Milestone System

<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 2rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center; color: white;"> <h2 style="margin: 0 0 1rem 0; color: white;">Recreate ML History with YOUR Code</h2> <p style="margin: 0; font-size: 1.1rem; opacity: 0.95;">Run the algorithms that changed the world using the TinyTorch you built from scratch</p> </div>

Purpose: The milestone system lets you run famous ML algorithms (1958-2018) using YOUR implementations. Every milestone validates that your code can recreate a historical breakthrough.

The milestone system lets you run famous ML algorithms using YOUR implementations.

What Are Milestones?

Milestones are runnable recreations of historical ML papers that use YOUR TinyTorch implementations:

1958 - Rosenblatt's Perceptron: The first trainable neural network
1969 - XOR Solution: Solving the problem that stalled AI
1986 - Backpropagation: The MLP revival (Rumelhart, Hinton & Williams)
1998 - LeNet: Yann LeCun's CNN breakthrough
2017 - Transformer: "Attention is All You Need" (Vaswani et al.)
2018 - MLPerf: Production ML benchmarks

Each milestone script imports YOUR code from the TinyTorch package you built.

Quick Start

Typical workflow:

bash

# 1. Build the required modules (e.g., Foundation Tier for Milestone 03)
tito module complete 01 # Tensor
tito module complete 02 # Activations
tito module complete 03 # Layers
tito module complete 04 # Losses
tito module complete 05 # DataLoader
tito module complete 06 # Autograd
tito module complete 07 # Optimizers
tito module complete 08 # Training

# 2. See what milestones you can run
tito milestone list

# 3. Get details about a specific milestone
tito milestone info 03

# 4. Run it!
tito milestone run 03

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Essential Commands

Discover Milestones

List All Milestones

bash

tito milestone list

Shows all 6 historical milestones with status:

LOCKED - Need to complete required modules first
READY TO RUN - All prerequisites met!
COMPLETE - You've already achieved this

Simple View (compact list):

bash

tito milestone list --simple

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Learn About Milestones

Get Detailed Information

bash

tito milestone info 03

Shows:

Historical context (year, researchers, significance)
Description of what you'll recreate
Required modules with / status
Whether you're ready to run it

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Run Milestones

Run a Milestone

bash

tito milestone run 03

What happens:

Checks prerequisites - Validates required modules are complete
Tests imports - Ensures YOUR implementations work
Shows context - Historical background and what you'll recreate
Runs the script - Executes the milestone using YOUR code
Tracks achievement - Records your completion
Celebrates! - Shows achievement message

Skip prerequisite checks (not recommended):

bash

tito milestone run 03 --skip-checks

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Track Progress

View Milestone Progress

bash

tito milestone status

Shows:

How many milestones you've completed
Your overall progress (%)
Unlocked capabilities
Next milestone ready to run

Visual Timeline

bash

tito milestone timeline

See your journey through ML history in a visual tree format.

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The 6 Milestones

Milestone 01: Perceptron (1958)

What: Frank Rosenblatt's first trainable neural network

Requires: Modules 01-03 (Tensor, Activations, Layers)

What you'll do: Implement and train the perceptron that proved machines could learn

Historical significance: First demonstration of machine learning

Run it:

bash

tito milestone info 01
tito milestone run 01

Milestone 02: XOR Crisis (1969)

What: Demonstrating the problem that stalled AI research

Requires: Modules 01-03 (Tensor, Activations, Layers)

What you'll do: Experience how single-layer perceptrons fail on XOR - the limitation that triggered the "AI Winter"

Historical significance: Minsky & Papert showed perceptron limitations; this milestone demonstrates the crisis before the solution

Run it:

bash

tito milestone info 02
tito milestone run 02

Milestone 03: MLP Revival (1986)

What: Backpropagation breakthrough - train deep networks on MNIST

Requires: Modules 01-08 (Complete Foundation Tier)

What you'll do: Train a multi-layer perceptron to recognize handwritten digits (95%+ accuracy)

Historical significance: Rumelhart, Hinton & Williams (Nature, 1986) - the paper that reignited neural network research

Run it:

bash

tito milestone info 03
tito milestone run 03

Milestone 04: CNN Revolution (1998)

What: LeNet - Computer Vision Breakthrough

Requires: Modules 01-09 (Foundation + Convolutions)

What you'll do: Build LeNet for digit recognition using convolutional layers

Historical significance: Yann LeCun's breakthrough that enabled modern computer vision

Run it:

bash

tito milestone info 04
tito milestone run 04

Milestone 05: Transformer Era (2017)

What: "Attention is All You Need"

Requires: Modules 01-08 + 11-13 (Foundation + Embeddings, Attention, Transformers)

What you'll do: Implement transformer architecture with self-attention mechanism

Historical significance: Vaswani et al. revolutionized NLP and enabled GPT/BERT/modern LLMs

