TinyTorch: Build ML Systems from Scratch#
Don't just import it. Build it.
What is TinyTorch?#
TinyTorch is an educational ML systems course where you build complete neural networks from scratch. Instead of blindly using PyTorch or TensorFlow as black boxes, you implement every component yourself—from tensors and gradients to optimizers and attention mechanisms—gaining deep understanding of how modern ML frameworks actually work.
Core Learning Approach: Build → Profile → Optimize. You’ll implement each system component, measure its performance characteristics, and understand the engineering trade-offs that shape production ML systems.
The ML Evolution Story You’ll Experience#
Journey through 40+ years of ML breakthroughs by building each era yourself: 1980s neural foundations → 1990s backpropagation → 2012 CNN revolution → 2017 transformer unification → 2024 production optimization. Each module teaches both the breakthrough AND the systems engineering that made it possible.
đź“– See Complete ML Evolution Timeline for the full historical context and technical progression.
🏆 Prove Your Mastery Through History#
As you complete modules, unlock historical milestone demonstrations that prove what you’ve built works! From Rosenblatt’s 1957 perceptron to modern CNNs achieving 75%+ accuracy on CIFAR-10, each milestone recreates a breakthrough using YOUR implementations:
🧠1957: Perceptron - First trainable network with YOUR Linear layer
⚡ 1969: XOR Solution - Multi-layer networks with YOUR autograd
🔢 1986: MNIST MLP - Backpropagation achieving 95%+ with YOUR optimizers
🖼️ 1998: CIFAR-10 CNN - Spatial intelligence with YOUR Conv2d (75%+ accuracy!)
🤖 2017: Transformers - Language generation with YOUR attention
⚡ 2024: Systems Age - Production optimization with YOUR profiling
đź“– See Journey Through ML History for complete milestone details and requirements.
Why Build Instead of Use?#
The difference between using a library and understanding a system is the difference between being limited by tools and being empowered to create them. When you build from scratch, you transform from a framework user into a systems engineer:
❌ Using PyTorch
import torch.nn as nn
import torch.optim as optim
model = nn.Linear(784, 10)
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Your model trains but then...
# 🔥 OOM error! Why?
# 🔥 Loss is NaN! How to debug?
# 🔥 Training is slow! What's the bottleneck?
You're stuck when things break
❌ Using TensorFlow
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10)
])
# Magic happens somewhere...
# 🤷 How are gradients computed?
# 🤷 Why this initialization?
# 🤷 What's happening in backward pass?
Magic boxes you can't understand
âś… Building TinyTorch
class Linear:
def __init__(self, in_features, out_features):
self.weight = randn(in_features, out_features) * 0.01
self.bias = zeros(out_features)
def forward(self, x):
self.input = x # Save for backward
return x @ self.weight + self.bias
def backward(self, grad):
# You wrote this! You know exactly why:
self.weight.grad = self.input.T @ grad
self.bias.grad = grad.sum(axis=0)
return grad @ self.weight.T
You can debug anything
âś… Building KV Cache
class KVCache:
def __init__(self, max_seq_len, n_heads, head_dim):
# You understand EXACTLY the memory layout:
self.k_cache = zeros(max_seq_len, n_heads, head_dim)
self.v_cache = zeros(max_seq_len, n_heads, head_dim)
# That's why GPT needs GBs of RAM!
def update(self, k, v, pos):
# You know why position matters:
self.k_cache[pos:pos+len(k)] = k # Reuse past computations
self.v_cache[pos:pos+len(v)] = v # O(n²) → O(n) speedup!
# Now you understand why context windows are limited
You master modern LLM optimizations
Who Is This For?#
Perfect if you’re asking these questions:
ML Systems Engineers: “Why does my model training OOM at batch size 32? How do attention mechanisms scale quadratically with sequence length? When does data loading become the bottleneck?” You’ll build and profile every component, understanding memory hierarchies, computational complexity, and system bottlenecks that production ML systems face daily.
Students & Researchers: “How does that nn.Linear() call actually compute gradients? Why does Adam optimizer need 3× the memory of SGD? What’s actually happening during a forward pass?” You’ll implement the mathematics you learned in class and discover how theoretical concepts become practical systems with real performance implications.
Performance Engineers: “Where are the actual bottlenecks in transformer inference? How does KV-cache reduce computation by 10-100×? Why does my CNN use 4GB of memory?” By building these systems from scratch, you’ll understand memory access patterns, cache efficiency, and optimization opportunities that profilers alone can’t teach.
Academics & Educators: “How can I teach ML systems—not just ML algorithms?” TinyTorch provides a complete pedagogical framework emphasizing systems thinking: memory profiling, performance analysis, and scaling behavior are built into every module, not added as an afterthought.
ML Practitioners: “Why does training slow down after epoch 10? How do I debug gradient explosions? When should I use mixed precision?” Even experienced engineers often treat frameworks as black boxes. By understanding the systems underneath, you’ll debug faster, optimize better, and make informed architectural decisions.
How to Choose Your Learning Path#
Two Learning Approaches: You can either build it yourself (work through student notebooks and implement from scratch) or learn by reading (study the solution notebooks to understand how ML systems work). Both approaches use the same Build → Profile → Optimize methodology at different scales.
🔬 Quick Start
15 minutes setup • Try foundational modules • Hands-on experience
Start Building →📚 Full Course
8+ weeks study • Complete ML framework • Systems understanding
Course Overview →📊 Learning Community
Track progress • Join competitions • Student leaderboard
View Progress →Getting Started#
Whether you’re just exploring or ready to dive in, here are helpful resources: 📖 See Essential Commands for complete setup and command reference, or 📖 See Complete Course Structure for detailed module descriptions.
Additional Resources:
Progress Tracking - Monitor your learning journey with 21 capability checkpoints
Testing Framework - Understand our comprehensive validation system
Documentation & Guides - Complete technical documentation and tutorials
TinyTorch is more than a course—it’s a community of learners building together. Join thousands exploring ML systems from the ground up.