TinyTorch/site/TEAM_ONBOARDING.md

Team Onboarding Guide: TinyTorch for Industry

Complete guide for using TinyTorch in industry settings: new hire bootcamps, internal training programs, and debugging workshops.

🎯 Overview

TinyTorch's Model 3: Team Onboarding addresses industry use cases where ML teams want members to understand PyTorch internals. This guide covers deployment scenarios, training structures, and best practices for industry adoption.

🚀 Use Cases

1. New Hire Bootcamps (2-3 Week Intensive)

Goal: Rapidly onboard new ML engineers to understand framework internals

Structure:

• Week 1: Foundation Tier (Modules 01-07)
  • Tensors, autograd, optimizers, training loops
  • Focus: Understanding loss.backward() mechanics
• Week 2: Architecture Tier (Modules 08-13)
  • CNNs, transformers, attention mechanisms
  • Focus: Production architecture internals
• Week 3: Optimization Tier (Modules 14-19) OR Capstone
  • Profiling, quantization, compression
  • Focus: Production optimization techniques

Schedule:

• Full-time: 40 hours/week
• Hands-on coding: 70% of time
• Systems discussions: 30% of time
• Daily standups and code reviews

Deliverables:

• Completed modules with passing tests
• Capstone project (optional)
• Technical presentation on framework internals

2. Internal Training Programs (Distributed Over Quarters)

Goal: Deep understanding of ML systems for existing team members

Structure:

• Quarter 1: Foundation (Modules 01-07)
  • Weekly sessions: 2-3 hours
  • Self-paced module completion
  • Monthly group discussions
• Quarter 2: Architecture (Modules 08-13)
  • Weekly sessions: 2-3 hours
  • Architecture deep-dives
  • Production case studies
• Quarter 3: Optimization (Modules 14-19)
  • Weekly sessions: 2-3 hours
  • Performance optimization focus
  • Real production optimization projects

Benefits:

• Fits into existing work schedules
• Allows deep learning without intensive time commitment
• Builds team knowledge gradually
• Enables peer learning

3. Debugging Workshops (Focused Modules)

Goal: Targeted understanding of specific framework components

Common Focus Areas:

Autograd Debugging Workshop (Module 05)

• Understanding gradient flow
• Debugging gradient issues
• Computational graph visualization
• Duration: 1-2 days

Attention Mechanism Workshop (Module 12)

• Understanding attention internals
• Debugging attention scaling issues
• Memory optimization for attention
• Duration: 1-2 days

Optimization Workshop (Modules 14-19)

• Profiling production models
• Quantization and compression
• Performance optimization strategies
• Duration: 2-3 days

🏗️ Deployment Scenarios

Scenario 1: Cloud-Based Training (Recommended)

Setup: Google Colab or JupyterHub

• Zero local installation
• Consistent environment
• Easy sharing and collaboration
• Best for: Large teams, remote workers

Steps:

1. Clone repository to Colab
2. Install dependencies: pip install -e .
3. Work through modules
4. Share notebooks via Colab links

Scenario 2: Local Development Environment

Setup: Local Python environment

• Full control over environment
• Better for debugging
• Offline capability
• Best for: Smaller teams, on-site training

Steps:

1. Clone repository locally
2. Set up virtual environment
3. Install: pip install -e .
4. Use JupyterLab for development

Scenario 3: Hybrid Approach

Setup: Colab for learning, local for projects

• Learn in cloud environment
• Apply locally for projects
• Best for: Flexible teams

📋 Training Program Templates

Template 1: 2-Week Intensive Bootcamp

Week 1: Foundation

• Day 1-2: Modules 01-02 (Tensor, Activations)
• Day 3-4: Modules 03-04 (Layers, Losses)
• Day 5: Module 05 (Autograd) - Full day focus
• Weekend: Review and practice

Week 2: Architecture + Optimization

• Day 1-2: Modules 08-09 (DataLoader, CNNs)
• Day 3: Module 12 (Attention)
• Day 4-5: Modules 14-15 (Profiling, Quantization)
• Final: Capstone project presentation

Template 2: 3-Month Distributed Program

Month 1: Foundation

• Week 1: Modules 01-02
• Week 2: Modules 03-04
• Week 3: Module 05 (Autograd)
• Week 4: Modules 06-07 (Optimizers, Training)

Month 2: Architecture

• Week 1: Modules 08-09
• Week 2: Modules 10-11
• Week 3: Modules 12-13
• Week 4: Integration project

Month 3: Optimization

• Week 1: Modules 14-15
• Week 2: Modules 16-17
• Week 3: Modules 18-19
• Week 4: Capstone optimization project

🎓 Learning Outcomes

After completing TinyTorch onboarding, team members will:

1. Understand Framework Internals

   • How autograd works
   • Memory allocation patterns
   • Optimization trade-offs

2. Debug Production Issues

   • Gradient flow problems
   • Memory bottlenecks
   • Performance issues

3. Make Informed Decisions

   • Optimizer selection
   • Architecture choices
   • Deployment strategies

4. Read Production Code

   • Understand PyTorch source
   • Navigate framework codebases
   • Contribute to ML infrastructure

🔧 Integration with Existing Workflows

Code Review Integration

• Review production code with TinyTorch knowledge
• Identify framework internals in production code
• Suggest optimizations based on systems understanding

Debugging Integration

• Apply TinyTorch debugging strategies to production issues
• Use systems thinking for troubleshooting
• Profile production models using TinyTorch techniques

Architecture Design

• Design new models with systems awareness
• Consider memory and performance from the start
• Make informed trade-offs

📊 Success Metrics

Individual Metrics

• Module completion rate
• Test passing rate
• Capstone project quality
• Self-reported confidence increase

Team Metrics

• Reduced debugging time
• Fewer production incidents
• Improved code review quality
• Better architecture decisions

🛠️ Setup for Teams

Quick Start

# 1. Clone repository
git clone https://github.com/mlsysbook/TinyTorch.git
cd TinyTorch

# 2. Set up environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt
pip install -e .

# 4. Verify setup
tito system doctor

# 5. Start with Module 01
tito view 01_tensor

Team-Specific Customization

• Custom datasets: Replace with company-specific data
• Domain modules: Add modules for specific use cases
• Integration: Connect to company ML infrastructure
• Assessment: Customize grading for team needs

📚 Resources

• Student Quickstart: docs/STUDENT_QUICKSTART.md
• Instructor Guide: INSTRUCTOR.md (for training leads)
• TA Guide: TA_GUIDE.md (for support staff)
• Module Documentation: modules/*/ABOUT.md

💼 Industry Case Studies

Case Study 1: ML Infrastructure Team

Challenge: Team members could use PyTorch but couldn't debug framework issues Solution: 2-week intensive bootcamp focusing on autograd and optimization Result: 50% reduction in debugging time, better architecture decisions

Case Study 2: Research Team

Challenge: Researchers needed to understand transformer internals Solution: Focused workshop on Modules 12-13 (Attention, Transformers) Result: Improved model designs, better understanding of scaling

Case Study 3: Production ML Team

Challenge: Team needed optimization skills for deployment Solution: 3-month program focusing on Optimization Tier (Modules 14-19) Result: 4x model compression, 10x speedup on production models

🎯 Next Steps

1. Choose deployment model: Bootcamp, distributed, or workshop
2. Set up environment: Cloud (Colab) or local
3. Select modules: Full curriculum or focused selection
4. Schedule training: Intensive or distributed
5. Track progress: Use checkpoint system or custom metrics

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For Questions: See INSTRUCTOR.md or contact TinyTorch maintainers