Run it:

bash

tito milestone info 05
tito milestone run 05

Milestone 06: MLPerf Benchmarks (2018)

What: Production ML Systems

Requires: Modules 01-08 + 14-19 (Foundation + Optimization Tier)

What you'll do: Optimize for production deployment with quantization, compression, and benchmarking

Historical significance: MLPerf standardized ML system benchmarks for real-world deployment

Run it:

bash

tito milestone info 06
tito milestone run 06

Prerequisites and Validation

How Prerequisites Work

Each milestone requires specific modules to be complete. The run command automatically validates:

Module Completion Check:

bash

tito milestone run 03

 Checking prerequisites for Milestone 03...
 Module 01 - complete
 Module 02 - complete
 Module 03 - complete
 Module 04 - complete
 Module 05 - complete
 Module 06 - complete
 Module 07 - complete
 Module 08 - complete

 All prerequisites met!

Import Validation:

bash

 Testing YOUR implementations...
 Tensor import successful
 Activations import successful
 Layers import successful

 YOUR TinyTorch is ready!

If Prerequisites Are Missing

You'll see a helpful error:

bash

 Missing Required Modules

Milestone 03 requires modules: 01, 02, 03, 04, 05, 06, 07, 08
Missing: 06, 07, 08

Complete the missing modules first:
 tito module start 06
 tito module start 07
 tito module start 08

Achievement Celebration

When you successfully complete a milestone, you'll see:

╔════════════════════════════════════════════════╗
║ Milestone 03: MLP Revival (1986) ║
║ Backpropagation Breakthrough ║
╚════════════════════════════════════════════════╝

 MILESTONE ACHIEVED!

You completed Milestone 03: MLP Revival (1986)
Backpropagation Breakthrough

What makes this special:
• Every line of code: YOUR implementations
• Every tensor operation: YOUR Tensor class
• Every gradient: YOUR autograd

Achievement saved to your progress!

 What's Next:
Milestone 04: CNN Revolution (1998)
Unlock by completing module: 09

Understanding Your Progress

Three Tracking Systems

TinyTorch tracks progress in three ways (all are related but distinct):

1. Module Completion (tito module status)

Which modules (01-20) you've implemented
Tracked in .tito/progress.json
Required for running milestones

2. Milestone Achievements (tito milestone status)

Which historical papers you've recreated
Tracked in .tito/milestones.json
Unlocked by completing modules + running milestones

3. Overall Status

Check tito module status and tito milestone status
Quick view of all progress
Purely informational

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Relationship Between Systems

Complete Modules (01-08)
 ↓
Unlock Milestone 03
 ↓
Run: tito milestone run 03
 ↓
Achievement Recorded
 ↓
Capability Unlocked (optional checkpoint system)

Tips for Success

1. Complete Modules in Order

While you can technically skip around, the tier structure is designed for progressive learning:

Foundation Tier (01-08): Required for first milestone
Architecture Tier (09-13): Build on Foundation
Optimization Tier (14-19): Build on Architecture

2. Test as You Go

Before running a milestone, make sure your modules work:

bash

# After completing a module
tito module complete 05

# Test it works
python -c "from tinytorch import Tensor; print(Tensor([[1,2]]))"

3. Use Info Before Run

Learn what you're about to do:

bash

tito milestone info 03 # Read the context first
tito milestone run 03 # Then run it

4. Celebrate Achievements

Share your milestones! Each one represents recreating a breakthrough that shaped modern AI.

Troubleshooting

"Import Error" when running milestone

Problem: Module not exported or import failing

Solution:

bash

# Re-export the module
tito module complete XX

# Test import manually
python -c "from tinytorch import Tensor"

"Prerequisites Not Met" but I completed modules

Problem: Progress not tracked correctly

Solution:

bash

# Check module status
tito module status

# If modules show incomplete, re-run complete
tito module complete XX

Milestone script fails during execution

Problem: Bug in your implementation

Solution:

Check error message for which module failed
Edit modules/XX_name/XX_name.ipynb (NOT tinytorch/)
Re-export: tito module complete XX
Run milestone again

Next Steps

<div style="background: #f8f9fa; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;"> <h3 style="margin: 0 0 1rem 0; color: #495057;">Ready to Recreate ML History?</h3> <p style="margin: 0 0 1.5rem 0; color: #6c757d;">Start with the Foundation Tier and work toward your first milestone</p> <a href="../tiers/foundation.html" style="display: inline-block; background: #007bff; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500; margin-right: 1rem;">Foundation Tier →</a> <a href="../milestones/milestones_ABOUT.html" style="display: inline-block; background: #6f42c1; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500;">Historical Context →</a> </div>

Every milestone uses YOUR code. Every achievement is proof you understand ML systems deeply. Build from scratch, recreate history, master the fundamentals.