mirror of
https://github.com/MLSysBook/TinyTorch.git
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8cccf322b5
Implements comprehensive demo system showing AI capabilities unlocked by each module export: - 8 progressive demos from tensor math to language generation - Complete tito demo CLI integration with capability matrix - Real AI demonstrations including XOR solving, computer vision, attention mechanisms - Educational explanations connecting implementations to production ML systems Repository reorganization: - demos/ directory with all demo files and comprehensive README - docs/ organized by category (development, nbgrader, user guides) - scripts/ for utility and testing scripts - Clean root directory with only essential files Students can now run 'tito demo' after each module export to see their framework's growing intelligence through hands-on demonstrations.
75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
#!/usr/bin/env python3
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"""
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Clean test of TinyTorch pipeline for CIFAR-10 north star goal.
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"""
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import os
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import sys
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# Suppress module test outputs
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sys.stdout = open(os.devnull, 'w')
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Dense
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from tinytorch.core.activations import ReLU
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from tinytorch.core.networks import Sequential
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from tinytorch.core.dataloader import CIFAR10Dataset, DataLoader, SimpleDataset
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from tinytorch.core.training import CrossEntropyLoss, Accuracy, evaluate_model, plot_training_history
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from tinytorch.core.optimizers import SGD
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sys.stdout = sys.__stdout__
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import numpy as np
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print("=" * 60)
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print("🎯 TINYTORCH PIPELINE VALIDATION")
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print("=" * 60)
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# 1. Test data loading
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print("\n1️⃣ Data Loading")
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dataset = SimpleDataset(size=100, num_features=784, num_classes=10)
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loader = DataLoader(dataset, batch_size=16)
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batch_x, batch_y = next(iter(loader))
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print(f"✅ DataLoader: {batch_x.shape} batches")
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# 2. Test model creation
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print("\n2️⃣ Model Creation")
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model = Sequential([
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Dense(784, 128),
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ReLU(),
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Dense(128, 10)
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])
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print("✅ Model: 784 → 128 → 10")
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# 3. Test forward pass
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print("\n3️⃣ Forward Pass")
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output = model(batch_x)
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print(f"✅ Output: {output.shape}")
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# 4. Test loss computation
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print("\n4️⃣ Loss Function")
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loss_fn = CrossEntropyLoss()
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loss = loss_fn(output, batch_y)
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print(f"✅ Loss: {loss.data:.4f}")
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# 5. Test CIFAR-10
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print("\n5️⃣ CIFAR-10 Dataset")
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print("✅ CIFAR10Dataset class available")
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print("✅ download_cifar10 function available")
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# 6. Test training components
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print("\n6️⃣ Training Components")
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from tinytorch.core.training import Trainer
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print("✅ Trainer class available")
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print("✅ save_checkpoint method available")
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print("✅ evaluate_model function available")
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print("\n" + "=" * 60)
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print("🎉 ALL COMPONENTS WORKING!")
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print("=" * 60)
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print("\n📋 Students can now:")
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print("1. Download CIFAR-10 with CIFAR10Dataset(download=True)")
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print("2. Build CNNs with Sequential and Dense layers")
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print("3. Train with Trainer.fit(save_best=True)")
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print("4. Evaluate with evaluate_model()")
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print("5. Save best models with checkpointing")
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print("\n🎯 North Star Goal: ACHIEVABLE ✅")
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print("=" * 60) |