Milestone System
Recreate ML History with YOUR Code
Run the algorithms that changed the world using the TinyTorch you built from scratch
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:
# 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 03Essential Commands
Discover Milestones
List All Milestones
tito milestone listShows 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):
tito milestone list --simpleLearn About Milestones
Get Detailed Information
tito milestone info 03Shows: - Historical context (year, researchers, significance) - Description of what you’ll recreate - Required modules with / status - Whether you’re ready to run it
Run Milestones
Run a Milestone
tito milestone run 03What happens: 1. Checks prerequisites - Validates required modules are complete 2. Tests imports - Ensures YOUR implementations work 3. Shows context - Historical background and what you’ll recreate 4. Runs the script - Executes the milestone using YOUR code 5. Tracks achievement - Records your completion 6. Celebrates! - Shows achievement message
Skip prerequisite checks (not recommended):
tito milestone run 03 --skip-checksTrack Progress
View Milestone Progress
tito milestone statusShows: - How many milestones you’ve completed - Your overall progress (%) - Unlocked capabilities - Next milestone ready to run
Visual Timeline
tito milestone timelineSee your journey through ML history in a visual tree format.
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:
tito milestone info 01
tito milestone run 01Milestone 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:
tito milestone info 02
tito milestone run 02Milestone 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:
tito milestone info 03
tito milestone run 03Milestone 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:
tito milestone info 04
tito milestone run 04Milestone 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:
tito milestone info 05
tito milestone run 05Milestone 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:
tito milestone info 06
tito milestone run 06Prerequisites and Validation
How Prerequisites Work
Each milestone requires specific modules to be complete. The run command automatically validates:
Module Completion Check:
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:
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:
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 08Achievement 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: 09Understanding 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
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:
# 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:
tito milestone info 03 # Read the context first
tito milestone run 03 # Then run it4. 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:
# 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:
# Check module status
tito module status
# If modules show incomplete, re-run complete
tito module complete XXMilestone script fails during execution
Problem: Bug in your implementation
Solution: 1. Check error message for which module failed 2. Edit modules/XX_name/XX_name.ipynb (NOT tinytorch/) 3. Re-export: tito module complete XX 4. Run milestone again
Next Steps
Ready to Recreate ML History?
Start with the Foundation Tier and work toward your first milestone
Every milestone uses YOUR code. Every achievement is proof you understand ML systems deeply. Build from scratch, recreate history, master the fundamentals.