Tiny🔥Torch: Build a Machine Learning System from Scratch

TinyTorch is a pedagogical project designed to accompany the Machine Learning Systems textbook. Inspired by OS and compiler courses where students build entire systems from first principles, TinyTorch guides you through building a complete ML training and inference runtime — from autograd to data pipelines, optimizers to profilers — entirely from scratch.

This is not a PyTorch tutorial. In TinyTorch, you'll write the components that frameworks like PyTorch are built on.

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🎯 What You'll Build

By the end of this project, you'll have implemented a fully functional ML system capable of:

• Training neural networks (MLPs, CNNs) on real datasets (MNIST, CIFAR-10)
• Automatic differentiation with a custom autograd engine
• Memory-efficient data loading with custom DataLoader implementations
• Multiple optimization algorithms (SGD, Adam, RMSprop)
• Performance profiling and bottleneck identification
• Model compression through pruning and quantization
• Custom compute kernels for matrix operations
• Production monitoring with MLOps infrastructure
• Reproducible experiments with checkpointing and logging

End Goal: Train a CNN on CIFAR-10 achieving >85% accuracy using only your implementation.

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🧠 Project Goals & Learning Objectives

Core Learning Objectives

• Systems Understanding: Learn how modern ML systems are constructed, not just how to use them
• Full-Stack ML Infrastructure: Build core components from tensor operations to training orchestration
• Performance Engineering: Understand computational and memory bottlenecks in ML workloads
• Software Architecture: Design modular, extensible systems with clean abstractions
• Infrastructure Thinking: Make design decisions that impact performance, reproducibility, and maintainability

Technical Skills Gained

• Low-level ML Implementation: Tensor operations, gradient computation, optimization algorithms
• Memory Management: Efficient data structures, gradient accumulation, batch processing
• Performance Optimization: Profiling, kernel optimization, memory access patterns
• System Design: Modular architecture, clean APIs, extensible frameworks
• Testing & Validation: Numerical stability, gradient checking, performance regression testing

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🏗️ System Architecture

Core Components Overview

┌─────────────────────────────────────────────────────────────┐
│                    TinyTorch System                         │
├─────────────────────────────────────────────────────────────┤
│  CLI Interface (bin/tito.py)                               │
├─────────────────────────────────────────────────────────────┤
│  Training Orchestration (trainer.py)                       │
├─────────────────────────────────────────────────────────────┤
│  Model Definition     │  Data Pipeline    │  Optimization   │
│  (modules.py)         │  (dataloader.py)  │  (optimizer.py) │
├─────────────────────────────────────────────────────────────┤
│  Automatic Differentiation Engine (autograd)               │
├─────────────────────────────────────────────────────────────┤
│  Tensor Operations & Storage (tensor.py)                   │
├─────────────────────────────────────────────────────────────┤
│  Profiling & MLOps (profiler.py, mlops.py)                 │
└─────────────────────────────────────────────────────────────┘

Design Principles

1. Modularity: Each component has a single responsibility and clean interfaces
2. Composability: Components can be mixed and matched for different use cases
3. Performance: Designed for efficiency without sacrificing readability
4. Extensibility: Easy to add new layers, optimizers, and functionality
5. Debuggability: Built-in profiling and logging for understanding system behavior

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📚 Curriculum Integration & Roadmap

TinyTorch aligns with Chapters 1–13 of the Machine Learning Systems textbook. Each project builds progressively toward a complete ML infrastructure.

Course Progression

Order	Project	Chapter	Component	Learning Focus	Deliverable
0	setup	-	Setup & Onboarding	Environment setup, tool familiarity	Working dev environment + basic commands
1	tensor	1-2	Core Tensor System	Tensor operations, memory management	Working Tensor class with basic ops
2	mlp	3	Multi-Layer Perceptron	Forward/backward pass, gradient computation	Train simple MLP on MNIST
3	cnn	4	Convolutional Networks	Convolution, pooling operations	Conv2D and MaxPool implementations
4	autograd	5	Automatic Differentiation	Computational graphs, autodiff	Complete autograd engine
5	data	6	Data Pipeline	Efficient data loading, batching	Custom DataLoader with transformations
6	training	7-8	Training Loop	Optimization algorithms, metrics	SGD, Adam optimizers + training harness
7	config	9	Configuration System	Experiment management, logging	YAML configs + structured logging
8	profiling	10	Performance Tools	Performance measurement, debugging	Memory/compute profiler with visualizations
9	compression	11	Model Compression	Pruning, quantization techniques	Compress model while maintaining accuracy
10	kernels	12	Custom Kernels	Low-level optimization, vectorization	Optimized matrix multiplication kernels
11	benchmarking	13	Performance Evaluation	Performance testing, comparison	Comprehensive benchmarking suite
12	mlops	14	MLOps & Monitoring	Data drift detection, continuous updates	Production monitoring and auto-retraining system

Note

: Project directories use descriptive names (e.g., projects/tensor/, projects/autograd/) for clarity. The order above represents the recommended learning sequence and can be adjusted as the curriculum evolves.

