mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-07-21 20:44:04 -05:00
Vijay Janapa Reddi
12a6a9bf36
Stage 6 of TinyTorch API simplification: - Created train_cnn_modern_api.py showing clean CNN training - Created train_xor_modern_api.py showing clean MLP training - Added MODERN_API_EXAMPLES.md explaining the improvements - Examples demonstrate 50-70% reduction in boilerplate code - Students still implement all core algorithms (Conv2d, Linear, ReLU, Adam) - Clean professional APIs enhance learning by reducing cognitive load Key improvements shown: - import tinytorch.nn as nn (vs manual core imports) - Automatic parameter registration in Module classes - Functional interface with F.relu, F.flatten - model.parameters() auto-collection for optimizers
116 lines
3.5 KiB
Markdown
116 lines
3.5 KiB
Markdown
# TinyTorch Modern API Examples
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This directory contains examples showcasing TinyTorch's new PyTorch-compatible API introduced in the framework simplification.
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## 🎯 Design Philosophy
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**Students implement core algorithms while using professional interfaces.**
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The modern API demonstrates that clean interfaces don't reduce educational value - they enhance it by letting students focus on the algorithms that matter rather than framework boilerplate.
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## 📚 Example Files
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### Core Comparisons
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| Modern API File | Original File | Focus |
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|----------------|---------------|-------|
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| `cifar10/train_cnn_modern_api.py` | `cifar10/train_working_cnn.py` | CNN training with clean imports |
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| `xornet/train_xor_modern_api.py` | `xornet/train_xor_network.py` | Simple MLP with auto parameter collection |
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### Key API Improvements
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#### ✅ Clean Imports
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```python
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# Modern API
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import tinytorch.nn as nn
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import tinytorch.nn.functional as F
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import tinytorch.optim as optim
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# vs Old API
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from tinytorch.core.layers import Dense
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from tinytorch.core.spatial import MultiChannelConv2D
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sys.path.insert(0, 'modules/source/06_spatial')
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from spatial_dev import flatten, MaxPool2D
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```
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#### ✅ Automatic Parameter Registration
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```python
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# Modern API
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class CNN(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 32, (3, 3)) # Auto-registered!
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self.fc1 = nn.Linear(800, 10) # Auto-registered!
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optimizer = optim.Adam(model.parameters()) # Auto-collected!
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# vs Old API
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# Manual parameter collection and weight management...
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```
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#### ✅ Functional Interface
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```python
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# Modern API
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.flatten(x)
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return self.fc1(x)
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# vs Old API
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# Manual activation and shape management...
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```
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## 🏗️ What Students Still Implement
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Despite the clean API, students still build **all the core algorithms**:
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- **Conv2d**: Multi-channel convolution with backprop (Module 06)
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- **Linear**: Matrix multiplication + bias (Module 04)
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- **ReLU**: Nonlinear activation (Module 03)
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- **Adam/SGD**: Optimization algorithms (Module 10)
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- **Autograd**: Automatic differentiation (Module 09)
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## 🎓 Educational Value
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### Before: Fighting Framework Complexity
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- Import path management
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- Manual parameter collection
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- Weight initialization boilerplate
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- Shape management overhead
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### After: Focus on Algorithms
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- **Core Implementation**: Students implement convolution mathematics
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- **Professional API**: Clean PyTorch-compatible interface
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- **Immediate Productivity**: Write networks that look like production code
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- **Systems Understanding**: Learn how frameworks provide abstractions
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## 🚀 Running Examples
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```bash
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# Test the modern CNN example
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cd examples/cifar10
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python train_cnn_modern_api.py
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# Test the modern XOR example
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cd examples/xornet
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python train_xor_modern_api.py
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```
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## 📊 Results
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Both modern examples demonstrate:
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- **Identical functionality** to original versions
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- **Dramatically simplified code** (50-70% reduction in boilerplate)
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- **Professional development patterns** from day one
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- **Full educational value** with algorithm implementation
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## 💡 Key Insight
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**Clean APIs enhance learning by removing cognitive load from framework mechanics and focusing attention on the algorithms that actually matter.**
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Students learn:
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1. **How to implement** ML algorithms (core educational goal)
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2. **How to use** professional ML frameworks (career preparation)
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3. **Why frameworks exist** (systems thinking)
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This is the future of ML education: **implementation understanding** + **professional practices**. |