Track Your Progress

Track Your Progress#

Monitor Your Learning Journey

Track your capability development through 20 modules and 6 historical milestones

Purpose: Monitor your progress as you build a complete ML framework from scratch. Track module completion and milestone achievements.

The Core Workflow#

TinyTorch follows a simple three-step cycle: Edit modules → Export to package → Validate with milestones

📖 See Student Workflow for the complete development cycle, best practices, and troubleshooting.

Understanding Modules vs Checkpoints vs Milestones#

Modules (18 total): What you’re building - the actual code implementations

  • Located in modules/source/

  • You implement each component from scratch

  • Export with tito module complete N

Milestones (6 total): How you validate - historical proof scripts

  • Located in milestones/

  • Run scripts that use YOUR implementations

  • Recreate ML history (1957 Perceptron → 2018 MLPerf)

Checkpoints (21 total): Optional progress tracking

  • Use tito checkpoint status to view

  • Tracks capability mastery

  • Not required for the core workflow

📖 See Journey Through ML History for milestone details.

Your Learning Path Overview#

TinyTorch organizes 20 modules through three pedagogically-motivated tiers: Foundation (build mathematical infrastructure), Architecture (implement modern AI), and Optimization (deploy production systems).

📖 See Three-Tier Learning Structure for complete tier breakdown, detailed module descriptions, time estimates, and learning outcomes.

Module Progression Checklist#

Track your journey through the 20 modules:

  • Module 01: Tensor - N-dimensional arrays

  • Module 02: Activations - ReLU, Softmax

  • Module 03: Layers - Linear layers

  • Module 04: Losses - CrossEntropyLoss, MSELoss

  • Module 05: Autograd - Automatic differentiation

  • Module 06: Optimizers - SGD, Adam

  • Module 07: Training - Complete training loops

  • Module 08: DataLoader - Batching and pipelines

  • Module 09: Spatial - Conv2d, MaxPool2d

  • Module 10: Tokenization - Character-level tokenizers

  • Module 11: Embeddings - Token and positional embeddings

  • Module 12: Attention - Multi-head self-attention

  • Module 13: Transformers - LayerNorm, GPT

  • Module 14: Profiling - Performance measurement

  • Module 15: Quantization - INT8/FP16

  • Module 16: Compression - Pruning techniques

  • Module 17: Memoization - KV-cache

  • Module 18: Acceleration - Batching strategies

  • Module 19: Benchmarking - MLPerf-style comparison

  • Module 20: Competition - Capstone challenge

📖 See Quick Start Guide for immediate hands-on experience with your first module.

Optional: Checkpoint System#

Track capability mastery with the optional checkpoint system:

tito checkpoint status  # View your progress

This provides 21 capability checkpoints corresponding to modules and validates your understanding. Helpful for self-assessment but not required for the core workflow.

📖 See Essential Commands for checkpoint commands.

Capability Development Approach#

Foundation Building (Checkpoints 0-3)#

Capability Focus: Core computational infrastructure

  • Environment configuration and dependency management

  • Mathematical foundations with tensor operations

  • Neural intelligence through nonlinear activation functions

  • Network component abstractions and forward propagation

Learning Systems (Checkpoints 4-7)#

Capability Focus: Training and optimization

  • Loss measurement and error quantification

  • Automatic differentiation for gradient computation

  • Parameter optimization with advanced algorithms

  • Complete training loop implementation

Advanced Architectures (Checkpoints 8-13)#

Capability Focus: Specialized neural networks

  • Spatial processing for computer vision systems

  • Efficient data loading and preprocessing pipelines

  • Natural language processing and tokenization

  • Representation learning with embeddings

  • Attention mechanisms for sequence understanding

  • Complete transformer architecture mastery

Production Systems (Checkpoints 14-15)#

Capability Focus: Performance and deployment

  • Profiling, optimization, and bottleneck analysis

  • End-to-end ML systems engineering

  • Production-ready deployment and monitoring

Start Building Capabilities#

Begin developing ML systems competencies immediately:

Begin Capability Development

Start with foundational capabilities and progress systematically

15-Minute Start → Begin Setup →

How to Track Your Progress#

The essential workflow:

# 1. Work on a module
cd modules/source/03_layers
jupyter lab 03_layers_dev.py

# 2. Export when ready
tito module complete 03

# 3. Validate with milestones
cd ../../milestones/01_1957_perceptron
python 01_rosenblatt_forward.py  # Uses YOUR implementation!

Optional: Use tito checkpoint status to see capability tracking

📖 See Student Workflow for the complete development cycle.

Approach: You’re building ML systems engineering capabilities through hands-on implementation. Each module adds new functionality to your framework, and milestones prove it works.

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