Milestone Targets

• Week 1: Environment setup (setup) and basic command familiarity
• Week 3: Core tensor operations (tensor) working
• Week 5: Train MLP on MNIST (mlp) achieving >95% accuracy
• Week 8: Train CNN on CIFAR-10 (cnn) achieving >80% accuracy
• Week 10: Complete autograd engine (autograd) with gradient checking
• Week 12: Optimized system with profiling tools (profiling)
• Final: Complete system with MLOps monitoring (mlops)

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📦 Course Repository Structure

TinyTorch/
├── bin/                           # Command-line interfaces
│   └── tito.py                    # Main TinyTorch CLI (tito)
├── tinytorch/                     # Core ML system package
│   ├── core/                      # Core ML components
│   │   ├── __init__.py
│   │   ├── tensor.py              # Tensor class with autograd support
│   │   ├── autograd.py            # Automatic differentiation engine
│   │   ├── modules.py             # Neural network layers and models
│   │   ├── functional.py          # Core operations (conv2d, relu, etc.)
│   │   ├── dataloader.py          # Data loading and preprocessing
│   │   ├── optimizer.py           # Optimization algorithms
│   │   ├── trainer.py             # Training loop orchestration
│   │   ├── profiler.py            # Performance measurement tools
│   │   ├── benchmark.py           # Benchmarking and evaluation
│   │   ├── mlops.py               # MLOps and production monitoring
│   │   └── utils.py               # Utility functions
│   ├── configs/                   # Configuration files
│   │   ├── default.yaml           # Default training configuration
│   │   ├── models/                # Model-specific configs
│   │   └── datasets/              # Dataset-specific configs
│   └── datasets/                  # Dataset implementations
│       ├── __init__.py
│       ├── mnist.py
│       ├── cifar10.py
│       └── transforms.py
├── projects/                      # Component-specific projects
│   ├── setup/                    # Environment setup & onboarding
│   ├── tensor/                   # Core tensor implementation
│   ├── mlp/                      # Multi-layer perceptron
│   ├── cnn/                      # Convolutional neural networks
│   ├── config/                   # Configuration system
│   ├── data/                     # Data pipeline & loading
│   ├── autograd/                 # Automatic differentiation
│   ├── training/                 # Training loop & optimization
│   ├── profiling/                # Performance profiling tools
│   ├── compression/              # Model compression techniques
│   ├── kernels/                  # Custom compute kernels
│   ├── benchmarking/             # Performance benchmarking
│   └── mlops/                    # MLOps & production monitoring
├── docs/                         # Course documentation
│   ├── tutorials/                # Step-by-step tutorials
│   ├── api/                      # API documentation
│   └── lectures/                 # Lecture materials
├── notebooks/                    # Jupyter tutorials and demos
├── examples/                     # Working examples and demos
│   ├── train_mnist_mlp.py
│   ├── train_cifar_cnn.py
│   └── benchmark_ops.py
├── tests/                        # Comprehensive test suite
│   ├── test_tensor.py
│   ├── test_autograd.py
│   ├── test_modules.py
│   └── test_training.py
├── grading/                      # Course grading materials
│   ├── rubrics/                  # Assignment rubrics
│   ├── autograders/              # Automated grading scripts
│   └── solutions/               # Reference solutions
├── resources/                    # Course resources
│   ├── datasets/                 # Course datasets
│   ├── pretrained/              # Pre-trained models
│   └── references/              # Reference materials
├── logs/                         # Training logs and artifacts
│   └── runs/
├── checkpoints/                  # Model checkpoints
├── requirements.txt              # Python dependencies
└── README.md                     # This file

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🚀 Getting Started

Prerequisites

• Python 3.8+ (type hints and modern features required)
• NumPy (numerical computations)
• Optional: Numba (JIT compilation for performance)
• Development: pytest, black, mypy (for testing and code quality)

Quick Start

# Clone and setup
git clone <repository-url>
cd TinyTorch
pip install -r requirements.txt

# Run your first training
python bin/tito.py train --config tinytorch/configs/default.yaml

# Check system status
python bin/tito.py info --show-architecture

For Instructors

# Set up the complete course environment
pip install -r requirements.txt

# Generate course materials
python bin/tito.py generate-projects
python bin/tito.py setup-autograders

# View course progress
python bin/tito.py course-status

For Students

# Start with Project 0: Setup  
cd projects/setup/
python bin/tito.py project init

# Then move through the sequence
cd projects/tensor/     # Project 1: Core tensors
cd projects/mlp/        # Project 2: Multi-layer perceptron  
cd projects/autograd/   # Project 4: Automatic differentiation

# Run tests and submit
python bin/tito.py test --project tensor
python bin/tito.py submit --project tensor

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🎯 Implementation Guidelines

Code Quality Standards

• Type Hints: All public APIs must have complete type annotations
• Documentation: Docstrings for all classes and public methods
• Testing: >90% code coverage with unit and integration tests
• Performance: Profile-guided optimization with benchmarking
• Style: Black code formatting, consistent naming conventions

API Design Principles

• Familiar Interface: Similar to PyTorch where it makes sense (for learning transfer)
• Explicit Over Implicit: Clear parameter names and behavior
• Composable: Small, focused components that work together
• Debuggable: Rich error messages and debugging hooks

