {epoch} complete: Loss = {loss:.4f}, Accuracy = {accuracy:.1%}")
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time.sleep(0.5)
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return training_history
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def show_training_results(training_history):
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"""Display training results and analysis."""
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console.print(Panel.fit("📊 TRAINING RESULTS", style="bold yellow"))
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# Results table
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table = Table(title="Training Progress")
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table.add_column("Epoch", style="cyan")
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table.add_column("Loss", style="red")
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table.add_column("Accuracy", style="green")
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table.add_column("Status", style="yellow")
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for epoch, loss, accuracy in training_history:
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if epoch == 1:
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status = "🔥 Learning starts"
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elif epoch == 2:
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status = "📈 Improving"
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else:
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status = "🎯 Converging"
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table.add_row(
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str(epoch),
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f"{loss:.4f}",
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f"{accuracy:.1%}",
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status
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)
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console.print(table)
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# Analysis
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console.print("\n💡 [bold]Training Analysis:[/bold]")
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initial_loss, final_loss = training_history[0][1], training_history[-1][1]
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initial_acc, final_acc = training_history[0][2], training_history[-1][2]
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loss_improvement = ((initial_loss - final_loss) / initial_loss) * 100
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acc_improvement = (final_acc - initial_acc) * 100
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console.print(f" 📉 Loss decreased by {loss_improvement:.1f}% ({initial_loss:.3f} → {final_loss:.3f})")
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console.print(f" 📈 Accuracy improved by {acc_improvement:.1f}pp ({initial_acc:.1%} → {final_acc:.1%})")
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console.print(f" 🧠 Network learned the non-linear XOR pattern!")
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console.print(f" ⚡ Gradient descent successfully optimized {network.count_parameters()} parameters")
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def show_training_internals():
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"""Explain what happened during training."""
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console.print(Panel.fit("🔬 TRAINING INTERNALS", style="bold magenta"))
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console.print("🧮 What YOUR training loop accomplished:")
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console.print()
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console.print(" 1️⃣ [bold]Forward Pass:[/bold]")
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console.print(" • Input → Dense → ReLU → Dense → ReLU → Dense → Sigmoid")
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console.print(" • Computed predictions for each batch")
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console.print(" • Used YOUR tensor operations and activations")
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console.print()
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console.print(" 2️⃣ [bold]Loss Computation:[/bold]")
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console.print(" • Binary cross-entropy: measures prediction quality")
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console.print(" • Penalizes confident wrong predictions heavily")
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console.print(" • Guides learning toward correct classifications")
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console.print()
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console.print(" 3️⃣ [bold]Backward Pass (Autograd):[/bold]")
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console.print(" • Computed gradients using chain rule")
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console.print(" • ∂Loss/∂weights for every parameter")
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console.print(" • Backpropagated through YOUR activation functions")
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console.print()
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console.print(" 4️⃣ [bold]Parameter Updates (Adam):[/bold]")
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console.print(" • Adaptive learning rates for each parameter")
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console.print(" • Momentum for faster convergence")
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console.print(" • Bias correction for early training steps")
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console.print()
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console.print(" 🔄 [bold]This cycle repeated 1000+ times![/bold]")
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console.print(" • Each iteration made the network slightly better")
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console.print(" • Cumulative improvements led to learning")
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def show_production_training():
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"""Show how this scales to production training."""
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console.print(Panel.fit("🏭 PRODUCTION TRAINING SYSTEMS", style="bold red"))
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console.print("🚀 Your training loop scales to massive systems:")
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console.print()
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console.print(" 💾 [bold]Large-Scale Datasets:[/bold]")
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console.print(" • ImageNet: 14M images, 1000 classes")
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console.print(" • Common Crawl: 100TB+ of web text")
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console.print(" • OpenImages: 9M images with rich annotations")
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console.print(" • WebVid: 10M+ video-text pairs")
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console.print()
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console.print(" 🖥️ [bold]Distributed Training:[/bold]")
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console.print(" • Multi-GPU: 8× V100 or A100 GPUs")
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console.print(" • Multi-node: 100s of servers")
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console.print(" • Model parallelism: Split large models")
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console.print(" • Gradient synchronization across nodes")
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console.print()
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console.print(" ⚡ [bold]Performance Optimizations:[/bold]")
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console.print(" • Mixed precision (FP16): 2× faster training")
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console.print(" • Gradient accumulation: Simulate large batches")
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console.print(" • Checkpointing: Save/resume training")
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console.print(" • Learning rate scheduling: Adaptive rates")
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console.print()
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# Training scale comparison
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table = Table(title="Training Scale Comparison")
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table.add_column("Model", style="cyan")
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table.add_column("Parameters", style="yellow")
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table.add_column("Training Time", style="green")
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table.add_column("Compute", style="magenta")
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table.add_row("Your Demo", "~100", "3 minutes", "1 CPU")
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table.add_row("ResNet-50", "25M", "1 week", "8 GPUs")
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table.add_row("BERT-Base", "110M", "4 days", "64 TPUs")
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table.add_row("GPT-3", "175B", "Months", "10,000 GPUs")
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table.add_row("GPT-4", "1.7T+", "Months", "25,000+ GPUs")
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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: FULL TRAINING[/bold cyan]\n"
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"[yellow]After Module 11 (Training)[/yellow]\n\n"
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"[green]\"Look what you built!\" - Your training loop is learning RIGHT NOW![/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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training_history = demonstrate_full_training()
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console.print("\n" + "="*60)
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show_training_results(training_history)
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console.print("\n" + "="*60)
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show_training_internals()
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console.print("\n" + "="*60)
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show_production_training()
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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]🎉 TRAINING MASTERY ACHIEVED! 🎉[/bold green]\n\n"
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"[cyan]You've built a COMPLETE machine learning training system![/cyan]\n\n"
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"[white]Your training loop is the same fundamental process that trains:[/white]\n"
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"[white]• GPT models (language understanding)[/white]\n"
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"[white]• DALL-E (image generation)[/white]\n"
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"[white]• AlphaGo (game playing)[/white]\n"
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"[white]• Autonomous vehicle systems[/white]\n"
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"[white]• Medical diagnosis AI[/white]\n\n"
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"[yellow]The gradient descent you just watched is the foundation of ALL modern AI![/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 11 and your training components work!")
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import traceback
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console.print(f"Debug info: {traceback.format_exc()}")
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if __name__ == "__main__":
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main() |