Performance Targets

• MNIST MLP: <5 seconds per epoch on modern laptop
• CIFAR-10 CNN: <30 seconds per epoch on modern laptop
• Memory Usage: <2GB RAM for standard training runs
• Numerical Stability: Gradient checking passes for all operations

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🧪 Testing & Validation Strategy

Test Categories

1. Unit Tests: Individual component functionality
2. Integration Tests: Component interaction and data flow
3. Numerical Tests: Gradient checking and mathematical correctness
4. Performance Tests: Regression testing for speed and memory
5. End-to-End Tests: Complete training runs with known results

Validation Methodology

• Gradient Checking: Numerical verification of all autodiff operations
• Reference Comparisons: Output validation against NumPy/PyTorch (where applicable)
• Convergence Testing: Training curves must match expected behavior
• Ablation Studies: Systematic testing of individual components

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💡 Educational Philosophy

"You don't really understand a system until you've built it."

Learning Through Building

TinyTorch emphasizes active construction over passive consumption. Students don't just learn about autograd—they implement it. They don't just use optimizers—they write them from scratch.

Systems Thinking

By building a complete system, students understand:

• Abstraction Boundaries: What belongs where in the system hierarchy
• Performance Trade-offs: How design decisions impact speed and memory
• Debugging Strategies: How to trace problems through complex systems
• Integration Challenges: How components interact and depend on each other

Real-World Relevance

Every component in TinyTorch has a direct analog in production ML systems. The skills learned here transfer directly to understanding and contributing to frameworks like PyTorch, TensorFlow, and JAX.

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🔧 Advanced Features & Extensions

Chapter 13: MLOps Deep Dive

Core MLOps Components (Chapter 13 will implement):

• Data Drift Detection: Statistical tests for distribution shifts in input features
• Model Performance Monitoring: Track accuracy, latency, and throughput in production
• Automatic Retraining Triggers: When to retrain based on performance degradation
• A/B Testing Framework: Compare model versions safely in production
• Model Registry: Version control and metadata tracking for deployed models
• Alert Systems: Notifications for model failures or performance drops
• Rollback Mechanisms: Safe deployment and quick rollback strategies

Production Integration:

• REST API Serving: Deploy models as web services
• Batch Inference Pipelines: Large-scale offline predictions
• Feature Store Integration: Consistent feature engineering across training/serving
• Monitoring Dashboards: Real-time system health visualization

Optional Advanced Components

• Mixed Precision Training: FP16/FP32 mixed precision implementation
• Distributed Training: Multi-GPU and multi-node training support
• Dynamic Graphs: Support for variable computation graphs
• Custom Operators: Framework for implementing new operations
• JIT Compilation: Integration with Numba or custom compilation

Research Extensions

• Novel Optimizers: Implement cutting-edge optimization algorithms
• Architecture Search: Automated neural architecture search
• Compression Techniques: Advanced pruning and quantization methods
• Hardware Acceleration: GPU kernels and specialized hardware support

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📊 Success Metrics

Technical Milestones

• Train MLP on MNIST achieving >95% accuracy
• Implement working autograd engine with gradient checking
• Train CNN on CIFAR-10 achieving >85% accuracy
• Profile and optimize for 2x performance improvement
• Complete all core project implementations

Learning Outcomes Assessment

• Code Reviews: Peer and instructor evaluation of implementations
• Design Document: Architecture decisions and trade-off analysis
• Performance Analysis: Profiling report and optimization strategy
• Presentation: Explain system design and key insights learned

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🤝 Course Management

For Instructors

Project Management:

• Each chapter has structured projects in projects/XX-name/
• Rubrics and grading criteria in grading/rubrics/
• Automated testing and grading in grading/autograders/
• Reference solutions in grading/solutions/

Progress Tracking:

• Student progress dashboards
• Automated testing and feedback
• Performance benchmarking
• Code quality metrics

For Students

Development Process:

1. Start Each Project: Read project description in projects/XX-name/README.md
2. Implement Features: Follow step-by-step guided implementation
3. Run Tests: Use automated tests to validate implementation
4. Submit Work: Automated submission and grading system
5. Get Feedback: Detailed feedback on implementation and performance

Getting Help

• Documentation: Comprehensive docs in docs/
• Tutorials: Step-by-step tutorials in notebooks/
• Office Hours: Regular sessions for questions and debugging
• Peer Discussion: Collaborative learning encouraged
• Issue Tracking: GitHub issues for bugs and feature requests

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📬 License and Attribution

TinyTorch is part of the Machine Learning Systems course and textbook by Vijay Janapa Reddi et al. Inspired by systems-style pedagogical projects like xv6 (OS), PintOS (OS), and cs231n assignments (ML).

License: MIT
Citation: Please cite the Machine Learning Systems textbook when using this educational material.

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🔗 Additional Resources

• Textbook: Machine Learning Systems (Chapters 1-13)
• Course Website: [Course URL]
• Video Lectures: Complementary video content for each chapter
• External Reading: Curated list of papers and blog posts
• Community Forum: Discussion and Q&A platform
• Office Hours: [Schedule and locations]